!!ijest 2(9) special issue npd whole

MultiCraft

ISSN 2141-2839 (Online); ISSN 2141-2820 (Print)

Available online at www.ijest-ng.com

International Journal of Engineering, Science and Technology

Special Issue: “New product development and innovation” Guest Editors: Eleonora Bottani Department of Industrial Engineering, University of Parma, viale G.P.Usberti 181/A, 43124 Parma (Italy) Barbara Bigliardi Department of Industrial Engineering, University of Parma, viale G.P.Usberti 181/A, 43124 Parma (Italy) Roberto Montanari Department of Industrial Engineering, University of Parma, viale G.P.Usberti 181/A, 43124 Parma (Italy)

Vol. 2, No. 9, 2010

International Journal of Engineering, Science and Technology (IJEST)

Aims and scope

IJEST is an international refereed journal published by MultiCraft. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of engineering, science and technology. Original theoretical work and application-based studies, which contributes to a better understanding of engineering, science and technological challenges, are encouraged.

Journal policy

International Journal of Engineering, Science and Technology (IJEST) publishes articles that emphasize research, development and application within the fields of engineering, science and technology. All manuscripts are pre-reviewed by the editor, and if appropriate, sent for blind peer review. Contributions must be original, not previously or simultaneously published elsewhere, and are critically reviewed before they are published. Papers, which must be written in English, should have sound grammar and proper terminologies.

Call for papers

We invite you to submit high quality papers for review and possible publication in all areas of engineering, science and technology. All authors must agree on the content of the manuscript and its submission for publication in this journal before it is submitted to us. Manuscripts should be submitted by e-mail to the Editor at: [email protected] Call for Reviewers Scholars interested in serving as volunteer reviewers should indicate interest by sending their full curriculum vitae to us. Reviewers determine submissions that are of quality. Since they are expected to be experts in their areas, they should comment on the significance of the reviewed manuscript and whether the research contributes to knowledge and advances both theory and practice in the area.

International Journal of Engineering, Science and Technology (IJEST) Editor: S.A. Oke, PhD, Department of Mechanical Engineering, University of Lagos, Nigeria E-mail: [email protected] Associate Editor (Electrical Engineering): S.N. Singh, PhD, Department of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur-208016, India E-mail: snsingh[AT]iitk.ac.in Associate Editor (Environmental Engineering) : Jian Lu, PhD, School of Civil & Environmental Engineering, Georgia Institute of Technology, 200 Bobby Dodd Way, Atlanta, Georgia 30332, USA. Email: lujian.leonard[AT]gmail.com Associate Editor (Computational Mechanics and Tribology): Prasanta Sahoo, PhD, Department of Mechanical Engineering, Jadavpur University, Kolkata, India. E-mail: psjume[AT]gmail.com Associate Editor (Environmental Science): R.M. Jingura, PhD, Chinhoyi University of Technology, Chinhoyi, Zimbabwe, Email: rjingura[AT]gmail.com Associate Editor (Operations Management): S. S. Mahapatra, PhD, Department of Mechanical Engineering, National Institute of Technology Rourkela, Rourkela- 769008, India E-mail: mahapatrass2003[AT]gmail.com Associate Editor (Logistics and Supply Chain Management): Eleonora Bottani, Ph.D., Department of Industrial Engineering, University of Parma, viale G.P.Usberti 181/A, 43124 Parma - Italy Email: eleonora.bottani[AT]unipr.it Associate Editor (Metallurgy and Materials): Shashi Anand, Ph.D., Retd. Sc.G, IMMT, Bhunabeswar, Orissa; Presently: Adjunct Professor (off campus), Faculty of Minerals and Energy, Murdoch University, Western Australia, Perth, Australia Email: anand.shashi[AT]gmail.com

EDITORIAL BOARD MEMBERS

Kyoji Kamemoto (Japan) Ian Blenkharn (UK) K. Somasundaram (India) M. Abdus Sobhan (Bangladesh) V. Sivasubramanian (India) Shashi Anand (India) Sri Niwas Singh (India) Chii-Ruey Lin (Taiwan) Piotr Czech (Poland) Shashank Thakre (India) S. Karthikeyan (Oman) Petr Konas (Czech Republic) Jun Wu (USA) Fatih Camci (Turkey) Syed Asif Raza (Qatar) Jian Lu (USA) Asim Kumar Pal (India) Atif Iqbal (India) Raphael Jingura (Zimbabwe) Prasanta Sahoo (India) Kampan Mukherjee (India) MKS Sastry (West Indies) Vidosav D. Majstorovich (Serbia) Mark Burgin (USA) P.K. Kapur (India) P. Thangavelu (India) Yechun Wang (USA) K.I. Ramachandran (India) Jian Ma (USA) P.K. Tripathy (India) J. Paulo Davim (Portugal) Rajneesh Talwar (India) S. Thangaprakash (India) A. Moreno-Muñoz (Spain) Prabin K Panigrahi (India) Vinay Gupta (India) Mohammed Al-Nawafleh (Jordan)

Tien-Fu Liang (Taiwan) Ranjit Kumar Biswas (Bangladesh)

Siba Sankar Mahapatra (India) Sangeeta Sahney (India) Kuang-Yuan Kung (Taiwan) Eleonora Bottani (Italy) Evangelos J. Sapountzakis

(Greece) A.M. Rawani (India)

Saurabh Mukherjee (India) P. Dhavachelvan (India) A. Bandyopadhyay (India) Velusamy Sivasubramanian (India)

S. Vinodh (India) Víctor Hugo Hinojosa Mateus (Chile)

K.S. Verma (India) Sarmila Sahoo (India) Ta Yeong Wu (Malaysia) Kioumars Paryani (USA) Vipan Kakkar (India) Amares Chattopadhyay (India) Elizabeth Anne Cudney (USA) Debojyoti Mitra (India) Cassandra C. Elrod (USA) Saurav Datta (India) Mukhtar Ahmad (India) Tzung-Pei Hong (Taiwan) Siddhartha Kumar Khaitan (USA)

Dragisa Nikodijevic (Serbia)

Fiorenzo Franceschini (Italy)

Totok R Biyanto (Indonesia) H.S.Dhami (India) J.B.V. Subrahmanyam (India) Shashidhar Kudari (India) Isa Yildirim (Turkey) Ottavia Corbi (Italy) Natarajan Meghanathan (USA) Ali Riza Motorcu (Turkey) Amauri Garcia (Brazil)

G.B.Gharehpetian (Iran) Behrooz Vahidi (Iran) M. Nouby (India) Bhu Dev Sharm (India) Alan Rennie (UK) Haitao Huang (Hong Kong) Alistair Thompson McIlhagger (UK)

Shaw Voon Wong (Malaysia)

Milorad Bojic (Serbia)

Amir Nassirharand (Malaysia) N. W. Ingole (India) Angelo Basile (Italy) Abdul Ravoof Shaik (Australia) Mahmoud Salimi (Iran) Humberto Varum (Portugal) M.A.Pathan (Botswana) M. Mansur (Sultanate of Oman) Omar Al-Araidah (Jordan) M. S. Sangha (UK) Ashwani Dhingra (India) V. Sundarapandian (India) Riccardo Manzini (Italy) Siddhartha Bhattacharyya (India) Abdessattar Chaari (Tunisia) Dingli Yu (UK) Jixiong Han (USA) Y. Abdelaziz (Algeria)

REVIEWERS The following reviewers have greatly helped us in reviewing our manuscripts and have brought such submissions to high quality levels. We are indebted to them.

Marcus Bengtsson (Sweden) Kit Fai Pun (West Indies) Peter Koh (Australia) Erhan Kutanoglu (USA) Jayant Kumar Singh (India) Maneesh Singh (Norway RRK Sharma (India) Jamil Abdo (Oman) Agnes S. Budu (Ghana) Yuan-Ching Lin (Taiwan) Withaya Puangsombut (Thailand) Abd Rahim Abu Bakar

(Malaysia) Fakher Chaari (Tunisia) Uday Kumar (Sweden) Elsa Rueda (Argentina) Ghosh Surojit (India) M.R. Sharma (India) Ming-Kuang Wang (Taiwan) Umut Topal (Turkey) Hyung Hee Cho (Korea) Ruey-Shin Juang (Taiwan) Maloy Singha (India) Souwalak Phongpaichit

(Thailand) Marisa Viera (Argentina)

Parviz Malekzadeh (Iran) Diwakar Tiwari (India) Shiguo Jia (China) Francisco Jesus Fernandez Morales (Spain)

Mustafa Soylak (Turkey)

S. Devasenapati Babu (India)

Masoud Rashidinejad (India) Jerzy Merkisz (Poland) Rajeeb Dey (India) Vera Meshko (Republic of Macedonia)

Md Fahim Ansari (India)

Subrata Kumar Ghosh (India)

Jun Luo (China) Jiun-Hung Lin (Taiwan) Timothy Payne (Australia) Tzong-Ru Lee (Taiwan) Subir Kumar Sarkar (India) Kee-hung Lai (Hong Kong) Jochen Smuda (Switzerland) Roland Hischier (China) Ahmed Abu-Siada (Australia) Hamzah Abdul Rahman (Jordan) Chih-Huang Weng (Taiwan) Yenming Chen (Taiwan) Dinesh Verma (USA) Devanandham Henry (USA) M. Habibnejad Korayem (Iran) Radu Radescu (Romania) Hsin-Hung Wu (Taiwan) Amy Trappey (Taiwan) A.B. Stevels (Netherlands) Liang-Hsuan Chen (Taiwan) Richard Hischier (Switzerland) Shyi-Chyi Cheng (Taiwan) Andrea Gerson (Australia) Ingrid Bouwer Utne (Norway) Maruf Hossain (Bangladesh) Enso Ikonen (Finland) Kwai-Sang Chin (Hong Kong) Jiunn – I Shieh (Taiwan) Khim Ling Sim (USA) Rong-Jyue Fang (Taiwan) Hung-Yan Gu (Taiwan) Chandan Guria (India) Rafael Prikladnicki (Brazil) Pengwei (David) Du (US) Juraj Kralik (Slovak) Indika Perera (Sri Lanka) Min-Shiang Hwang (Taiwan) R K Srivastava (India) J. Raghava Rao (India) Ekata Mehul (India)

Suresh Premil Kumar (India)

Fernando Casanova García (Colombia)

J. Ashayeri (The Netherlands) Jim Austin (UK) Rafael Prikladnicki (Brazil) V. Balakrishnan (India) P. Dhasarathan (India) R. Venckatesh (India) Avi Rasooly (USA) Barbara Bigliardi (Italy) Huiling Wu (China) Ahmet N. Eraslan (Turkey) G. Possart (Germany) Ryoichi Chiba (Japan) Kesheng Wang (Norway) Chuan-Ming Li (Taiwan) Mehdi Hassani (Iran) Arup Borah (India) Feng qin Yang (China) P.K.Dutta (India) Kalyani Radha (India) Sunita Gakkhar (India) Kimon Antonopoulos (Greece) Krishna Mohanarao Gurram (India)

Hao Chen (USA) Josefa Mula (Spain)

Syed R Mousavi (Iran) Amit K. Garg (India) Amiya Ku.Rath (India) Morteza Sadeghi (Iran) K. Pandurangan (India) Fabio Leao (Brazil) Leonardo Acho (Spain) Pankaj Chandna (India) G.S. Seth (India)

W Q Zhu (China) Maher Moakher (Tunisia) Sanjeet Dwivedi (Denmark) Mohamed Gaith (Jordan) Tamer Samir Mahmoud (Egypt) R.V. Murali (Oman) Alfonso Fernandez-Canteli (Spain)

Tadeusz Antczak (Poland) S. Vishnupriyan (Oman)

Fetih Yildir (Turkey) Arijit Bhattacharya (Ireland) Alfred Huber (Austria) D. Chakravorty (India) Lai Khin Wee (Germany) Mathivanan Dakshinamoorthi

(India) P Y Lin (Taiwan) P.K. Kuipa (Zimbabwe) Tarek Abushreehah (Jordan) Andrew N. Norris (USA) Mohamad Mirbagheri (Iran) Sunil Kumar Kashyap (India) Antonio Gonzalez Herrera (Spain)

KimHo Yeap (Malaysia) Z. M. Xiao (Singapore)

I.A.Chidambaram (India) Anand Kumar (India) Md. Mamun Habib (Bangladesh) Alvaro Torres (Mexico) Yiju Wang (China) Engui Fan (China) Abdoullah Namdar (India) Miithun M. Bhaskar (India) Kalyani Radha (India) Petia Dineva (Bulgaria) Pierre Comon (France) Rasim Amer Ali (Libya) Jatinder Saini (India) Maode Ma (Singapore) Fadhlur Rahman Bin Mohd

Romlay (Malaysia) Zhenjun Yang (UK) Shaveta Garg (India) Paramjeet Singh (India) Per Hansson (Sweden) Loris Molent (Australia) Xiaobo Yu (Australia) Wolfgang Seemann (Germany) Tirivanhu Chinyoka (South

Africa) J. Raghava Rao (India)

MultiCraft


Vol. 2, No. 9, 2010, pp. i-iii

INTERNATIONAL JOURNAL OF

ENGINEERING, SCIENCE AND TECHNOLOGY

www.ijest-ng.com

© 2010 MultiCraft Limited. All rights reserved

Editorial Decades ago, companies believed that innovation and new product development (NPD) were activities that only big companies could carry out mainly due to their costs. Given the rapid changes and the more and more competitive environment where firms have to compete, most academics and practitioners now agree that these issues are much more important than previously envisioned, thus suggesting companies must develop a steady stream of new products and services. Thus, the relevance of innovation and NPD for the competitive performance of firms and for the long-term economic growth is a known topic, recognized as a fundamental requisite of a company in order to grow in today’s competitive context, and as one of the key drivers of the firms’ long-term success. In particular, during the last decades, a shift has been observed from the technology push innovation model to the market pull one, thus forcing companies to focus more on quality product rather than on internal efficiency, and to quickly identify changing customers’ needs, to develop more complex products to satisfy those needs, and to provide higher level of customer supports and service. As a consequence, greater focus has been placed on NPD, that is often considered to be the lifeblood of a company. On the basis of the above mentioned premises, the main objective of our special issues is to contribute to this thought process in the area of innovation management by collecting a set of high-quality papers examining topics such as product innovation, product engineering, quality management systems, reliability and quality control, integrated product engineering and so on. We received a total of 19 submissions, and, after review, 9 papers were accepted for publication in the special issue. Such papers address a wide range of topics related to field of NPD and innovation, and can be grouped into four sets, depending on the topics examined. A first group of works includes the first, third and fifth papers, and proposes studies whose general aim is to improve the NPD process. In the first paper, Rashid et al. examine the issue of effectively capturing the voice of the customer in defining the product attributes, which is one of the first steps of a NPD process. They propose a computer system, exploiting the Monte-Carlo simulation technique, which allows determining the minimal number of respondents to make a reliable conclusion for a definite product attribute. In the third paper, De Felice and Petrillo propose a new methodological approach to define customer specifications in NPD. The approach grounds on the combined application of Quality Function Deployment (QFD) and Analytic Hierarchy Process (AHP) models, which are exploited with the purpose of delineating and ranking the relative importance weight of expressed judgments for customer needs and functional characteristics of a new product. In the fifth paper, Voigt and Ernst investigate the potential of using of Web 2.0 applications for generating and sharing knowledge in research and development departments of companies, especially on the emergence of innovations. The second group of works includes the second and sixth papers, and provides real case examples of NPD processes. Specifically, the second paper, by Bigliardi et al., examines a successful case of new product development, in the field of the food packaging industry. From the analysis carried out, the authors derive the key elements for successful NPD in that context. In the sixth paper, La Scalia et al. focus on a specific NPD process, i.e. almond paste production, with the purpose of developing a new production process which allows delivering high nutritional contents of the raw almonds into the finished product. The third group of works includes papers 4 and 8, i.e. empirical studies related to the NPD process. In the fourth paper, Lazzarotti and Pizzurno conduct an empirical study to investigate the role of companies offering technical and scientific services (TSS) in the new product development process. They aim at identifying TSS companies that support the entire NPD process, or that provide services to a smaller part of the innovation process, as well as at examining the profile of such companies. The eighth paper, by Kulatunga et al., examines R&D processes in the construction industry. Through a combination of literature review and in-field investigation, the authors derive a set of critical success factors that should be considered and implemented for successful R&D process in that field. Finally, the fourth group of works, including the seventh and ninth papers, investigates industrial issues related to the characteristics of new products manufactured and to the new product development process. Through a case study related to the electronic industry, in the seventh paper Pero et al. examine the issue of effectively and efficiently managing a supply chain when new products are introduced. A case study is also proposed by Davoli et al. in the ninth paper. The authors examine the issue of scheduling manufacturing activities in the ceramic tiles industry; a simulation model is proposed as a useful tool to support management decisions related to production scheduling and investment planning. Thanks to the variety of topics addressed, we believe that this special issue provides the scientific community with valuable information and knowledge in the field of new product development and innovation. The value-added by a special issue is only as good as the contributions of the manuscripts it receives, and the quality of the feedback provided by its reviewers. We are very

ii

grateful to all the authors, who supported this special issue through their contributions. We are also indebted to the reviewers, who helped us in managing the papers received in a timely manner and provided useful and professional reports about the papers. Finally, we would like to thank the Editor in Chief of International Journal of Engineering, Science and Technology, which gave us the possibility of organizing the special issue and helped us in its successful completion.

Eleonora Bottani, Lecturer in Mechanical Industrial Plants Department of Industrial Engineering, University of Parma viale G.P.Usberti 181/A, 43124 Parma (Italy) Email: [email protected]

Barbara Bigliardi, Lecturer in Industrial Engineering and Management Department of Industrial Engineering, University of Parma viale G.P.Usberti 181/A, 43124 Parma (Italy) Email: [email protected]

Roberto Montanari, Full Professor of Mechanical Industrial Plants Department of Industrial Engineering, University of Parma viale G.P.Usberti 181/A, 43124 Parma (Italy) Email: [email protected]

iii

Serial No. Title of paper Authors Page No.

1 A proposed computer system on Kano model for new product development and innovation aspect: A case study is conducted by an attractive attribute of automobile

Md Mamunur Rashid, Jun’ichi Tamaki, A.M.M. Sharif Ullah and Akihiko Kubo 1-12

2 Successful new product development in the food packaging industry: evidence from a case study Barbara Bigliardi, Eleonora Bottani, Roberto Montanari & Giuseppe Vignali 13-24

3 A multiple choice decision analysis: an integrated QFD – AHP model for the assessment of customer needs

F. De Felice, A. Petrillo 25-38

4 What is the place of technical and scientific service companies (TSS) in the process of developing new products? insights on their managerial and organizational features

V. Lazzarotti, E. Pizzurno 39-53

5 Use of Web 2.0 applications in product development: an empirical study of the potential for knowledge creation and exchange in research and development

K.-I. Voigt, M. Ernst 54-68

6 Controlled temperature grinding under modified atmosphere for Almond (Prunus Dulcis) paste production

G. Aiello, G. La Scalia, L. Cannizzaro 69-82

7 The impact of new product introduction on supply chain ability to match supply and demand

R. Crippa, L. Larghi, M. Pero, A. Sianesi 83-93

8 Implementation of critical success factors in construction research and development process U. Kulatunga, D. Amaratunga and R. Haigh 94-106

9 A stochastic simulation approach for production scheduling and investment planning in the tile industry

G. Davoli, S. A. Gallo, M. W. Collins, R. Melloni 107-124

MultiCraft


Vol. 2, No. 9, 2010, pp. 1-12



www.ijest-ng.com


A proposed computer system on Kano model for new product development and innovation aspect: A case study is conducted by an attractive

attribute of automobile

Md Mamunur Rashid1*, Jun’ichi Tamaki2, A.M.M. Sharif Ullah2 and Akihiko Kubo2

1*Graduate School of Kitami Institute of Technology, Kitami Institute of Technology, Hokkaido, JAPAN

2 Department of Mechanical Engineering, Kitami Institute of Technology, Hokkaido, JAPAN *Corresponding Author: e-mail: [email protected]

Abstract Voice of Customer is important for new product development. New product development is a complex task in which a great deal of human physical resources, methods, and tools are involved. One of the well- appreciated models is Kano model for customer needs study for product development. Customer requirements are an important component of new product development. The customer expectations to the technical requirements of products are also necessary for new product development. The success of a new product development process for a desired customer satisfaction is sensitive to the customer needs assessment process. In most cases, customer needs of a product or product family are incorporated by setting the customer requirements and their relative importance in the first house of quality of QFD. This procedure is practically informal and does not present an obvious link between customer satisfaction and product attribute. In this view, Kano Model is a superior choice. Kano model has two dimensional questionnaires regarding customer satisfaction, i.e. functional and dysfunctional. Both functional and dysfunctional answer is determined Kano evaluation (product attribute). A computer system has been developed using the Monte-Carlo Simulation technique to simulate functional and dysfunctional answers independently and subsequently the Kano evaluation. Using this system one can determine the minimal number of respondents make a reliable conclusion for a definite product attribute. A case study is conducted for system verification by an attractive attribute regarding Kano model about an automobile. Keywords: Kano Model, Attributes of Product, New Product Development and Innovation, Monte-Carlo Simulation 1. Introduction Customer needs are changing due to technology, and their age, income, profession, education. The assessment of customer needs is now continuous process. In the case of new product development and innovation is now considered the customer satisfaction, affordability, production rate, technical ability, value chain and competition for successfully launch and sustaining the product in the market, which are shown in Fig.1 (Browing, 2006 and 2003). New product development is a complex engineering task in which a great deal of human-physical resources, methods, and tools are involved for greater customer satisfaction (Fujita and Matsuo, 2006) which are shown in Fig.2. Product development team of QFD could consider the customer requirements (CR) as an arbitrary basis in the first house of quality of QFD (Kobayashi, 2006; Poel, 2007 and Hari et al., 2007). For removing this arbitrary value of CR, a fuzzy QFD approach could be used to find appropriate CR from customer feedback (Bottani and Rizzi, 2006). In this perspective, Kano model (Kano et al., 1984) is also a better tool for determination CR for new product development and innovation. Presently, Kano model has been applied for multiple new product design and innovation for compliance customer need with respect to customer satisfaction (Sireli et al., 2007, Chen and Chuang, 2008, Chen at al., 2010, Lee and Huang, 2009, Xu et al., 2009). Section 2 describes the elements of Kano model for how to customer satisfaction, i.e. both functional and dysfunctional answer leads to a mapping Kano evaluation/customer needs regarding product attribute. The usual practice is to use questionnaires and

Rashid et al. / International Journal of Engineering, Science and Technology, Vol. 2, No. 9, 2010, pp. 1-12

2

obtain the opinion of customers. Yet, it is difficult to obtain answers of respondents on time. Ullah and Tamaki (2009, 2010) have shown how to simulate missing or unknown answer. Ullah and Tamaki’s study has been used for finding limited frequency of unknown respondents. The present study is also directed Kano model based computer system for generic respondents.

Customer Segment 1

Customer Segment 2

Product Family

SatisfactionAffordabilityProduction RateTechnical AbilityValue ChainCompetition

Main Challenge

Figure 1. Main challenge of new product development and innovation

Organizational Aspects:Strategic GoalsHierarchyCoordinationTeamingSupport…

√

×

…Voice of Customer:Customer SegmentsNeedsFeedbackSatisfaction…

Methods/Tools:Scientific AnalysisQFDTRIZAxiomatic DesignKnowledge-Based DesignSimulationBrainstormingLCADesign for XCAD/CAM/CAEPrototypingDesign of ExperimentSix-SigmaLeanMass CustomizationValidationVerification…

Peripheral Aspects:CompetitorsSupply ChainRegulationsEnvironment…

Product Development

Figure 2. Elements of new product development (Ullah and Tamaki, 2010)

In this circumstance, we deal to customer needs assessment a generic computer system procedure shown in section 3 on Kano model aspect for new product development to know the needs of the customers for a given product (or a set of products). It is noted that recently the authors, the proposed computer system’s verification and applications on Kano model aspect is presented (Rashid et al., 2010). Moreover, this raises a fundamental question that is how many customers should be asked to make a reliable conclusion for an attractive attribute. This question is answered as a case study discussion in section 4. It is also used for system verification. In this situation, a Kano model based computer system is shown a screen print in Appendix A and conclusion in section 5.


3

2. A Study the Frame of Kano Model

Kano model of customer satisfaction defines the relationship between product attribute regarding classifications of customer needs and customer satisfaction and provides five types of product attributes: 1) Must-be (M), 2) One-dimensional (O), 3) Attractive (A), 4) Indifferent (I), and 5) Reverse (R), as schematically illustrated Fig.3. In Fig.3, the upward vertical axis represents satisfaction and downward vertical axis represents dissatisfaction. The leftward horizontal axis represents absence of performance that is called dysfunctional side. The rightward horizontal axis represents presence of performance that is called functional side.

Figure 3. Relationship between products attribute regarding customer needs and customer satisfaction (Ullah and Tamaki,2010)

Table 1 describes the meaning of Must-be (M), One-dimensional (O), Attractive (A), Indifferent (I), and Reverse (R) attribute.

Table 1. Five categories of product attributes for customer satisfaction adapted from Ullah and Tamaki (2010)

The combination of functional and dysfunctional answers is then used to identify the status of the attribute in term of: 1) Must-

be, 2) One-dimensional, 3) Attractive, 4) Indifferent, or 5) Reverse. All possible combinations of customer answers and the corresponding type of product attribute are summarized in following Table 2.

Table 2. Kano evaluation (KE) adapted from Berger et al. (1993)

Functional (FA) (↓) Dysfunctional (DFA) (→) Like (L)

Must-be (M)

Neutral (N)

Live-with (Lw)

Dislike (D)

Like (L) Q A A A O Must-be (M) R I I I M Neutral (N) R I I I M Live-with (Lw) R I I I M Dislike (D) R R R R Q KE : A=Attractive, I=Indifferent, M=Must-be, O=One-dimensional, Q=Questionable, and R=Reverse

Product attributes Definition Recommendations Attractive An Attractive attribute leads to a better satisfaction, whereas it is not

expected to be in the product. Include a good number of Attractive attributes

One-dimensional A One-dimensional attribute fulfillment helps enhance the satisfaction and vice versa.

Include a good number of One-dimensional

Must-be A Must-be attribute absence produces absolute dissatisfaction and its presence does not increase satisfaction

Continue Must-be attributes

Indifferent An Indifferent attribute, that result neither in satisfaction nor dissatisfaction, whether fulfilled or not.

Avoid Indifferent attributes as many as possible

Reverse A Reverse attribute presence causes dissatisfaction and its absence causes satisfaction.

Avoid Reverse attributes


4

As seen from Table 2, besides the above mentioned five types of attribute in Table 1, there is one more type of attribute called Questionable. This occurs when one selects Like or Dislike from both functional and dysfunctional sides (i.e., when an answer does not make any sense). As mentioned earlier, Kano model is accommodating for integrating the VOC into the succeeding processes of product development. Thus, for the meaningful integration of VOC into the succeeding processes of product development, it is important to follow of recommendation of Table 1. The straight forward relationship is shown in Table 3 among functional answer (FA), dysfunctional answer (DFA) and Kano evaluation (KE). This table is also exposes a frame among functional answer (FA), dysfunctional answer (DFA) and Kano evaluation (KE).

Table 3. Correlations among FA, DFA and KE

Sl FA DFA Combination of FA and DFA KE1 Like Like Like Like Questionable (Q)

2 Like Must-be Like Must-be Attractive (A)3 Like Neutral Like Neutral Attractive (A)4 Like Live-with Like Live-with Attractive (A)5 Like Dislike Like Dislike One-dimensional (O)6 Must-be Like Must-be Like Reverse ( R)7 Must-be Must-be Must-be Must-be Indifferent (I)8 Must-be Neutral Must-be Neutral Indifferent (I)9 Must-be Live-with Must-be Live-with Indifferent (I)10 Must-be Dislike Must-be Dislike Must-be (M)11 Neutral Like Neutral Like Reverse ( R)12 Neutral Must-be Neutral Must-be Indifferent (I)13 Neutral Neutral Neutral Neutral Indifferent (I)14 Neutral Live-with Neutral Live-with Indifferent (I)15 Neutral Dislike Neutral Dislike Must-be (M)16 Live-with Like Live-with Like Reverse ( R)17 Live-with Must-be Live-with Must-be Indifferent (I)18 Live-with Neutral Live-with Neutral Indifferent (I)19 Live-with Live-with Live-with Live-with Indifferent (I)20 Live-with Dislike Live-with Dislike Must-be (M)21 Dislike Like Dislike Like Reverse ( R)22 Dislike Must-be Dislike Must-be Reverse ( R)23 Dislike Neutral Dislike Neutral Reverse ( R)24 Dislike Live-with Dislike Live-with Reverse ( R)25 Dislike Dislike Dislike Dislike Questionable (Q)

3. A Proposed Computer System For the design of computer system, a generic method of simulation using the concept of Monte Carlo is discussed in subsection 3.1. A proposed computer system for consumer needs analysis regarding kano model by simulate functional and dysfunctional answer independently and then calculate the probability of kano evaluation is discussed in subsection 3.2. While a proposed computer system for consumer needs analysis regarding Kano model by simulates the functional and dysfunctional answers for a given Kano evaluation is discussed in subsection 3.3.

3.1 A generic Method of Simulation using the concept of Monte Carlo The simulation of customer answer needs a method. This method is formulated in the following way by using Monte Carlo

simulation principle. For this reason, an event is a set of outcomes to which a probability is assigned. An event vector E = (E1... En), whose components are scalar-valued random variables on the same probability space (Ω, F, P). Every such random event vector gives rise to a probability measure. An event vector with non-negative entries is that which adds up to one. The event

vector components must sum to one 11

=∑=

n

iiP . The requirement of each individual component must have a probability between

zero and one; 0 < Pi < 1; for all i. The probability of an event is a non-negative real number: 0 < P (E) < 1; FE∈∀ ; Where, probability P of some event E is denoted P(E), F is the event space and E is any event in F. Any countable sequence of pair wise

disjoint events E1, E2,…, En satisfies P (E1 U E2 U ,…, En) = )(1∑=

n

iiEP . This simulation process is also considered discrete-event

simulation. The operation of a system of discrete event simulation is represented as a chronological sequence of events. Each event occurs at an instant in time and marks a change of state in the system. In discrete-event simulations, events are generated instantaneously. This simulation is also followed at least one list of simulation events. The simulation process has been needed random number in the interval [0, 1]. It is normally generated by using RAND () formula. Theoretically a discrete–event


5

simulation could run forever. Thus, this simulation is done after processing ‘n’ number of events. The theoretical explain of present simulation process is constituted among start, do loop and end phase. Start Phase is initialized ending condition to FALSE, system state variables, clock (usually starts at simulation time zero and schedule an initial event (i.e. put some initial event in to the Events list). When ending condition is FALSE then do loop or while loop phase is acted to set clock to next event time, to do next event and remove from the event list and to update statistics. End Phase is generated the statistical report. The simulation process is following:

Inputs: V= (E1,…, En) //Event Vector P= (Pr (E1),…, Pr (En)) //Event Probability Vector N //Number of Trials Calculate: CPr (Ei) =Pr (E1) +…+Pr (Ei), i=1,…, n //Cumulative Probability of Events For j=1,…, N (1) Do rj ∈[0, 1] //rj is a random number in the interval [0, 1] If rj≤CPr (E1) Then Sj = E1 Otherwise For i=2,…, n If CPr (Ei-1) <rj≤CPr (Ei) Then Sj = Ei

This formulation also guarantees that the summation of all CPr (Si) is equal to 1 (i.e., the axiom of Normality as required by the concept of classical probability). Therefore, simulating Si, the probability of Si should be maintained around CPr (Si).However, for the sake of simulation, first the cumulative probability should be considered, as follows:

CPr =Pr (S1)+,...,+Pr(Si), where , i=1,…,n

S1,…,SNSi∈E1,…,En

Event Vector E= (E1,…,En) Probability Vector Pr = (Pr(E1),…,Pr(En))

Simulation Process

Figure 4. A generic method of simulation It is important that the cumulative probability of the last event Sn is 1, i.e. CPr (Sn) =1. The cumulative probability, CPr (Si) can be applied along with a random number rj in the scale [0, 1] to simulate the states Ss ∈S1,…, Sn.rj is between the cumulative probabilities of the two consecutive event Sj and Si, (j=i-1), then Ss = Si.

3.2 A proposed computer system for consumer needs analysis regarding Kano Model by simulate functional and dysfunctional answer independently and then calculate the probability of Kano Evaluation.

Figure 5 shows a customer need analysis model for the proposed simulation process. Six steps are involved in this process, as described below:

Step 1: Choices of FA and DFA of unknown customer, FA, or DFA ∈Like (L), Must-be (M), Neutral (N), Live-with (LW), Dislike (D)

Step 2: Generate a set of random inputs Step 3. Simulation of dysfunctional answer of customer independently Step 4. Simulation of functional answer of customer independently Step 5. Simulation of customer evaluation by using combination of FA and DFA Step 6. Analysis for consistency of developed model.

A unique probability distribution may be hard to identify, when information is scarce, vague, or conflicting (Coolen et. al., 2010) for product design information. In that case probability represents the real knowledge, and provides tools to modeling and work weaker states of information. As a result, the unknown customers’ FA and DFA is generally uncertain, i.e., scarce, vague etc. It is facilitated to consider equal probability of choices. This formulation also guarantees that the summation of all choices probabilities


6

is equal to 1 (i.e., the axiom of Normality as required by the concept of classical probability). In this simulation process probability has been applied. (Walley, 1991; Cooman and Hermans, 2008). Generic individuals are considered and it is expected that these individuals opinion are enough Choices FA, or DFA ∈L, M, N, Lw, D is considered uniform cumulative vector probability of individuals. According to step 2, a set of random inputs has been generated by using the formula=RAND () in a cell of Microsoft office Excel.

Simulate Functional Answer(FA)

FA= (Like, Must-be, Neutral, Live-with, Dislike)

S1

Simulate Kano Evaluation(KE)

A simulation Instance from FA/DFA

Simulate Dysfunctional Answer(DFA)

S2

E=(A,M,I,O,R,Q) given S1 and S2

DFA= (Like, Must-be, Neutral, Live-with, Dislike)

0

0.05

0.1

0.15

0.2

0.25

Like Must-be Neutral Live-with Dislike

Pr(.)

FA/ DFA

Figure 5. Analysis of scenario

In Table 3, shows the rules of combination of functional and dysfunctional answer for customer Evaluation from Kano model. This rule was applied for simulated the unknown customer answer, where combination of answers for functional and dysfunctional parts of Kano questionnaire for choosing evaluation KE ∈A, O, M, I, R, Q. Therefore, a system is developed to implement the simulation in accordance with Eq.1, (i e., in accordance with steps 1-6).

3.3 A proposed computer system for consumer needs analysis regarding Kano Model by simulates the functional and dysfunctional answers for a given Kano evaluation.

Figure 6 shows illustrate the proposed simulation process for consumer needs analysis regarding Kano Model by simulates the functional and dysfunctional answers for a given Kano evaluation (KE) (Must-be, Attractive, One-dimensional, Indifferent, or Reverse and Questionable). Six steps are also involved in this process, as described below:

Step 1: Choices of Kano evaluations of customer, KE ∈A, I, M, O, Q, R, is considered uniform probability.

Simulate Kano Evaluation (KE)

E= (A, M, I, O, R, Q)

Kano Rules

S1

Simulate Functional Answer (FA)

F1 given S1

Simulate Dysfunctional Answer(DFA)

F2 given S1 and F1A simulation Instance

Figure 6. Proposed Simulation Process


7

Step 2: Generate a set of random inputs Step 3. Simulate the Kano evaluation Step 4. Simulation of functional answer (FA) from simulated KE by using Kano Rules Step 5. Simulation of dysfunctional answer (DFA) from KE by using Kano Rules Step 6. Analysis for consistency of developed model.

4. A Case Study for Model Verifications and Discussions

A case is considered in Fig. 7 for model verification and application of the system. According to Fig.7, there is a questionnaire regarding a product (automobile) attribute (radio antenna automatically retracts when the radio is turned off). It is well-known that radio antenna of an automobile is “Attractive” attribute. Therefore, the ideal answer of a respondent would be “Like” from functional side (i.e., the automobile should have radio antenna automatically retracts) and “Neutral” from dysfunctional side (i.e., if the radio antenna does not automatically retract when the radio is turned off, I am neutral in this regard). This combination of answer (Like, Neutral) yields an “Attractive” attribute according to Kano Evaluation (see Table 2). In reality, respondents exhibit a rather fuzzy behavior and sometimes answer different than the ideal one. For example, see the frequency of the answers of 23 (Berger et al., 1993) respondents shown in Fig.7. As a result, some respondents answer makes the attribute “Attractive” some others make it “Indifferent” and so on. This raises a fundamental question that is how many respondents should be requested to know for certain that the specified attribute is an Attractive attribute or not.

0

2

4

6

8

10

12

14

16

18

20


0

2

4

6

8

10

12

14

16

18

20


The radio antenna of an automobile automatically retracts when the radio is turned off.

An Ideal AnswerFunctional Answer

LikeMust-beNeutralLive-withDislike

Dysfunctional Answer

LikeMust-beNeutralLive-withDislike

Freq

uenc

yFr

eque

ncyFunctional Answer

Dysfunctional Answer

Real Answer

Figure 7. Ambiguity in respondents answer

This question can be answered using the system shown in the previous section. To use the system shown in the previous sub section in 3.2, the first step is to input the probability vectors of functional answers and dysfunctional answers. To determine the probability vectors of functional/ dysfunctional answers the following procedure can be used. As it is seen from the case shown in Fig.8, from functional side, the respondents are “most-likely” to choose “Like”, “less-likely” to choose “Must-be, Neutral, Live-with and Dislike”. On the other hand, from the dysfunctional side, the respondents are “quite-likely” to choose Neutral , “some-likely” to choose “Must-be, Live-with and Dislike” and less likely “Like”.

0

0.25

0.5

0.75

1

1.25

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Mem

bers

hip

Val

ue

Pr

Less-likely some-likely quite-likely most-likely

Figure 8. Defining linguistic likelihoods by fuzzy numbers (Ullah and Tamaki, 2010)


8

These linguistic likelihoods (“most-likely”, “some-likely”, “less-likely”, and so on) can be transformed into numerical probability using fuzzy logic. Ullah and Tamaki, 2010 have afforded a fuzzy logic method, which is used here. Figure 8 illustrates the fuzzy numbers defining the linguistic likelihoods “most-likely”, “quite-likely”, “some-likely”, and “less-likely.” From the linguistic likelihoods shown in Fig.8, the average value and lower and upper limits of are determined using centroid method (Ullah and Harib, 2006) and α-cuts at α=0.5, respectively. The results are shown in Table 4.

Table 4. Numerical probability of linguistic likelihoods

Pr Linguistic likelihoods

Lower limit Upper limit Average most-likely 0.85 1 0.9 quite-likely 0.5 0.85 2/3 some-likely 0.15 0.5 1/3 less-likely 0 0.15 0.1

Table 5 shows the probabilities of functional answers for average and worst-case scenarios. For average scenario the average probabilities of linguistic likelihoods (shown in Table 4) are used. These probabilities are normalized to calculate crisp probabilities shown in 4-th column in Table 5. For worst-case scenario, the lower limit of most-likely is used and upper limits of quite –likely, some-likely and less-likely are used. These limits are normalized to calculate the crisp probabilities for worst-case scenarios, as shown in last column in Table 5.

Table 5. Probabilities of functional answers for average and worst-case scenarios. average scenario worst-case scenario

Functional Answers

Linguistic likelihoods

average Pr Crisp Pr upper/lower

limits of Pr Crisp Pr

Like Most-likely 0.9 0.69230769 0.85 0.5862069

Must-be some-likely 0.1 0.07692308 0.15 0.10344828

Neutral some-likely 0.1 0.07692308 0.15 0.10344828

Live-with Less-likely 0.1 0.07692308 0.15 0.10344828 Dislike Less-likely 0.1 0.07692308 0.15 0.10344828

Similarly the probabilities of dysfunctional answers for average and worst-case scenarios are determined and listed in Table 6.

Table 6. Probabilities of dysfunctional answers for average and worst-case scenarios. average scenario worst-case scenario

Dysfunctional Answers

Linguistic likelihoods

average Pr Crisp Pr upper/lower

limits of Pr Crisp Pr

Like less-likely 0.1 0.05665722 0.15 0.06

Must-be some-likely 0.333 0.18866856 0.5 0.2

Neutral quite-likely 0.666 0.37733711 0.85 0.34

Live-with some-likely 0.333 0.18866856 0.5 0.2

Dislike some-likely 0.333 0.18866856 0.5 0.2

The results shown in Tables 5-6 provides two sets probabilities of functional/dysfunctional answers. These probabilities are illustrated in Fig. 9. Using these probabilities a study has been carried out to determine the minimum number of respondents to conclude whether or not an attribute is Attractive. Figure 10 shows results for average scenario. As observed from Fig. 10, for 25 respondents there is overlap among the probabilities of Attractive and Indifferent. This means that using the results of 25


9

respondents it is not reliable to conclude that the attribute is a Reverse attribute. For the case of 50 respondents, there is no overlap between the probabilities of Attractive and Indifferent, this trend remains more or less the same for more respondents (e.g., compares the results of 50 respondents,100 respondents and 200 respondents shown in Fig.10).

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Like Must-be Neutral Live-with Dislike0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1


Average Scenario

Functional Answers Dysfunctional Answers

Functional Answers Dysfunctional Answers

Worse-case Scenario

Prob

abilit

yPr

obab

ility

Prob

abilit

yPr

obab

ility

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Like Must-be Neutral Live-with Dislike0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1


Figure 9. Probabilities of functional/dysfunctional answers for two scenarios

0

0.2

0.4

0.6

0.8

1

Attractive One-dimensional Must-be Indifferent Reverse Questionable

Pr(

.)

Evaluations

0

0.2

0.4

0.6

0.8

1


Pr(

.)

Evaluations

0

0.2

0.4

0.6

0.8

1


Pr(.)

Evaluations

0

0.2

0.4

0.6

0.8

1


Pr(.)

Evaluations

25 respondents50 respondents

100 respondents

200 respondents

Figure 10. Number of respondents versus Kano Evaluation for average scenario


10

Therefore, at least answer from 50 respondents should be collected to determine that an attribute is an Attractive attribute. What if the other set of probabilities (probabilities for worst-case scenario) is used? Figure 11 shows the results for the case. In that case 25 respondents it is not reliable to conclude that the attribute is an Attractive attribute. For the case of 50 respondents, there is no an overlap between the probabilities of Attractive and Indifferent, this trend remains more or less the same for more respondents (e.g., compares the results of 50 respondents,100 respondents and 200 respondents shown in Fig.11).

.

0

0.2

0.4

0.6

0.8

1


Pr(.)

Evaluations

0

0.2

0.4

0.6

0.8

1


Pr(

.)

Evaluations

0

0.2

0.4

0.6

0.8

1


Pr(.)

Evaluations

0

0.2

0.4

0.6

0.8

1


Pr(.)

Evaluations

25 respondents 50 respondents

100 respondents200 respondents

Figure 11. Number of respondents Versus Kano Evaluations for worst case scenario

According to the above results it can be completed that if the answers of at least 50 respondents should be considered an Attractive attribute. This working standard can be used as a guideline while distinctive an Attractive attribute from others in all kinds of products. A proposed computer system on Kano model aspect can support a product development team by providing an answer to the question: minimal how many respondents should be asked to determine whether or not an attribute is Must-be, One-dimensional or Indifferent in accordance with Kano Model. Exactly it is found that at least 50 respondents should be requested to verify whether or not an attribute is an Attractive attribute. Monte Carlo simulation applies random number and simulates states of a variable using a predefined probability mass or density function. In for more details are illustrated in chapter 20 for how to generate and use random number and error occurs because of the limitation of computer–generated random number for Monte-Carlo simulation. This error will be reduces exponentially with the increase in number of iterations N (Hiller and Lieberman, 2005). In this case study, when increased numbers of iterations (number of respondents) then the errors are decreased in Figs. 10 and 11. Therefore, this case study is shown for both verification and application of the system.

5. Conclusions

A computer system can be developed to simulate functional (FA) and dysfunctional answers (DFA) independently and then calculate the probability of Kano evaluation (KE). A system can also be developed to simulate the functional and dysfunctional answers for a given Kano evaluation (KE). This system can comply regarding Kano Model based for product attribute regarding customer needs with customer satisfaction.


11

Appendix A : A screen print of the Proposed Computer System on Kano Model

References Berger, C.; Blauth, R.; Boger, D.; Bolster, C.; Burchill, G.; Du-Mouchel, W.; Pouliot, F.; Richter, R.; Rubinoff, A.; Shen, D.; Timko, M.; and Walden, D.; (1993): Kano's Methods for Customer Defined Quality, The Center for Quality Management Journal, Vol. No.2, No.4, pp.2-36. Browing, T.R., Fricke, E. and Negele, H., 2006. Key concepts in modeling product development processes, System Engineering, Vol.9. No.2, pp.104-128. Browning T.R., 2003. On Customer Value and Improvement in Product Development Processes, Systems Engineering, Vol. No.1, pp. 49-61. Bottani, E. and Rizi, A., 2006. Strategic Management of Logistics Service: A Fuzzy QFD Approach, International Journal of Production Economics, Vol.103, No. 2, pp.585-599. Chen C.C. and Chuang M.C., 2008. Integrating the Kano Model into a Robust Design Approach to Enhance Customer Satisfaction with Product Design, International Journal of Production Economics, Vol.114. No.2, pp.667-681. Chang C.C., Chen P.L., Chiu F.R. and Chen Y.K., 2009. Application of Neural Networks and Kano’s Method to Content Recommendation in Web Personalization, Expert Systems with Applications, Vol.36, pp.5310-5316. Chen H.C., Lee T.R. Lin H.Y. and Wu H.C., 2010. Application of TRIZ and the Kano Method to Home Life Industry


12

Innovation, International Journal of Innovation and Learning, Vol.7. No.1, pp.64-84. Cooman G. and Hermans F., Imprecise Probability Trees: Bridging Two Theories of Imprecise Probability, Artificial Intelligence, Vol.172, No.11 (2008), pp.1400-1427. Coolen, F.P.A.; Troffaes, M.C.M. ; Augustin, T., 2010. Imprecise Probability, International Encyclopedia of statistical Sciences, Spring 2010, http://www.springer.com/statistics/book/978-3-642-04897-5. Fujita, K. and Matsuo, T., 2006. Survey and analysis of utilization of tools and methods in product development, Trans Japan Soc Mech Eng Ser C Vol.72. No.713, pp. 290–297 (in Japanese). Hari, A., Kasser, J.E., and Weiss, M.P., 2007. How Lessons Learned from Using QFD Led to the Evolution of a Process for Creating Quality Requirements for Complex Systems, System Engineering, 10(1), 45-63. Hillier, F.S. and Lieberman, G.J., 2005. Introduction to Operations Research (Eight Edition), MacGraw Hill, New York. Kano, N., Seraku, N., Takahashi, F. and Tsuji, S., 1984. Attractive quality and must-be quality, Hinshitsu, Vol.14. No.2, pp.39–48 (in Japanese). Kobayashi, H., 2006. A systematic approach to eco-innovative product design based on life cycle planning, Advanced Engineering Informatics, Vol.20, pp 113-125. Lee Y.C. and Huang S.Y., 2009. A New Fuzzy Concept Approach for a New Fuzzy Concept on Kano's Model, Expert Systems with Applications, Vol.36, No.3, pp.4479-4484. Li Y., Tang J., Luo X. and Xu J., 2009. An Integrated Method of Rough Set, Kano's Model and AHP for Rating Customer Requirements' Final Importance, Expert Systems with Applications, Vol. 36, No.3, pp.7045-7053. Mannion M. and Kaindle H., 2008.Using Parameters and Discriminant for Product Line Requirements, Systems Engineering, Vol. 11, No.1, pp.61-80. Poel, I.V.d., 2007. Methodological problems in QFD and directions for future development, Research in Engineering Design, Vol.18. No.1, pp. 21-36. Rashid M. M., Ullah A.M.M.S., Tamaki J. and Kubo A., 2010a. A Virtual Customer Needs System for Product Development, Proceedings of the JSPE Hokkaido chapter Annual Conference ,04 September , 2010. Sireli Y., Kauffmann P. and Ozan E., 2007. Integration of Kano's Model into QFD for Multiple Product Design, IEEE Transactions on Engineering Management, Vol.54, No.2, pp.380-390. Ullah, A.M.M.S. and Tamaki J., 2009. Uncertain Customer Needs Analysis for Product Development: A Kano Model Perspective, Proceedings of the Sixth International Symposium on Environmentally Conscious Design and Inverse Manufacturing, Sapporo, Japan. Ullah A.M.M.S. and Tamaki J., 2010. Analysis of Kano-Model-Based Customer Needs for Product Development, System Engineering, Accepted March 23, 2010, DOI 10.1002/sys.20168 (in print). Walley P., 1991. Statistical Reasoning with Imprecise Probabilities, Chapman & Hall. Xu Q., Jiao R.J., Yang X., Helander M., Khalid H.M and Opperud A., 2009. An analytical Kano model for customer need analysis, Design Studies, Vol.30, No.1, pp. 87-110. Biographical notes Md Mamunur Rashid is a Management Counsellor (Faculty) at Bangladesh Institute of Management since 2004. He is currently working at Graduate School of Kitami Institute of Technology, Japan since January 2010. He received B.Sc in Mechanical Engineering from Bangladesh Institute of Technology, Rajshahi in 1993, M.Sc in Mechanical Engineering Bangladesh University of Engineering and Technology, Dhaka in 1996, MBA from Bangladesh Open University in 2004. He also did PGD in HRM, PGD in Marketing Management and Diploma in Computer Science and Application. Prior to joining Bangladesh Institute of Management he was a Mechanical Engineer at Jamuna Fertilizer Company Limited of BCIC in 1997-2004. His teaches productivity, TQM, project management related courses in the BBA and MBA level at DIU, BOU. IBAISU, BUBT at Dhaka, Bangladesh as an adjunct faculty. His main research area is customer needs analysis for product development. His is a member of IEB, JSPE, BSME, BSTD, IPM and BCS. Jun’ichi Tamaki is a Professor of Mechanical Engineering at Kitami Institute of Technology since 1996. He is currently the Vice President for Academic Affairs at Kitami Institute of Technology. He received his Doctoral degree in Engineering from Tohoku University in 1987. He served as the Chairman of International committee for Abrasive Technology in 2002 and the President of Japan Society for Abrasive Technology in 2007. His main research interest is ultra-precision machining and intelligent product realization. He is member of JSME, JSPE, JSAT, JSEM and ASPE. A.M.M. Sharif Ullah is an Associate Professor of Design and Manufacturing at Kitami Institute of Technology. He received his B.Sc in Mechanical Engineering from Bangladesh University of Engineering and Technology in 1992 and a Masters and a Doctor of Engineering in Mechanical Engineering from Kansei University in 1996 and 1999, respectively. He was an Assistant Professor of Industrial System Engineering at Asian Institute of Technology in 2000-2002. Prior to joining Kitami Institute of Technology he was an Associate Professor at United Arab Emirates University. His teaches manufacturing and product realization related courses in the undergraduate and graduate degree programs. His main research area is application of artificial intelligence in systems design and manufacturing. His is a member of JSME, ASME, JSPE, JSAT, AAAI, and SME. Akihiko Kubo is an Assistant Professor of Mechanical Engineering at Kitami Institute of Technology. He is member of JSME, JSPE. Received August 2010 Accepted October 2010 Final acceptance in revised form October 2010

MultiCraft


Vol. 2, No. 9, 2010, pp. 13-24



www.ijest-ng.com


Successful new product development in the food packaging industry: evidence from a case study

Barbara Bigliardi, Eleonora Bottani, Roberto Montanari & Giuseppe Vignali

Department of Industrial Engineering, University of Parma, Viale G.P.Usberti 181/A – 43124 Parma, ITALY *Corresponding Author: Giuseppe Vignali, [email protected]; Tel. +39 0521 906061; Fax +39 0521 905705

Abstract The relevance of product innovations and new product development for the competitive performance of firms and for the long-term economic growth is a known and recognized topic. In the context of the food industry, process and product innovations are usually the result of cross-discipline ideas, involving, for instance, biology, chemistry, technology, engineering, nutrition and law. Furthermore, the food industry suffers from the fact that the benefits of innovations are often not evident at the manufacturing stage. This paper analyzes a successful case of product innovation, in the context of the Italian food industry. The analysis is proposed in the form of a case study-based research, which was carried out through a questionnaire survey and some field interviews with managers of a food company located in Northern Italy. Specifically, the company selected operates as a packaging manufacturer, providing plants, equipments and sachets for food and drink packaging. The study focuses on the development of a new product, which the company has recently launched on the market in response to the needs of packaging liquid, viscous or creamy foods. By presenting a successful case study, this paper aims to highlight the strengths and weaknesses of the new product development process undertaken by the company (i.e., from the idea of the new product to the launch on the market and patent). At the same time, results presented provide useful guidelines for new product development processes in the food context. Keywords: product innovation, new product development, patent, food packaging industry, case study.

1. Introduction

Innovation is widely recognized, from both academics and managers, as a fundamental requisite of a company in order to grow in today’s competitive context, and as one of the key drivers of the firms’ long-term success (Baker and Sinkula, 2002; Balkin et al., 2000; Darroch and McNaugton, 2002; Lyon and Ferrier, 2002). The reason is that innovative companies will be able to respond to environmental challenges faster and better than the non-innovative ones (Jimenez et al., 2008). Therefore, organizations have been forced to embrace innovation as an integral part of their corporate strategy, and to offer products that are adapted to the needs of target customers in order to create a sustainable competitive advantage and to stay ahead of the competition (Calantone et al., 1995; Damanpour and Gopalakrishnan, 2001; Scarborough and Zimmerer, 2002). During the last decades, a shift has been observed from the technology push innovation model to the market pull one, thus forcing companies to focus more on quality product rather than on internal efficiency, and to quickly identify changing customers’ needs, to develop more complex products to satisfy those needs, and to provide higher level of customer supports and service (Sheperd and Ahmed, 2000). As a consequence, greater focus has been placed on new product development (NPD).

Taking account of the above, this paper examines an example of successful NPD process. The literature review reported in section 2 focuses on NPD, and discusses in particular the factors that may determine its success. In the same section, a brief mention to the protection of the results of NPD process is reported. Section 3 describes the research methodology followed for the study, while section 4 reports the results from the case study, that is the position of a small company operating in the food packaging industry. Specifically, the context investigated and the company are briefly described, followed by the description of the new product developed, the related five-step NPD process and some economic figures. In conclusions, some guidelines are extrapolated from the case study, and managerial implications and future research directions are presented in section 5.

Bigliardi et al. / International Journal of Engineering, Science and Technology, Vol. 2, No. 9, 2010, pp. 13-24

14

2. Literature analysis

Innovation has been object of numerous studies in literature, notwithstanding, there is not a general definition of the term “innovation”, thus generating greater ambiguity (Garcia and Calantone, 2002). However, innovation may be defined as the successful introduction of something new and useful, for example new methods, techniques, or practices or new or altered products and services. As for the different forms of innovation, Clarysse et al. (1998) and Lundvall (1992) identify four domains of innovation, interacting each other (Figure 1), namely:

1. Product innovation, that is any good, service or idea that is perceived by someone as new; 2. Process innovation, referring to the adaptation of existing production lines as well as the installation of an entirely new

infrastructure and the implementation of new technologies, and generally it allows the creation of new products; 3. Organisational innovation, that is changes in marketing, purchases, sales, administration, management, staff policy and so

on; 4. Market innovation, defined as the exploitation of new territorial markets and the penetration of new market segments

within existing markets.

Figure 1. The four types of innovation. The innovation process can be seen as a continuum starting from basic or applied research and ending with the development of

a commercial product (IRI, 2000). Innovation projects, however, usually do not pass all the phases of this continuum, rather companies, depending on the type of project and the type of product/process to be developed, follow a specific path of innovation. NPD refers to the process of bringing a new product or service to market and involves several steps, namely idea generation, product design, product engineering, market research and marketing analysis, and so on. Companies typically see NPD as the first stage in generating and commercializing new products within the overall strategic process of product life cycle management. In today’s environments, NPD is seen as a key factor mainly due to three reasons: first, the increasing international competition; second, the fragmenting and demanding markets; third the diverse and changing technologies (Wheelwright and Clark, 1992). Competitive advantage is more and more derived from knowledge, technological skills and experience in NPD (Tidd et al., 1997). Consequently, the NPD process has become the focus of recent research that may be classified into two main groups: (i) NPD process and (ii) NPD success.

Countless models, both normative and descriptive, have been proposed aiming at the definition of the steps to be followed to develop a new product. As for the normative models, it is possible to cite, for example, the works by Klompmaker et al. (1976) and Hanan (1970), which proposed a 27-steps model of NPD and a 24-activities flow diagram suitable for non-industrial goods respectively, while McGuire (1973) proposed a similar model for industrial goods. As far as the descriptive models are concerned, these have been developed on the basis of previous NPD projects or through direct observations of how firms manage a NPD project. What emerges from the study on this matter is that not all the steps are common to all NPD projects, nor are the steps necessarily undertaken with the same relative emphasis. However, most of these models agree in the identification of a series of phases, to be depicted schematically as a flow diagram, always present in the NPD process, regardless of the size of the company, the type of product or the industry it belongs to: a first “recognition phase”, aiming at the exploration of new product needs, the “idea formulation and screening idea phase”, and, obviously, “the product development phase” (Cooper, 2001).


15

In recent years, mainly due to the competitive environment where companies have to operate, another important phase is undertaken by more and more companies as the final phase of the NPD process: the protection of the new product, especially in the case of products that are strategic for the firm. Among all, the instrument widely recognized as the most effective one is the patent. Patents are generally accepted as indicators of the innovation and R&D process, mainly due to the widespread availability of their statistics collected over long periods of time across nations and regions (Thomas et al., 2010). As stressed by Abraham and Moitra (2001), most studies have used patent statistics as a tool to investigate the relationship between technological development and economic growth, as well as to assess the research and innovation process within both a national and international context. Other studies have analyzed patents from an internal standpoint, thus assessing from a company’s policy perspective the level of technology development in a specific industry, as well as analyzing a firm’s policy with regard to R&D, estimation of technological strengths and weaknesses of competitors, and exploitation of foreign markets (Abraham and Moitra, 2001). Thus, even though not all inventions are patented and not all patents are useful, they have become the most common indicator of innovative output. The economic literature also recognizes patents as the way to inhibit immediate direct competition and preserve an adequate profit margin (Malewicki and Sivakumar, 2004). Patents tend to cover product innovation (Drucker, 1985), as innovative products are supposed to be novel and non-obvious referred to previous knowledge as condition for issuance. Patenting activity may be undertaken both within the nation where the innovation is developed, and in a foreign country. As for the latter activities, these usually aim at protecting a potential market in that country for a firm’s products, but due to the prolonged and expensive process usually required, companies often patent abroad only when it is confident that a relatively large market exists for its products in that specific country.

As for the NPD success matter, special attention has been paid to the measurement of innovation performance, and NPD performance in particular, and specifically to those factors that determine the success (or failure) of a NPD process. Success or failure of NPD activities have been found to be determined by the steps characterizing a NPD project as it moves from the idea generation to the new product commercialization (Rothwell et al., 1974). As there are different types of new products, the term successful NPD may be interpreted in many ways. Montoya-Weiss and Calantone (1994) for example, identified four categories of variables that determine the performance of a NPD process, and in turn, its success or failure, namely: (i) strategic factors, (ii) organizational factors, (iii) development process factors and (iv) market innovation factors. Cooper (1999) identified the eight factors listed below:

i. up-front homework before proceeding further from the idea stage; ii. building in the voice of the customer;

iii. seeking differentiated and superior products; iv. early and stable product definition before actual development; v. strong market launch;

vi. tough go/kill decision points; vii. organizing around cross-functional project teams;

viii. building an international orientation into the NPD process. A research by Shepard and Ahmed (2000) reveals success factors within NPD process as follows:

i. a strong market orientation; ii. an in-depth understanding of user needs and wants;

iii. a unique superior product, a product with a high performance to cost ratio; iv. a strong market launch, backed by significant resources devoted to the selling/promotion effort; v. an attractive market, a high need level, a large growing market, and uncompetitive market;

vi. synergy in a number of areas, including technological and marketing; vii. top management support;

viii. good internal and external communications. Shenar et al. (2002) proposed in their works three main dimensions of success: (i) meeting design goals, (ii) benefits to

customers, and (iii) commercial success and future potential, while Millward and Lewis (2005), examining the performance of small and medium manufacturing firms, based their research on the consideration of the following factors affecting the success of the NPD process: (i) undertaking up-front research into competitors and suppliers, and building in the voice of the customer; (ii) sharp, early product definition in order to target differentiated, superior products; (iii) an international market-focused orientation with effective internal and external communications; (iv) competent, truly cross-functional project teams guided by strong project leaders; (v) senior management support with unhindered access to financial, personnel and political resources; (vi) thoroughly planned and resourced development stages (including market launch) with pre-defined, tough “go” or “kill” critical-decision points in the process.

Finally, Jokioinen and Suomala (2006), in their multiple case study-based research proposed four example of successful innovative firms, and by examining in depth each case, identified the following as the main success factors:

i. high leverage for production efficiency; ii. short pay-back time for customers;

iii. quick installation; iv. simple product to be used;


16

v. effective and innovative utilisation of well-known technology in the developer company; vi. short pay back time of the NPD process costs;

vii. better quality of the production process and end product. Most of the studies above mentioned, however, refer to large, well-established companies; conversely, innovation mechanisms

and NPD are rarely investigated in companies with little technological endorsement (Moreira, 2005); there is thus limited knowledge as regard NPD processes within small and medium enterprises (SMEs). Only two notable studies by Woodcock et al. (2000) and Freel (2000) may be cited. The Authors of these works identified a number of common elements with large firms and the main constraint that SMEs have to face with, respectively. On the basis of this premise, our paper aims to add value to the current debate on NPD within SMEs. 3. Research methodology

The analysis described in this paper is the result of a case study-based research. A “case study” is defined by Yin (2009) as “an empirical inquiry that investigates a contemporary phenomenon within its real-life context; when the boundaries between phenomenon and context are not clearly evident; and in which multiple sources of evidence are used”. Case studies are frequently used as field research where an investigator tries to conjecture answers to ‘what’, ‘how’ and ‘why’ questions, by focusing on observing contemporary events (Yin, 2009).

In this paper, a case study was developed with the purpose of answering two primary research questions, namely: • which are the main steps of a NPD process in the food industry? • which are the critical factors affecting the success of NPD in that context and in particular in SMEs?

The intended use of case study-based research is thus theory building (Jaspers, 2007), and, in particular, the aim of the study is

identifying best practices in NPD for SMEs operating in the food packaging industry. The reasons for selecting this context mainly refer to the fact that process and product innovations of the food industry are usually the result of cross-discipline ideas, involving, for instance, biology, chemistry, technology, engineering, nutrition and law. Furthermore, the benefits of innovations in the food industry often are not evident in the manufacturing stage; hence, there is the need for field studies which clearly highlight such benefits, as well as the critical factors of successful NPD processes in the food industry.

To select the company to be investigated, we first identified a group of SMEs operating in the food manufacturing or machinery fields, and located in Northern Italy. The group of companies was defined including SMEs which concluded at least one successful NPD process during the last year. Such companies were contacted and asked for their willingness to be investigated through a case study; the company analyzed in this paper was finally selected among those which indicated their availability for a field investigation.

Case studies typically involve multiple sources of information (Yin, 2009): in the present study, the sources of information were two questionnaires, which were used as a guideline for as many semi-structured interviews with the company’s managers. The first questionnaire consisted of 15 questions, focusing on:

i. general aspects of product innovation practices used in the company; ii. the NPD process and its management, referring to a recent product innovation made by the company;

iii. innovation protection mechanisms and their role for the company considered. The questionnaire was emailed to the company’s administrator, and answered through a direct interview in January 2010.

Results collected from the interview were elaborated and submitted to the company’s administrator for his approval. At the same time, from these outcomes, we identified the need for further investigating some specific aspects, such as the technical features of the product developed and some economic figures of the NPD process. These topics were included in a second questionnaire, which was sent to the company’s administrator in April 2010, and answered through a second semi-structured interview. Both interviews were held by some academics of the University of Parma (Italy), which were identified between research experts in innovation, supply chain management, industrial plants and food processing. The results obtained from the case studies were finally validated by the company’s top management in May 2010. 4. Case study 4.1 The context: innovation in the food packaging industry

The food industry has been a relatively slow-moving sector for many years, while recently it has focused more on innovations in marketing, distribution, development of new products and new packaging solutions (Siegrist, 2008). In response to changed life styles, in fact, it has become imperative for food companies to explore ways to improve their productivity, in terms of preserving product safety, using sustainable packaging materials, implementing flexible and standardized technology, and adopting proven management principles. In the food industry, innovations are currently driven by the goal to market more healthful foods, as well as by some general innovation trends, such as the creation of food that can ‘drive’ health, the push for variety, the market growth


17

and expansion of the food and beverage industries (Moskowitz and Hartmann, 2008). These trends are forcing the food industry to develop new products, packaging and equipments, in order to satisfy the customer’s needs in terms of flexibility and quality. In the context of the food packaging, in particular, the attention has been focused on increasing the ease of use of the package and improving the shelf life and quality of packaged foods (Mahalik and Nambiara, 2010).

A recently developed packaging system is the Form Fill Seal (FFS) technology, that is an integrated system of forming a package, filling the product into the package and sealing it, usually in a single machinery. Such technology provides high production rates at economical and flexible mechanism, and enables realizing a flexible single-dose packaging, made with polymeric materials (Lee et al., 2008). These materials (like Ethylene Vinyl Alcohol, EVOH, or Ethylene Vinyl Acetate, EVA) have almost completely substituted the cellulosic ones, thus allowing the reduction of food aroma losses through the packaging, and increasing the shelf life of food products (Peychès-Bach et al., 2009).

The field of single-dose packaging made with FFS technology, which is the specific context of our study, has shown a considerable increase in innovation and NPD, as confirmed by data on international and Italian patents applications (Table 1). As can be seen from Table 1, the number of food packaging patents has significantly increased from 2004 to 2008, moving from 871 to 1048 international patents and from 13 to 20 Italian patents.

Table 1: Trend of International and Italian food packaging patents (source: http://ep.espacenet.com).

Year 2004 2005 2006 2007 2008 International patents 871 884 940 880 1048 Italian patents 13 12 10 20 20

4.2 The company

The company examined in the case study is EasyPack Solutions Srl (hereafter referred to as EasyPack). With respect to the classification of the European Commission (2003), the company is a small enterprise, founded in 1985 in San Giovanni in Persiceto, in Northern Italy. EasyPack started its activity operating as a manufacturer of packaging machinery for the food industry, and especially of Form-Fill-Seal machines; in recent years, the company extended its activity to the production of packaging solutions for food products, manufacturing small 3-side seal or 4-side seal sachets. EasyPack offers to its clients not only a mechanical system but the whole process, including the packaging materials, in order to fill all food products assigned to the single-dose sector.

Besides the food industry, products manufactured are commercialized in the chemical, cosmetic and pharmaceutical markets, both in Italy and abroad. The company uses a network of sales agents located throughout the world, and controls four subsidiaries in Spain, Korea, South Africa and North America. 4.3 The product analysed

The product examined in this study is an innovative single-dose packaging, called Easysnap. Easysnap is a single-dose sachet intended to replace the 4-side or 3-side seal packages, which are commonly used when packaging small contents of food fluids (from 2 to 25 ml). Examples of products packed in 4-side or 3-side seal sachets are mayonnaise and mustard among foods, or shampoo or shower gel among cosmetics.

The innovation in the product developed primarily consists in the functional characteristics of new packaging. The package is, in fact, extremely easy to use, because the opening mechanism allows the sachet to be opened with one hand by the user. The package should be taken by the user with one hand and bent, until a micro-fracture of the plastic material is generated. The subsequent pressure by the user involves an extension of the fracture, as well as the complete breaking and consequent product flow. As a function of the pressure he applies, the user is also able to precisely control the amount of product flowing out, avoiding dirty. As regards the organoleptic properties of the content, the product is packaged in the absence of air, and thus benefits from a long shelf life.

To protect the innovative opening mechanism, Easysnap was recently patented. In fact, the opening mechanism requires accurate evaluation and design of the thickness of the plastic material, which makes it possible to open the package as a result of pressure applied by the user. Easysnap thus differs from technological solutions available on the market in that the package is not “cut”, as in many other types of packaging; rather, a cavity is generated, exploiting the elastic properties of the packaging material. This solution has some advantages. First, it allows keeping intact the plastic material, which provides higher protection to the product; moreover, such a solution also avoids non-controlled flow of product.

EasyPack also engineered proper automated machines for the production of the new packaging; such machines were designed in order to manufacture a sachet with internal and external grooves, whose depth depends on the density of the product packed. Specific customer’s requirements could also affect the grooves’ depth: for example, the customer may require that a viscous product flows out all together, but only when the sachet has been completely opened. The automated packaging machines were also patented. It will be explained in the next section that patents are crucial for new packaging solutions; conversely, the relevance


18

of patenting the machine is lower, and does not fully protect the innovation. As a matter of fact, it is always possible that a competitor finds a different technical solution to achieve a similar industrial output (EasyPack itself designed different machines, besides the one patented, that can be used to produce Easysnap). On the other hand, patenting the machine could be a “barrier” for competitors of EasyPack, as it prevents them to build machines that operate with the same procedure as those patented.

4.4 The steps from “concept” to “patent”

As mentioned, a typical NPD process starts with the identification of an opportunity in the market and ends with the successful launch of the product on the market. According to the findings obtained from the case study, the NPD process of Easysnap consists of 5 steps (Figure 2), namely:

i. identification of the market need and generation of the “idea” of the product; ii. development of the technological solution;

iii. pre-testing of the product with selected customers; iv. innovation protection through Italian patents; v. innovation protection through international patents. The production process for Easysnap partially overlaps the NPD process, as it consists of:

i. identification of the market need and generation of the “idea” of the product; ii. development of the technological solution;

iii. pre-testing of the product with selected customers; iv. product manufacturing and launch in the food packaging market; v. expansion in different markets.

Figure 2. New product development process and production process of Easysnap.

The starting point for the NPD process was the generation of the “idea” of the product (step 1). As mentioned, EasyPack started its activity as a manufacturer of automatic packaging machines, for 4-side or 3-side seal packages, and, in this context, the company achieved a good market share and was able to expand its production in foreign markets (e.g., China). On the basis of the success gained in the packaging machinery industry, the company decided to enter the product packaging market, where it faced numerous competitors. The idea of the new product was generated starting from the analysis of the packaging systems manufactured by competitors, as well as from the investigation of the main limits of single-dose sachets, both 4-side or 3-side seal, commonly used for packaging food and pharmaceutical products. Hence, the idea of the new product has not been a real “intuition”; rather, it was generated as a result of marketing research of the company, as well as of the analysis of different packaging solutions currently available for food products. The main limit the company found for single-dose sachets was the need for the user to use both hands when opening the package. Of course there is the need for breaking the sachet, to open it; nonetheless, the company developed the idea of a single-dose sachet that could be opened using only one hand, by means of a natural hand movement.

Once the idea was generated, the company started the development of the technological solution to engineer it (step 2). More precisely, the company originally pondered several possible solutions. For instance, one possible technological solution was

Partnership with patent consultants

Partnership with patent consultants

4a - innovation protection through Italian patents

5a - innovation protection through international patent (PCT)

4b - product manufacturing and launch in the food packaging market

5b - expansion in different markets

Limits of the single-dose packaging

Partnership with raw material supplier

Partnership with raw material supplier

Analysis of the competitors R&D activities and project supported by Italian and European centers

1 - identification of the market need and generation of the “idea” of the product

2 - development of the technological solution

3 - pre-testing of the product with selected customers

Production process

NPD process


19

grounded on the application of a supplementary element inside the sachet, which could be used to break the packaging though the hand movement. This solution was not engineered, however, due to the fact that adding a supplementary element would have increased the cost of the packaging machines, and the complexity of the packaging process as well. After a preliminary screening, several technical solutions were dropped, due to either technical limitations, difficulties in industrializing them or limited economical profits. The company finally decided to focus its attention on the development of a technical solution based on a special kind of plastic film, whose structure allowed the internal and external breaking of the sachet, thanks to the hand movement by the user. To engineer such solution, the company developed a partnership with a supplier, operating as a plastic manufacturer, which was directly involved in the NPD process. The partnership allowed EasyPack to benefit from the considerable know-how of the supplier in the field of plastic materials, as well as to save significant money (about 50%) in the purchase of raw materials for manufacturing activities. At the same time, the partnership with the supplier also had some shortcomings. For instance, the chosen partner is a big company, with limited flexibility: as a matter of fact, the supplier required quite long time (approx 6 months) to fulfill an order received from EasyPack. Obviously, the limited flexibility of the supplier in turn affects the capability of EasyPack to provide the final customers with the product in a relatively short time.

After the engineering of the technological solution, the product underwent a pre-testing with selected customers, by which it was proposed to some customers at Italian and European trade fairs (step 3). At this stage, the product proposed was still in a prototypical form: for instance, the plastic material used was different from that currently adopted, and, as a consequence, the manufacturing process could not be fully automated. This also resulted in scarce precision when opening the single-dose sachet, and to unexpected breaking as well. Despite the limits of the prototype, the pre-testing phase played a crucial role for the company to collect the voice of the customer as regards to the new product, as well as to identify the need for modifications to be made to the product in response to further customer’s needs. At the same time, pre-testing also allowed the company to start the presentation of the new product to potential customers. On the basis of the interest raised by the new product, the company proposed it to additional customers. To enhance the attractiveness of the product, the company paid particular attention to differentiate it from the products offered by competitors. Specifically, the company undertook considerable efforts in research and development (R&D), to obtain an innovative plastic material which could preserve the product for 24 months instead of 12 months, thus providing unique and tangible customer’s benefits. Due to the strategic role of raw materials in achieving this aim, R&D activities were still supported by the partnership with the supplier of plastic materials. Moreover, specific R&D activities and projects (aimed, for instance, to assess the preservation and the organoleptic properties of the product packaged) were carried out thanks to the collaboration with research centers, such as Italian universities and foreign research centers.

As regards to the NPD process, before starting the launch of the new product, the company submitted an application for an Italian patent, covering the technical features of the new product (step 4a). The aim of the patent was to protect the intellectual property of the innovative solution, and, in particular, the opening mechanism of the sachet. Moreover, the Italian patent was required before starting the large-scale production of the new product: in fact, in the food packaging industry, protecting the technological solution is crucial, since the significant cost for engineering it and manufacturing the new product can be paid back only through exclusive use of the technological innovation. In this phase, as well as during the application for the international patent, the company was supported by patent consultants. At the time EasyPack applied for the Italian patent (late 2006), there was no need for the Italian patent office to check the technical contents of the patent. That is, the contents of a new patent application were assumed as valid and innovative, until otherwise proven, meaning that any other company could prove the inadequacy of the patent contents within 24 months from the submission. The underlying rational for this procedure is that, before applying for a patent, a company should personally check the national patents databases, to ensure that the solution developed is really innovative1. The guidance of patent consultants was fundamental for EasyPack to ensure that such checks are accurate and extensive.

As mentioned, in the context of food packaging, the Italian patent is often a prerequisite to start the large-scale production of the new product and its launch on the market. It should also be noted that, for the company examined, the application for the Italian patent did not delayed the launch of the product on the market, as the patent application was submitted before starting the large-scale manufacturing of the product. Overall, the time to market of Easysnap (i.e., the time elapsed from the generation of the “idea” of the new product and the first placement on the market) was about 18 months, which is a relatively short time compared to the typical time to market of a new product. This leads to obvious advantages for the company, as the reduction of NPD time creates opportunities for gaining market share, increasing profits, and ensuring long-term competitiveness (Ittner and Larcker, 1997; Griffin, 1997). At the same time, the choice to start the large-scale production of the new product as soon as the application for the Italian patent was submitted entails obvious risks. In fact, it is clear that the submission for a patent does not ensure the success of the application: due to the lack of checks on the technical contents of the patent at the time an application is submitted, any competitor could prove the inadequacy of the patent pending, nullifying the economical investments made by a company.

The international patent application was submitted by EasyPack to extend the intellectual property of the technological solution at international level (step 5a). The international patent chosen by EasyPack is the Patent Cooperation Treaty (PCT). Submitting an

1 It has to be mentioned that the procedure described has changed in late 2008. In particular, the Italian patent office now needs to check the innovative contents of any patent, as well as the contents of similar previous patents, before accepting any application for an Italian patent (Ufficio Italiano Brevetti e Marchi, 2008).


20

application for a PCT patent involves significant costs, as it requires a detailed examination of the technical contents of the patent, to ensure its innovation; moreover, the analysis should be extended to all countries where the PCT should be recognized as valid. At the time of writing, PCT embraces a total of 142 countries (World Intellectual Property Organization, 2006). This is why the company submitted the PCT application two years after the Italian patent (late 2008). After the international application, EasyPack could start manufacturing and selling the new product in foreign countries, generating considerable revenues from sales and covering part of the costs incurred for the development of Easysnap. Finally, in 2009, the company has applied for worldwide patent.

As mentioned, in parallel with the Italian patent application, the company started product manufacturing and launch in the Italian food packaging market (step 4b). It is well known that adequately planning the launch on the market is a relevant point to ensure the success of the new product (Ionescu and Stancu, 2007). In the case in exam, the launch phase of Easysnap was supported by the recruitment of several new staff units, which acted as project managers in carrying out and monitoring the launch of the product on the market. More precisely, the company embodied a chemical engineer, some lawyers and an industrial engineer in its staff. The chemical engineer was a past employee of the supplier of plastic material, partner of EasyPack, and operated in the R&D function; currently, he is in charge for the procurement of plastic materials, meaning that he still has close relationships with the partner of EasyPack. The lawyers were embodied in the company’s staff with the purpose of taking care of bureaucracy associated with patents and certifications related to new products. Finally, the industrial engineer was included in the staff of EasyPack with the purpose of supervising the start-up of a company’s subsidiary, in Spain, and the bureaucracy associated.

The last step of the production process of Easysnap was the expansion in different markets (step 5b). This phase was supported by a strategic partnership with a company operating in the pharmaceutical sector; the partnership allowed EasyPack to enter this market field, as well as similar contexts (e.g., health, beauty care and cosmetics sectors). To enter such markets, the company started by manufacturing products for third parties, with a limited annual production. Currently, the Italian production plant of the company manufactures between 8 and 9 million single-dose sachets per year, which are sold in the food market. For the same market, about 10 million sachets per year are manufactured in the production plant in South Africa. The market penetration of EasyPack in the cosmetics, health care and pharmaceutical fields is lower, due to the more recent entry into those markets. 4.5 Economic analysis

Recouping the investments in NPD is crucial for ensuring company’s competitiveness and generating future revenue from the new products. For this reason, we have conducted an economic analysis of the NPD process of Easysnap, following the main phases of the process, as described in the section above. The analysis is based on a 4-year horizon, which approximately covers the years from 2006 to 2009. For each year, we propose a percentage sharing of the investments required in the different steps of the NPD process, as shown in Figure 3.

For confidentiality reasons, we are not allowed to provide the detailed economic figures, such as investments, costs and revenues, of the company investigated; hence, all economic figures are expressed as percentage values with respect to the company turnover of each year. The economical analysis in Figure 3 shows that the steps 1-3 of the NPD process absorb most of the company’s turnover during the first year; this indicates that the initial activities of the NPD requires significant financial efforts. Similarly, the development of the technological solution (step 2) still requires relevant investments during year 2.

Conversely, during the last years, innovation protection mechanisms become the activities which require the most significant efforts. This confirms the relevance of innovation protection for the context examined and for the technological solution developed. Finally, from the total cost shown in Figure 3, it can be deduced that the NPD did not generate revenues during years 1-2. In fact, the cost of the NPD process covers the overall amount of the annual turnover of the company, meaning that the turnover is entirely reinvested. Conversely, in years 3-4, the total cost of the NPD process is lower than 100% (approx 80% during year 3 and 50% during year 4), indicating that the turnover of the company has significantly increased. Eventually, this suggests that the NPD process started generating positive revenues from year 3 onwards.


21

Steps 1-2

Step 3

Steps 4a-5a

Steps 4b-5b

Year 1 Year 2 Year 3 Year 4

Total cost

Figure 3. Economic analysis for the new product development process.

5. Conclusions

Responsive and timely NPD has become even more critical in the today’s highly competitive global environment. The need to respond quickly to these dynamic forces requires every company to integrate rapidly the perspectives and needs of product developers as well as of the customer. As for the food industry, such context has seen significant advances in the packaging sector, with the development of active and intelligent innovations. These advances have led to improved food quality and safety. While some innovations have stemmed from unexpected sources, most have been driven by changing consumer preferences or specific customer’s requirements.

The issue discussed in this article was the NPD process and those factors that determine its success within a food packaging SMEs, where still little attention has been paid to this matter. In particular, we have attempted to provide a framework for the NPD process, grounding on the literature on the traditional NPD process and on the analysis of a real-life experience. Specifically, with this purpose in mind, the paper has explored a successful industrial NPD case, in order to present the lessons from the real scenario. First, the paper has illustrated the variety of interpretations that the concept of NPD, and of NPD success in particular, receives. Second, by exploring the case history, the paper has drawn a picture of the 5-step NPD process, as well as it identifies the background successful factors present in the case.

From the analysis of the case study, it can be argued that the success of the NPD process of the company examined was grounded on some main strong points, shown in Figure 3 and described below.

i. Differentiation. The company has paid particular attention to increase the attractiveness of the new product in comparison with the product manufactured by the competitors. The generation of the idea, in particular, was focused on the analysis of the competitors, to identify the limits of their products and propose an innovative solution. Similarly, extensive R&D


22

activities were accomplished by the company during the pre-testing of the product, to develop a plastic material which could improve the preservation of the package contents for 24 months. By means of these activities, the company was able to deliver the customers unique and tangible benefits, increasing the perceived value of the product;

ii. Voice of the customer. The pre-testing was the phase which allowed the company to collect the opinions of the customers as regard to the new product, as well as to improve its technical characteristics in response to customer’s needs;

iii. Market launch. The launch of the new product was accurately planned by the company, and several resources were spent in this phase. Specifically, the company has coupled the launch of the new product with innovation protection mechanisms, to protect the product innovation; moreover, the launch of the new product was supported by a cross-function team, involving four new staff units. The team members acted as project managers in promoting the new product and monitoring its launch on the market;

iv. Innovation protection mechanisms. The company examined has chosen to protect the product innovation with both national and international patents. Innovation protection is not always included in a typical NPD process. In fact, innovation protection, particularly at international level, often involves significant cost, which could prevent a company to undertake this step. Instead, in the case in exam, the significant efforts of the company toward innovation protection have been crucial for the successful launch of the new product on the market;

v. Reduced time to market. A short time to market is particularly relevant in the food context (and especially in the food packaging industry), as the food market increasingly requires new products and new packaging solutions. In the case examined, the company was able to limit the overall time to market of the new product to about 18 months, which is relatively short compared with typical NPD processes. This involved significant benefits for the company, such as opportunities for gaining market share, increased profits, and long-term market competitiveness.

Successfulnewproductdevelopment

process

Innovationprotection

mechanisms

Reducedtime tomarket

Market launch

Voice of the customer

Differentiation

Figure 3. Critical success factors of new product development for the case study. The above factors provide useful guidelines and indications for a successful NPD process in the food industry. We thus believe

that investigating innovation mechanisms in the food context can provide interesting insights for practitioners in other different fields.

At the same time, we acknowledge that our results cannot be generalised to other contexts: our study, in fact, is focalized on a specific industry, while different sectors might have a different approach to the NPD process and patenting activities. These limitations will be carefully considered and will hopefully inform future research. Moreover, as we have examined a case study referring to a SME, the NPD process we defined could not be suitable for application, for instance, in a big company. Hence, the logical prosecution of this work is to define a more general model for NPD processes in the context of the food industry. This could be done, for instance, by multiple case studies and cross-analyses between them. References Abraham, B.P. and Moitra, S.D. 2001. Innovation assessment through patent analysis, Technovation, Vol. 21, pp. 245-252. Baker W.E. and Sinkula J.M. 1999. The synergistic effect of market orientation and learning orientation on organizational

performance, Journal of Academy of Marketing Science, Vol. 27, No. 4, pp. 411-427. Balkin D.B., Markaman G.D. and Gòmez-Mejìa L.R. 2000. Is CEO pay in high-technology firms related to innovation?, Academy

of Management Journal, Vol. 43, No. 6, pp. 1118-1129. Calantone R.J., Vickery S.K. and Droge C. 1995. Business performance and strategic new product development activities: an

empirical investigation, Journal of Product Innovation Management, Vol. 12, pp. 214-223.


23

Clarysse B., Van Dierdonck R., Gabriels W., Lambrechts J. and Uytterhaegen M. 1998. Strategische verschillen tussen innovative KMOs: een kijkje in de zwarte doos. Publication No. 5, IWT, Brussels.

Cooper R.G. 1999. The invisible success factors in product innovation, Journal of Product Innovation Management, Vol. 16, pp. 115-133.

Cooper R.G. 2001. Winning at new product: accelerating the process from idea to launch. New York, Perseus Publishing. Damanpour F. and Gopalakrishnan S. 2001. The Dynamics of the Adoption of Product and Process Innovations in Organizations,

Journal of Management Studies, Vol. 38, No. 1, pp. 45-65. Darroch J. and McNaugton R. 2002. Examining the link between knowledge management practices and types of innovation,

Journal of Intellectual Capital, Vol. 3, No. 3, pp. 210-222. Drucker P. 1985. Innovation and Entrepreneurship, Harper & Row, New York, NY. European Commission 2003. Commission Recommendation 2003/361/EC of 6 May 2003 concerning the definition of micro,

small and medium-sized enterprises. Official Journal L124 of 20.05.2003 Garcia R. and Calantone R. 2002. A critical look at technological innovation typology and innovativeness terminology: a literature

review, The Journal of Product Innovation Management, Vol. 19, pp. 110-132. Hanan M. 1970. Effective coordination of marketing with research and development, Handbook of modern marketing, Victor

Buell (ed.), McGraw-Hill. Ionescu F.T. and Stancu A. 2007. Key success factors in new product development process. The Journal of the Faculty of

Economics - Economic Science Series, Vol. 1, pp. 984-986 IRI 2000. R&D facts 2000, Industrial Research Institute, Washington, DC. Jaspers F. 2007. Case study research: some other applications besides theory building. Journal of Purchasing & Supply

Management, Vol. 13, pp. 210–212. Jimenez J., Valle R.S. and Espallardo M.H. 2008. Fostering innovation. The role of market orientation and organizational learning,

European Journal of innovation management, Vol. 11 No. 3, pp. 389-412. Jokioinen I. and Suomala P. 2006. Lessons learned from industrial success stories, European Journal of Innovation Management,

Vol. 9, No. 4, pp. 370-395. Klompmaker J.E., Hughes G. and Haley R. 1976. Test marketing in new product development, Harvard Business Review, Vol. 54,

pp.128-38. Lee D.S., Yam, K.L., Piergiovanni, L. 2008. Food Packaging Science and Technology. Boca Raton (USA): CRC Press –

Taylor&Francis Lundvall B.A. 1992. National Systems of Innovation: towards a theory of innovation and interactive learning, Frances Printer,

London. Lyon D. and Ferrier W. 2002. Enhancing performance with product-market innovation: the influence of the top management team,

Journal of Managerial Issues, Vol. 14, No. 14, pp. 452-469. Mahalik N.P. and Nambiara A.N. 2010. Trends in food packaging and manufacturing systems and technology, Trends in Food

Science & Technology, Vol. 21, No. 3, pp. 117-128. McGuire E.P. 1973. Evaluating new product proposals, The conference board report, No. 604. Millward H. And Lewis A. 2005. Barriers to successful new product development within small manufacturing companies, Journal

of Small Business and Enterprise Development, Vol. 12, No. 3, pp. 379-394. Montoya-Weiss M.M. and Calantone R.J. 1994. Determinants of new product performance: a review and meta-analysis, Journal of

Product Innovation Management, Vol. 11, pp. 397–417. Moreira A.C. 2005. Supplier-buyer collaboration in new product development: four case studies involving SMEs. Brazilian

Journal of Operations & Production Management, Vol. 2, No. 1, pp. 5-24. Moskowitz H. and Hartmann J. 2008.Consumer research: creating a solid base for innovative strategies. Trends in Food Science &

Technology, Vol. 19, No. 11, pp 581-589. Peychès-Bach A., Moutounet M., Peyron S. and Chalier P. 2009. Factors determining the transport coefficients of aroma

compounds through polyethylene films. Journal of Food Engineering, Vol. 95, No. 1, pp. 45-53. Rothwell R., Freeman C., Jervis P., Robertson A. and Townsend J. 1974. Sappho updated—Project SAPPHO Phase 2, Research

Policy, Vol. 3, pp. 258–291. Scarborough N. and Zimmerer T. 2002. Essentials of Entrepreneurship and Small Business Management, Paperback. Shenhar A.J., Tishler A., Dvir D., Lipovetsky S. and Lechter T. 2002. Refining the search for project success factors: a

multivariate, typological approach, R&D Management, Vol. 32, No. 2, pp. 111-26. Sheperd C. and Ahmed P.K. 2000. From product innovation to solutions innovation: a new paradigm for competitive advantage,

European Journal of Innovation Management, Vol. 3, No. 2, pp. 100-106. Siegrist M. 2008. Factors influencing public acceptance of innovative food technologies and products. Trends in Food Science &

Technology, Vol. 19, No. 11, pp 603-608. Thomas, V.J., Sharma, S. and Jain, S.K. 2010. Using patents and publications to assess R&D efficiency in the states of the USA,

World Patent Information, forthcoming.


24

Tidd J., Bessant J. and Pavit K. 1997. Managing Innovation: Integrating technological, market and organizational change, John Wiley & Sons Ltd, Chichester.

Ufficio Italiano Brevetti e Marchi, 2008. Istruzioni deposito domanda brevetto nazionale. Available at http://www.uibm.gov.it/it/Incontro%20Brevetti-PI-5.pdf (accessed June 2010).

Wheelwright S.C. and Clark K.M. 1992. Revolutionary product development, The Free Press, New York, NY. World Intellectual Property Organization 2006. Protecting your inventions abroad: frequently asked questions about the Patent

Cooperation Treaty (PCT). Available at http://www.wipo.int/export/sites/www/pct/en/basic_facts/faqs_about_the_pct.pdf (accessed June 2010).

Yin, R.K. 2009. Case Study Research: Design and Methods. Fourth Edition. SAGE Publications. California. Biographical notes Dr. Barbara Bigliardi graduated (with distinction) in 2004 in Industrial Engineering and Management at the University of Parma and holds a Ph.D. in Industrial Engineering from the same University in 2008, with a thesis on the technological innovation management in the food machinery industry. Since 2005 she has been researcher at the Department of Industrial Engineering of the same University. She is currently teaching “Business administration” at the degree courses in Industrial Engineering and Management and Mechanical Engineering, and “Economics and Corporate Organizations A” at the degree courses in Information Engineering (Computer Engineering, Electronic Engineering, and Communication Engineering) at University of Parma. She is also strongly involved in Executive Education running seminars for public and private organisations. Her primary research interests focus on innovation, human resources management, performance management systems, knowledge asset & intellectual capital management and entrepreneurship. She has authored or co-authored many publications, including articles and research reports on the range of research topics listed above. Her research has been published on international journal, as well as on national and international conferences. She is also referee for international scientific journals. Dr. Eleonora Bottani graduated (with distinction) in 2002 in Industrial Engineering and Management at the University of Parma (Italy), where she is lecturer (with tenure) in Mechanical Industrial Plants since 2005. In March 2006, she got her Ph.D. in Industrial Engineering at the same University. She taught “Supply Chain Management” to the Industrial Engineering and Management class, and, at present, she teaches “Logistics for the Food Industry” to the Mechanical Engineering for the Food Industry class. Her research activities concern logistics and supply chain management issues, and encompass intermodal transportation, development of methodologies for supplier selection, analysis and optimization of supply chains, supply chain agility, supply chain modeling and performance analysis. As secondary research topics, she studies industrial plants, with a particular attention to process optimization and plant safety. Recently, she started studying the use of RFID technology as a tool for the optimization of logistics processes and supply chain dynamics. Results of her studies related to the above topics have been published in about 60 scientific papers, most of which appear in national and international journals, as well as in national and international conferences. She acts as a referee for about 40 international scientific journals and for several international conferences. She is editorial board member of two international scientific journals. Prof. Roberto Montanari graduated (with distinction) in 1999 in Mechanical Engineering at the University of Parma (Italy), where he is employed as Associate Professor since 2005, teaching “Industrial Plants” to the Industrial Engineering and Management class. From 2001 to 2005, he worked as a lecturer at the Faculty of Agriculture of the same University, where he taught “Food processing plants” and “Machines and Equipments of the Food industry” to the Food Science class. In 2003, he was visiting professor at the New Jersey Institute of Technology (USA) where he spent 7 months for research purpose. His research activities concern equipment maintenance, power plants, food plants, logistics, supply chain management, supply chain modeling and simulation, inventory management. He has published his research in several qualified international journals, as well as in national and international conferences. He acts as a referee for numerous scientific journals such as European Journal of Operational Research; International Journal of Systems Science; Computers & Industrial Engineering; Chemical Product and Process Modeling; International Journal of Food Science and Technology; and International Journal of Quality and Reliability Management. He is editorial board member of two international scientific journals. Dr. Giuseppe Vignali graduated in 2004 in Mechanical Engineering at the University of Parma. In 2009, he held a PhD in Industrial Engineering at the same university, related to the analysis and optimisation of food processes. Since August 2007, he works as a Lecturer at the Department of Industrial Engineering of the University of Parma, and, since the employment at the University, he has been teaching “Materials, Technologies and Equipments for Food Packaging” to the Food Industry Engineering class. His research activities concern food processing and packaging issues and safety/security of industrial plant. Results of his studies related to the above topics have been published in more than 20 scientific papers, some of which appear both in national and international journals (e.g., Safety Science, Chemical product and process modelling, Prevention today, Food Manufacturing Efficiency), as well in national and international conferences. He acts also as a referee for an International Journal: Prevention Today. Received June 2010 Accepted November 2010 Final acceptance in revised form November 2010

MultiCraft


Vol. 2, No. 9, 2010, pp. 25-38

INTERNATIONAL

JOURNAL OF

ENGINEERING,

SCIENCE AND

TECHNOLOGY

www.ijest-ng.com

2010 MultiCraft Limited. All rights reserved

A multiple choice decision analysis: an integrated QFD – AHP model for the

assessment of customer needs

F. De Felice

1, A. Petrillo

2*

1Department of Mechanism, Structures and Environment, University of Cassino, Faculty of Engineering, ITALY 2* Department of Mechanism, Structures and Environment, University of Cassino, Faculty of Engineering, ITALY

*Corresponding Author: e-mail: [email protected], Tel +39-0776-2994350, Fax.+39-0776-2994349

Abstract

The aim of this work is to propose a new methodological approach to define customer specifications through the employment

of an integrated Quality Function Deployment (QFD) – Analytic Hierarchy Process (AHP) model. The model, which is loosely

based on QFD, incorporates the AHP approach to delineate and rank the relative importance weight of expressed judgments for

customer needs and functional characteristics. The Analytic Hierarchy Process is very useful for this aim because it is a

mathematically rigorous, proven process for prioritization and decision-making. By reducing complex decisions to a series of

pair-wise comparisons, then synthesizing the results, decision-makers arrive at the best decision with a clear rationale for that

decision. The methodology adopted in this work is directed to evaluate as well as rank the definition of the customer’s needs and

functional characteristics among several alternatives. The approach has been validated in a real case study concerning the filter

in ceramic material production.

Keywords: Quality Function Deployment, Analytic Hierarchy Process, Multi Criteria Decision Analysis. 1. Introduction

The aim of this study is to propose a new methodological approach to state the functional characteristics of a ceramic filter in

order that it might be competitive as regards performance and price. Owing to this aim, the application of the QFD-AHP seemed to

be a very good approach to improve the definition of the customers’ needs during the planning phase. Once established a

consistent and finite set of functions it is essential to define a hierarchy of importance to determine the criticality of each one,

proportional to the function for the customer. We therefore defined an algorithm that, by direct comparison of functions can define

a prioritized list that is objective, scientific and unique. In this, AHP is very useful because it is an algorithm that helps to solve

decision problems such as MCDA - Multiple Choice Decision Analysis (Saaty, 2005). There are many MCDA methods that have

been developed such as ELECTRE, TOPSIS, AHP, etc., but these methods do not consider the interdependence among criteria and

alternatives (Lin et al., 2008). Contrarily to other methods, AHP, given a number n of functions, allows to define the importance

for customers through a direct and objective value of each function and of all the others. This occurs within a matrix of assessment

in which the functions appear on both axes.

We note that the risk usually involved in industrial programmes is not always caused by the possibility of technical unsuccess,

but by the probability of obtaining a result that, although scientifically valid, might not be worthwhile for the customer (De Felice,

Petrillo, 2009). Thus, the new methodological approach allows to pilot effectively all the variables concurring in the process of

generation of the ―value‖ of the product, so as to obtain both the customer’s approval and the control of costs. The QFD – AHP

method is very flexible and allows to analyse any customer’s requirement with impartiality and effectiveness. In particular, it

permits to identify the customer’s actual ―needs‖ and to focus the technical activity on the output much more in demand.

Therefore, it is possible to establish the priority characteristics of the output on an objective basis. As a consequence, the operating

units taking part in the project can firstly identify the technical strategies for the achievement of each output, and afterwards assess

the needs and thus plan the activities. At the end of our study the company set up an instrument permitting it to take part, with its

home and foreign customers, in the development of the products with the consequent reduction in times and costs. The paper is

De Felice et al./ International Journal of Engineering, Science and Technology, Vol. 2, No. 9, 2010, pp. 25-38

26

organized as follows: in section 2, the survey of existing literature about integration on QFD – AHP is analyzed, the process

production of a ceramic filter is described in section 3, QFD and AHP theoretical basis are analyzed in section 4. Finally the model

and the case study are proposed in section 5.

2. Survey of existing literature about integration on QFD - AHP

Due to its wide applicability and ease of use, the Analytic Hierarchy Process (AHP) has been studied extensively for the last 20

years. Recently, it has been observed that focus has been confined to the applications of the integrated AHPs rather than the stand-

alone AHP (Ho, 2008). The five tools that are commonly combined with the AHP include mathematical programming, quality

function deployment (QFD), meta-heuristics, SWOT analysis, and data envelopment analysis (DEA).

In our study we would like to focus attention on QFD-AHP integration. QFD determines product design specifications (hows)

based on customer needs (whats) and competitive analysis (whys), which represent a customer-driven and market-oriented process

for decision-making. It is quite natural to use QFD in this field for purposes such as determining customer needs and development

priorities. Essentially, QFD has been widely applied to the major aspects of decision-making: measurement,

selection/determination, and evaluation. In particular, we analyzed some works during 1998-2009. Major applications concern:

Higher education sectors, Manufacturing sectors, Military sectors, Logistics sectors, Sports sectors with particular attention to:

Education requirement selection, Teaching method selection, Product design selection, Capital budgeting project selection,

Peacekeeping force composition selection, Multi-functional team selection, Facility location selection, Game rule selection, Robot

selection, Rapid tooling process selection, Tourism, Competitive benchmarking (Köksal and Eğitman, Lam and Zhao, 1998;

Partovi, 1999; Chuang, 2001; Hsiao, Kwong and Bai, 2002; Bhattacharya et al., 2005; Hanumaiah et al., 2006; Dan et al. 2007; Lin

et. al., 2008; Li et al., 2009). Recently, also Ho (2008) proposed that the combination of AHP and QFD is one of the most

commonly used techniques to deal with incomplete and imprecise information in customer requirements.

In Table 1 a synthetic report about major applications is shown. Of course analysis is not exhaustive, but it is representative.

Table 1. Synthetic report about major applications on QFD-AHP

Year Author/s Applications

1998 Köksal and Eğitman

Lam and Zhao

Köksal and Eğitman applied the combined AHP–QFD approach to improve the

education quality and to identify appropriate teaching techniques. The

alternative teaching techniques were prioritized based on the AHP weightings

and the relationship between students’ requirements and teaching techniques.

1999 Partovi Applied the combined AHP–QFD approach to aid the project selection. The

author used the AHP to quantify the strength of the relationships between rows

(e.g., customer requirements) and columns (e.g., design specifications), instead

of evaluate the relative importance weightings of decision alternatives.

2001 Chuang Applied the combined AHP–QFD approach to deal with the facility location

problem. The AHP was applied again to determine the relative importance

weightings of alternative locations with respect to each evaluating criterion. A

location with the total highest score was selected.

2002 Hsiao

Kwong and Bai

Used the combined AHP–QFD approach to aid the new product development.

The AHP was used to obtain the relative importance weightings of the criteria.

2005 Bhattacharya et al. Applied the combined AHP–QFD approach to aid the robot selection. The AHP

was adopted to evaluate the relative importance weighting of each robot based

on the technical requirements. The robot with the highest score was selected.

2006 Hanumaiah et al. Presented the combined AHP–QFD approach to deal with the rapid tooling

process selection. The AHP was adopted to determine the relative importance

weightings of the tooling or customer requirements while considering

constraints, such as material, geometric features, die material, and production

quantity.

2007 Das et al. Developed an AHP-QFD framework for designing a tourism product, which

takes care of the touristic needs of tourists.

2008 Lin et al. Evaluated the relative overall importance of customer requirements and design

characteristics.

2009 Li et al. Combining rough set theory, Kano’s model, analytical hierarchy process (AHP),

and scale method, an integrated method is proposed to obtain the final

importance of customer requirements (CRs) in product planning house of

quality (PPHOQ).


27

Some papers (Partovi, 1999) have applied the ANP combined with the QFD to handle MCDM problems, but they do not entirely

utilize the AHP in their models. Our proposed method completely utilizes all natures of the AHP to make all paired comparisons

based on Saaty’s nine-point scale to evaluate the customer’s needs and functional characteristics among several alternatives.

The use of AHP is favorable because the AHP provides an effectual method to cope with complex MCDM matters. The AHP

has advantages such as: it handles human intuitive judgment by making paired comparisons with a ratio scale. In other words, the

AHP adds benefits in that it can capture priorities using natural language comparisons and converts them into ratio scale numbers.

3. Filters in ceramic material: Process description

The filter market is divided into sections defined by the different functions of the use of the product (the filters, in fact, can be

used: to catalyze, to filter aluminium alloys or treat them from fused to filter metals, to filter air at high temperatures, as insulators

for high temperatures, as heat exchangers, in fire-blockings, in water treatments, etc) and by various types of users (De Felice,

Falcone, 1999). Within such a variegated framework the company has focused on ceramic foam filters, because, compared to the

traditional ones, they allow to obviate some disadvantages, such as:

Encumbrance.

High energy consumption.

Insufficient operating flexibility.

Limited productivity due to the dead times of change of the filter bed.

The filtering system suggested by the company employs a ceramic foam as filtering element with a continuous net of pores. It is a

structure showing the following characteristics:

Resistance to high temperatures.

Low specific weight.

Resistance to chemical attacks and winding flows.

High porosity.

Thanks to these properties, the ceramic foam with open pores can satisfy various types of demands for different uses. Besides, the

structure of the ceramic foam can be ―thin‖ or ―thick‖ according to the number of pores per linear inch.(p.p.i.). The factory

produces ceramic foams varying from a size of 10 pores per inch to 100 p.p.i. and over. The production technique of the ceramic

foam filters is simple. In fact, a polyurethane foam with open pores is used as a base of the filter. The foam is soaked with a watery

alumina pulp. The overplus of alumina is sqeezed so that the foam may result completely covered. With a drying process the water

is eliminated from the foam, which decomposes later, at the stated temperature, leaving a ceramic copy of the original organic

foam. The filter production process consists of:

1. Production of sheets of polyurethane.

2. Wash sponge.

3. Impasto preparation.

4. First dip sponge.

5. Preliminary drying.

6. Second dip sponge.

7. Second drying.

8. Cooking Filter.

9. Packaging.

4. Theoretical Basis: Quality Function Deployment and Analytic Hierarchy process

In this paper, we employ a combined QFD and AHP approach to develop an effective decision-making method to help make

better decisions for planning or evaluation problems (Chin et. al., 2009). Both QFD and AHP are the comprehensive decision-

making method that provides a means of coping with complex MCDM matters. The QFD is an overall concept that provides a

means of translating customer requirements into the appropriate technical requirements for each stage of product development and

production. The QFD methodology can be used for both tangible products and non-tangible services, including manufactured

goods, service industry, software products, IT projects, business process development, government, healthcare, environmental

initiatives, and many other applications. The AHP is a MCDM method used to derive relative priority from individual judgments,

which can deal with all kinds of interactions systematically (Chan et. al, 2002). In this section we will briefly explain the

traditional QFD and AHP framework.

4.1 Quality Function Deployment Approach: In this section we will briefly explain the traditional QFD framework as it is

commonly discussed in the quality management literature. The birth of QFD can be placed around 1972, when Takayanagi

Nishimura and his engineers presented a quality chart for a shipyard in Kobe in Japan. The aim of QFD is to translate the customer

requirements to the product requirement (Juran, 1993). In other words, QFD is a tool for transforming the ―Voice of customer‖ to


28

product design (Cohen, 1995). As experiment they used a matrix in which customer needs were reported in lines and methods to

satisfy them in columns. The idea was that the matrix must gradually fill on the basis of extensive discussions between Marketing

Manager, Design Manager and Production Manager. This collaboration is the fundamental difference between QFD and previous

methods. Two years later, Professor Yoji Akao founded and directed a Research Committee on QFD (Akao, 2003). He oversaw

the dissemination of QFD as a technique for improving the transition from design to production. We define the Quality Function

Deployment (QFD) tool for the development of products starting from customer needs, resulting in a systematic technical

specifications which are a guide to manufacturing activities. QFD translates customer requirements into appropriate specifications

within the company in each of its functional areas, from research and development to engineering, manufacturing, distribution,

sales and service. The logical process that underlies the development of new products with the QFD can be enclosed in a matrix, to

describe it briefly, we refer to the matrix of quality (one fundamental matrix of QFD) called House of Quality. This matrix has the

appearance of a tiled roof, hence the name ―house of quality‖(Chen, 2009).

According to Cohen (1995) and Han (2001), there are six stages of the hierarchical framework of QFD as follows (Figure 1):

1. Voice of customer – developing, categorizing and prioritizing customer requirements.

2. Competitive analysis – comparing the performances with competitors and set target levels for customer requirements.

3. Voice of organization – translating the voice of customers to the voice of organization.

4. Design Targets – specifying target values for design requirements and determining the project costs.

5. Relationship Matrix – evaluating impact of design requirements on customer requirements.

6. Correlation Matrix – specifying tradeoffs and selecting the appropriate design requirement.

The structure of the house of quality is outlined in the following figure (Figure 1). We note that in this matrix there are 6 different

areas.

C

Functional

characteristics

B

Benchmarking

D

Relationship MatrixA

Customer Needs

F

Numerical Values of

functional characteristics

(Quantity)

E

Correlation

Matrix

Figure 1. House of Quality

The motivation of planning house of quality is designing a product that embedded as well as possible the initial, potential

abstract, customer requirement (Chan & Wu, 2005; Ramanathan & Jiang, 2009). QFD improves quality while reducing costs, this

obviously makes it more competitive in business. It is created to solve three problems: inattention to the voice of the customer; loss

of information during the course of the product along the development cycle; different interpretations of the specifications by the

various departments involved.

In general, QFD facilitates organization: 1) understanding the actual requirements of customers, 2) prioritizing customer

requirements in order of importance from the customer’s point of view, 3) communicating among team members in order to ensure

decision making and reducing loss of data, 4) designing the products which meet or exceed customer requirements, 5) planning or

selecting the product design strategically (Han, 2000; Cohen, 1995).

4.2 Analytic Hierarchy Process Approach: AHP uses an interactive hierarchical structure for multi-objective decision-making

(MODM) developed by Saaty (Saaty, 1980). AHP is the one of the most systematic analytical techniques of MCDM within the


29

framework of operational research techniques that facilitates a rigorous definition of priorities and preferences of DMs. The AHP

consists of three main operations, including hierarchy construction, priority analysis and consistency verification.

The methodology combines both qualitative and quantitative approaches. In the qualitative sense, it decomposes an unstructured

problem into a systematic decision hierarchy. It then uses a quantitative way to employ pair-wise comparison to determine the

local and global priority weights and the overall ranking of the alternatives. The methodology quantifies the qualitative factors

with a scale called Saaty’s nine- point scale (see Table 2).

Table 2. The fundamental Scale proposed by Saaty

INTENSITY OF

IMPORTANCE aij

DEFINITION EXPLANATION

1 Equal Importance Two activities contribute equally to the objective

3 Moderate importance Experience and judgment slightly favor one activity over another

5 Strong importance Experience and judgment strongly favor one activity over

another

7 Very strong or

demonstrated importance

An activity is favored very strongly over another; its dominance

demonstrated in practice

9 Extreme importance The evidence favoring one activity over another is of the highest

possible order of affirmation

2,4,6,8 For compromise between

the above values

Sometimes one needs to interpolate a compromise judgment

numerically because there is no good word to describe it

After establishing pair-wise comparison, a pair-wise comparison matrix is established. The matrix is called A = (aij). This matrix

is constructed with respect to a particular property the elements have in common. It is reciprocal, that is, aij = 1/aij; There are n(n

−1) / 2 judgments required for a matrix of order n. We note that when you express judgments on comparisons in pairs inconsistent

judgments are formed, as the human mind has the inability to simultaneously take into account all the relations between the terms

of the comparison (Saaty, 2001). It is necessary to identify the degree of inconsistency that can be considered tolerable. In

mathematical terms, the verification of consistency is expressed through the calculation main eigenvalue λmax: if the value is n then

the matrix (of rank n) is consistent. More λmax is equal to the number n more consistent is the result. The deviation of the coherence

is shown with the index of consistency (I.C.):

I.C. = (λmax – n)/(n-1) < 0.10 (1)

where n is the number of components evaluated in the pairwise comparison matrix, and λmax is the largest eigenvalue

characterizing the previous matrix. When the calculated CR values exceed the threshold, it is an indication of inconsistent

judgment. In such cases, the decision makers would need to revise the original values in the pairwise comparison matrix. Finally, it

is necessary to aggregate the relative priorities of the decision elements to obtain an overall rating for decision alternatives. The

numerical analysis method is employed to calculate the eigenvalue vector and the maximized eigenvalue for an understanding of

the consistency established and the relative weight among elements.

5. Integration QFD – AHP: The model and case study

In order to work correctly to determine customer needs and design characteristics, an inter functional team was set up. It was

composed of 2 delegates from the 5 main departments of the firm (commercial, technical, production, quality and purchase

departments). The solutions are designed in groups by using morphological analysis and the brainstorming technique. This team

gave a description of the needs that a customer expects to satisfy through the purchase of the product of the firm. This procedure

was repeated for the different groups, who, after working autonomously for 4 weeks, confronted one another trying to agree upon a

definite list of the customer’s needs. After sharing the list, the groups were set up again, and together defined a pooled ranking for

the customer’s needs and functional characteristics with the AHP methodology (see step 1). In Figure 2 methodological steps of

the QFD-AHP approach are illustrated.


30

Definition of correlations

Definition of quantity

START

FINISH

Development of House of Quality

Team of Experts Definition

Benchmarking

Prioritize alternative

Definition of a New

QFD Method

STEP 1

STEP 2

QFD-AHP framework

Results

STEP 3

AHP framework

QFD framework

Definition of relations

Definition of functional

characteristics

Definition of customer’s nees

AHP – Pairwaise comparison

matrix

Figure 2. Methodological steps of QFD-AHP approach

Step 1: Prioritize Alternatives. The team of experts defined with the help of the AHP methodology the needs and functional

characteristics that a customer expects to satisfy through the purchase of the product of the firm. Using AHP a sample question

used here would be ―How much more important is Certification than Cost of raw material with respect to the Functional

characteristics?‖ The outcome of this evaluation is a set of weights representing the relationships between elements. The result of

this work is shown in the following Table 3 and Table 4.

Table 3. AHP priority vector for customer’s needs

Customer’s need Identification Code of Customer’s needs AHP Priority vector - weight Order

Filtering power A 0.28 1°

Capacity of regulating the flow B 0.22 3°

Lifetime C 0.23 2°

Dimensional specification of coupling D 0.05 5°

Product certified E 0.12 4°

Competitive price F 0.07 6°

Table 4. AHP priority vector for functional characteristics

Functional features Identification Code of

Customer’s needs

AHP Priority vector -

weight

Order

Filtering degree A 0.08 5°

Thermic resistance B 0.10 4°

Mechanical resistance C 0.21 2°

Dimension D 0.19 3°

Certification E 0.35 1°

Cost of raw materials F 0.04 6°


31

Step 2: Definition of a new QFD method of aid for the creation of the correlation matrix. Then, through the application of the QFD

a complete and appropriate transfer of the needs which pointed out into measurable functional characteristics was established, so

as to make a table, in which these functional characteristics are compared with the simple needs of the customer. Therefore 3

degrees of correlation are established conventionally by suitable factors: 3= strong; 2= average; 1= weak. If there are no

connections, the corresponding crossings in the matrix are left empty. The correlation factors and the allotment of the priorities to

the requisites allowed to establish a classification of the characteristics according to their importance (Figure 3).

Figure 3. Correlation matrix

The functional characteristics are still a matter for the realization of another table; the correlations between these characteristics

are pointed out by using suitable factors (Figure 4).


32

FIL

TE

RIN

G D

EG

RE

E

TH

ER

MIC

RE

SIS

TA

NC

E

Mec

ha

ica

l R

esi

sta

nc

e

DIM

EN

SIO

N

CE

RT

IFIC

AT

ION

Co

st o

f R

aw

Mate

ria

l

C1 C2 C3 C4 C5 C6

Figure 4. Correlations between functional characteristics

In order to make the definition of the correlations among the different functional requisites pointed out as objective as possible,

we set up a system to express the correlation table automatically, making the planner intervene only when necessary to establish

the ―sign‖ of the correlations themselves. Before going on with the description of the algorithm, it is necessary to define the terms

―correlation‖ and ―sign‖ of the correlations. Within the QFD, two functional characteristics are said to be correlated if the

variations of the values of one cause changes in the values of the other and viceversa. and, on the contrary, the sign of such

correlations is positive, if to the ―positive‖ variations of one correspond ―positive‖ changes of the other, ―negative‖ if otherwise. In

order to define the relations existing (induced by the requisites) between functional characteristics, it is necessary to take into

consideration the Relationship Matrix R (House of Quality – area D). Here below is Relationship Matrix R (figure 5):

312220

030000

003000

020330

022213

021223

R

Figure 5. Relationship Matrix R

It is also necessary take into consideration all the vectors (Matrix B) taken together in bi column (each combined with a well

defined functional characteristic expressed in the matrix of the relations R). Here below is Matrix B (Figure 6):

B

1 1 1 1 1 0

1 1 1 1 1 0

0 1 1 0 1 0

0 0 0 1 0 0

0 0 0 0 1 0

0 1 1 1 1 1

Figure 6. B Matrix


33

The elements of the vectors bi (= 1,….,n) have been defined starting from Relationship Matrix R in the following manner

(Equation 2):

i,j if rij= “3” or “2” or “1”, then bij = 1 (2)

Thus, from R matrix, we then pass to a B binary matrix, whose bi columns are afterwards standardized in order to make their

interpretation easier, so as to obtain a new set of vi vectors (=1,…,n) which define a new N matrix (Figure 7):

N

1

2

1

2

1

2

1

2

1

50

1

2

1

2

1

2

1

2

1

50

01

2

1

20

1

50

0 0 01

20 0

0 0 0 01

50

01

2

1

2

1

2

1

51

Figure 7. N Matrix

To represent the effects of the dependence between the i-nth and the j-nth characteristics, we introduce the note qij (Equation 3):

qij= vi vj= cos (vi, vj) ij= 1,…,n (3)

Effecting the calculation of qij for all the vector couples of N matrix, it is possible to determine the matrix of the dependences of

the Q characteristics: Q= N NT (Q is a symmetrical matrix with qij=qji; i=j=1,…, n). The Q matrix expresses the degree of

dependence induced between functional characteristics with reference to how they influence the customer’s requisites. It is

interesting to note that the determination of the Q matrix also permits to underline the presence of columns and /or lines without

relations respectively with other lines and/or columns of the same matrix, marked by the appearance of some ―zeros‖ along the

principal diagonal of the matrix. For the ―filling up‖ of the roof of the ―house of quality‖, the information contained in Q is

compared with a K predefined threshold (with 0 < k < 1); ij if qij > k the existence of a potential correlation between the j-nth

and i-nth characteristics is accepted, otherwise it is considered inexistent. The Q matrix obtained is the following (Figure 8):

Figure 8. Q Matrix

As the table confirms, the Q matrix is really symmetrical. In the table the values of q ij > k are also pointed out, since, in this

particular case, a threshold value of K= 0.7 has been fixed. After establishing and pointing out the couples of characteristics whose

degree of dependence induced is superior to the K threshold, it is necessary to estimate the real consistency of the dependence and

transfer it into a correlation, defining its positive or negative ―sign‖. So, in this particular case, all the dependences are transferred

into a correlation, except the couple C1,C3 which is rejected since it does not possess the necessary requisites. Finally, semantic

correlations must be added to all dependences, defined on the base of quality reasoning. The whole of the correlations identified

defines the roof of the house of quality (Figure 9).

C1 C2 C3 C4 C5 C6

C1 1 0,71 0,71 0,71 0,63 0

C2 0,71 1 1 0,75 0,89 0,5

Q= C3 0,71 1 1 0,75 0,89 0,5

C4 0,71 0,75 0,75 1 0,67 0,5

C5 0,63 0,89 0,89 0,67 1 0,45

C6 0 0,5 0,5 0,5 0,45 1


34

Figure 9. Identification of the roof of the house

Step 3: Benchmarking and Results. Benchmarking started by appraising the ranges of the functional specifications (Table 5).

Table 5. Ranges of the functional specifications

FU

NC

TIO

NA

L C

HA

RA

CT

ER

IST

ICS

FIL

TE

RIN

G D

EG

RE

E

TH

ER

MIC

RE

SIS

TA

NC

E

ME

CH

AN

ICA

L R

ES

IST

AN

CE

DIM

EN

SIO

N

UNITS p.p.i. °C n°utilizations mm

width length height

10-20 730-750 1 10-33 10-25 2-3

20-30 750-760 2 33-50 25-33 3-12

30-80 760-780 50-66 33-50 12-15

80-90 780-800 66-70 50-66 15-20

90-100 800-1000 70-75 66-70 20-22

100-150 1000-1050 75-80 70-75 22-23

1050-1100 80-81 75-80

1100-1150 81-90 80-81

1150-1200 90-99 81-90

1200-1250 99-100 90-99

1250-1300 99-100

1300-1320 100-114

1320-1360 114-200

1360-1380

1380-1400

1400-1440

1440-1460

1460-1500

RA

NG

E o

f F

UN

CT

ION

AL

SP

EC

IFIC

AT

ION

S


35

In order to verify the quality of the specifications of the project, a scheme was made which lets allot its value to every project

under examination. The project characteristics defined for the Project A (our company) and for Project B (a firm leader in the field

of filters) are reported in Table 6.

Table 6. Project evaluation table (C.F. = Functional Characteristics – R.S.F. = Ranges of functional specifications)

weight C.F. R.S.F. A B

A B A B

117 C1 10-20 1 1 83 33 100 70

20-30 1 1

30-80 1

80-90 1

90-100 1

100-150

168 C2 730-750 1 1 100 100 100 100

750-760 1 1

760-780 1 1

780-800 1 1

800-1000 1 1

1000-1050 1 1

1050-1100 1 1

1100-1150 1 1

1150-1200 1 1

1200-1250 1 1

1250-1300 1 1

1300-1320 1 1

1320-1360 1 1

1360-1380 1 1

1380-1400 1 1

1400-1440 1 1

1440-1460 1 1

1460-1500 1 1

186 C3 1 100 100 100 100

2 1 1

Cast Iron

% FOUNDRIES SATISFIEDput the value 1 where appropriate

TABLE OF VALUATION OF PROJECT

Bronze-Brass-Copper-Aluminium

It is important to note that the range of functional specifications and the percentage of customers satisfied per section have been

supplied by external sampling including firms that are leaders in the field concerning our work and allow to represent all the

situations of the Cast Iron Foundries and of Bronze-Brass-Copper-Aluminium Foundries. After defining the value customer for

both projects and knowing the relative sale prices, it is possible to estimate their cost per point for the customer. From the

comparison of costs it is evident that Project B is better than Project A (Table 7).

Table 7. Comparative overall results

A B

1400 1300

806,61 853,71

895 874,9

1,74 1,52

1,56 1,49

Cast iron foundries

Bronze-Brass-Copper-Aluminium foundries

Cast iron foundries


COMPARATIVE OVERALL RESULTS

Sale price

VALUE A CUSTOMER

(expressed in points)

COST PER POINT FOR THE CUSTOMER

(€)


36

After these results, an improvement was started for Project A. The improvement was made possible by varying the ranges of

specifications connected with the C5 and C4 characteristics which are at the top in the relative hierarchical scale (Table 8).

Table 8. New comparative overall results

A B A*

1400 1300 1400

806,61 853,71 941,11

895 874,9 927

1,74 1,52 1,49

1,56 1,49 1,51

Cast iron foundries


COMPARATIVE OVERALL RESULTS

Sale price

VALUE A CUSTOMER

(expressed in points)

COST PER POINT FOR THE CUSTOMER

(€)

Cast iron foundries


The actions set out allowed an increase of the value/customer of Project A and therefore a reduction in the cost per point for the

customer. The method proposed permits to simplify the activities of analysis of the information contained in the QFD tables;

however, it does not take into consideration the opportunity of inserting more functional characteristics; if these are exhaustive of

the problem or if some of these are redundant. Only the planner can give these matters an appropriate answer. Therefore, the

method suggested can be used in an interactive manner. Finally, it is interesting to observe how the scheme of valuation of the

dependence between functional characteristics can be reproposed for the valuation of possible correlations with the customer’s

requirements. Further development could be oriented in developing a benchmarking analysis to define the market share of product.

6. Conclusions

Complex or important decisions should not be based solely on instinct. Whether prioritizing customer needs in QFD, making

budget decisions involving a variety of tangible and intangible strategic goals, managing conflicting stakeholders, or selecting

from among dozens or hundreds of alternative initiatives to be pursued, the Analytic Hierarchy Process (AHP) can help managers

and developers combine all this information and make informed decisions.

The new methodological approach allows to:

Align their decisions with their organizational objectives.

Implement a structured, repeatable and justifiable decision making approach.

Leverage organizational expertise.

Improve top-down and bottom-up communication.

Prioritize customer needs.

In the light of the results of the analysis of the business case proposed, it comes out that the QFD-AHP visualizes, in the most

impartial way, the customers’ requests, putting them into quality measurable characteristics and judging their rationality,

productivity and adequacy to the market. Besides, the QFD points out the most critical characteristics deriving from the customer’s

real needs and from the position of the firm as to competition, simplifying the determination of the sections of improvement on

which it is possible to intervene in order to fill the existing gaps.

In this work, some methods were realized which allowed to simplify the phase of analysis of the data contained in the ―house of

quality‖, and, therefore, brought about a reduction in the times of development of the project. Particularly, through the

methodology employed for the valuation of the matrix of correlation, a procedure was set up, which, owing to the experimental

verification, fulfilled, has allowed to:

Make the filling of the table of correlations among the functional characteristics of the product easier.

Verify automatically the presence of functional characteristics and/or customer’s needs not connected with other needs

and/or functional characteristics.

Determine the minimal whole of functional characteristics which are connected with all the customer’s requisites.

Besides, from the analysis of the results obtained, it came out that it:

Allows to have at our disposal a shared definition of the products of the factory expressed in functional terms (profile of

the product).


37

Allows to work in teams efficiently and without waste of time.

Keeps the functional and technical characteristics separate, without any risk of binding the progress of the project.

Allows to define in time all the functional specifications.

Represents the base for comparing different alternatives of the project as to the value perceived by the market.

Represents the base for a comparison with competition: it is possible to effect a direct comparison, or, also, to have

elements in order to establish the sale price of the new product, so as to assure a sufficient competitive advantage.

The proposed model does not eliminate subjectivity completely—however, subjectivity elimination is not an attainable or desirable

goal. The advantage of the proposed analytic model is that it adds quantitative precision to an otherwise ad hoc decision-making

process.

Further development could be oriented in analyzing more complex case studies and in introducing the risk, cost and opportunity

control in AHP hierarchy.

References

Akao, Y., Mazur, G.H., 2003. The leading edge in QFD: Past, present, and future. International Journal of Quality and

ReliabilityManagement, Vol. 20, No. 1, pp. 20–35.

Bhattacharya, A., Sarkar, B., Mukherjee, S.K., 2005. Integrating AHP with QFD for robot selection under requirement perspective.

International Journal of Production Research, Vol. 43, No. 17, pp. 3671–3685.

Chan, L.K., Wu, M.L., 2002. Quality function deployment: A literature review. European Journal of Operational Research,

Vol.143, No.3, pp. 463-497.

Chan, L. K., & Wu, M. L. 2005. A systematic approach to quality function deployment with a full illustrative example. Omega,

Vol. 33, No. 1, pp. 119–139.

Chen, C. C., 2009. Integration of quality function deployment and process management in the semiconductor industry.

International Journal of Production Research, Vol. 47, No. 5, pp. 1469–1484.

Chin, K. S., Wang, Y. M., Yang, J. B., & Poon, K. K. G., 2009. An evidential reasoning based approach for quality function

deployment under uncertainty. Expert Systems with Application, Vol. 36, No. 3P1, pp. 5684–5694.

Chuang, P.T., 2001. Combining the analytic hierarchy process and quality function deployment for a location decision from a

requirement perspective. International Journal of Advanced Manufacturing Technology, Vol. 18, No. 11, pp. 842–849.

Cohen, L., 1995. Quality function deployment: how to make QFD work for you. Addison-Wesley Publishing Company. United

States of America

Cor P.M. Govers, 2001. QFD not just a tool but a way of quality management. International Journal Production Economics, Vol.

69, pp. 151-159.

Das, D., Mukherjee, K., 2007. Development of an AHP-QFD framework for designing a tourism product. International Journal of

Services and Operations Management, Vol. 4, No. 3, pp. 321 – 344.

De Felice, F., Falcone D., 1999. Definition of the specifications of a filter in ceramic material by the employment of the Quality

Function Deployment. ECEC’99 European Concurrent Engineering Conference 1999 – SCS, Erlangen- Norimberga (Germany).

De Felice, F., Petrillo, A., 2009. Proposal of a new model for the optimization of the organizational process in industrial company

through the application of the Analytic Network Process. 6th International Conference on Modelling & Applied Simulation,

Porto de la Cruz (Tenerife).

Han S.B., Chen S.K., Ebrahimpour M., Sodhi M.S., 2001. A conceptual QFD planning model. International Journal of Quality &

Reliability Management, Vol. 18, No. 8, pp. 796 – 812.

Juran, J.M., 1993. Quality planning and analysis: from product development through use. 3th ed. McGraw-Hill, Inc. United States

of America

Hanumaiah, N., Ravi, B., Mukherjee, N.P., 2006. Rapid hard tooling process selection using QFD-AHP methodology. Journal of

Manufacturing Technology Management, Vol. 17, No. 3, pp. 332–350.

Ho, W., 2008. Integrated analytic hierarchy process and its applications – A literature review. European Journal of Operational

Research, Vol. 186, pp. 211–228.

Hsiao, S.W., 2002. Concurrent design method for developing a new product. International Journal of Industrial Ergonomics, Vol.

29, No. 1, pp. 41–55.

Köksal, G., Eğitman, A., 1998. Planning and design of industrial engineering education quality. Computers and Industrial.

Engineering, Vol. 35, No. 3–4, pp. 639–642.

Kwong, C.K., Bai, H., 2002. A fuzzy AHP approach to the determination of importance weights of customer requirements in

quality function deployment. Journal of Intelligent Manufacturing, Vol. 13, No. 5, pp. 367–377.

Lam, K., Zhao, X., 1998. An application of quality function deployment to improve the quality of teaching. International Journal

of Quality and Reliability Management, Vol. 15, No. 4, pp. 389– 413.

Li, Y., Tang, J., Luo, X., Xu, J., 2009. An integrated method of rough set, Kano’s model and AHP for rating customer

requirements’ final importance. Expert Systems with Applications, Vol. 36, pp. 7045–7053.


38

Lin, M.C., Wang C.C., Chen, M.S., & C. Chang, A., 2008. Using AHP and TOPSIS approaches in customer-driven product design

process. Computers in Industry, Vol. 59, No. 1, pp. 17-31.

Partovi, F.Y., 1999. A quality function deployment approach to strategic capital budgeting. The Engineering Economist, Vol. 44,

No. 3, pp. 239–260.

Ramanathan, R., & Jiang, Y. F., 2009. Incorporating cost and environmental factors in quality function deployment using data

envelopment analysis. Omega, Vol. 37, No. 3, pp. 711–723.

Saaty, T.L., 1980. The Analytic Hierarchy Process. McGraw–Hill, New York NY.

Saaty, T.L., 2001. Decision making with independence and feedback: The Analytic Network Process, Pittsburgh, RWS

Publications.

Saaty, T.L., 2005. Theory and Applications of the Analytic network Process: decision making with benefits, opportunities, costs,

and risks. Pittsburg, PA: RWS Publications.

Biographical notes F. De Felice, Professor at the Faculty of Engineering of the University of Cassino, board member of several international organizations and responsible for

scientific research and training in industrial plants. Holder of the course planning and management of industrial plants. The scientific activity developed through

studies and researches on problems concerning industrial plant engineering. Such activity ranges over all fields from improvement of quality in productive processes to the simulation of industrial plants, from support multicriteria techniques to decisions (Analytic Hierarchy Process, Analytic Network Process), to

RAMS Analysis and Human Reliability Analysis. The main courses, both curricular and specialistic for territorial businesses in which he is involved are: Safety of

Industrial Plants, Industrial Production Management, Industrial Simulation, Human Reliability Analysis. General Secretary of the Analytic Hierarchy Process – AHP Academy - International Association for the promotion of multi-criteria decision making methods.

A. Petrillo is a degree in Mechanical Engineering, now PhD at the Faculty of Engineering of University of Cassino where she conducts research activities on

Multi-criteria decision analysis (MCDA), Industrial Plant and Quality Engineering at the Department of Mechanism, Structures and Environment of University of

Cassino.

Received August 2010

Accepted December 2010

Final acceptance in revised form December 2010

MultiCraft


Vol. 2, No. 9, 2010, pp. 39-53



www.ijest-ng.com


What is the place of technical and scientific service companies (TSS) in the process of developing new products? Insights on their managerial and

organizational features

V. Lazzarotti, E. Pizzurno*

Università Carlo Cattaneo – LIUC, Corso Matteotti 22, 21053 Castellanza (VA), ITALY

*Corresponding Author: e-mail: [email protected], Tel +39-0331-572.233, Fax. +39.0331.483.447

Abstract The new product development (NPD) process is composed by several phases, from concept to final production rump-up, and for each phase, it is now widely recognized that innovators can find external supporting services as design, prototyping, engineering, etc. Companies offering these technical and scientific services (TSS) are in fact more and more diffused. Literature has conducted some explorative attempts to study more in depth how these service companies are organized and managed by assuming a sort of internal perspective and analyzing the main companies’ choices in terms of business model elements. In a similar vein, we deeply study the organizational and managerial features for a sample of technical service Italian companies. The empirical study has been conducted by a twofold aim: (i) to search for TSS companies that support the entire process of new product development and/or, conversely, that provide services to a smaller part of the innovation process or only on few stages in order to identify clusters of companies; (ii) once identified clusters, to study if they show differences in terms of some organisational and managerial features. The analysis of the gathered data has actually allowed to identify some typical profiles of TSS companies in which the elements of the business model take a particular and coherent combination. Keywords: new product development, TSS, outsourcing, Italy, multiple case-studies 1. Introduction

Several authors offer many different reasons for the establishment and growth of firms supplying technological competences, agreeing that such TSS companies are nowadays more and more diffused (MacPherson, 1997a; MacPherson, 1997b; Howells, 1999; Chiesa and Manzini, 2000; Larsen, 2000; den Hertog e Bilderbeek, 2000; Arora et al., 2001; Muller and Zenker, 2001; Chiesa et al., 2004). Literature (Windrum and Tomlinson, 1999; Debackere 1999; Chiesa and Manzini, 2001, Chiesa et al., 2008) has also suggested several taxonomies in order to identify existing service firms. Moreover, there have been some explorative attempts to study more in depth how these service companies are organized and managed by assuming a sort of internal perspective and analyzing the main companies’ choices in terms of business model elements (Hargadon and Sutton, 1997; Kelly, 2001; Chiesa et al., 2004; Chiesa et al., 2008), for example in terms of organizational structure (i.e. firm organization, human resources management, level of internal competencies, services offered); commercialisation strategy and marketing approach (i.e. clients’ business sector of activity, web site approach, informal approach, etc.), management tools to provide the offered services (i.e. project organization and management tools such as intellectual property, performance measurement systems and project management techniques). However, to our best knowledge, literature contributions to the subject are still too scarce, unstructured and too concentrated on few top firms – as the American IDEO – missing the variety of hundreds of companies offering such services. In this way, they do not give relevant suggestions that can help TSS firms’ managers to organize and provide effective and efficient NPD services; on the other side a deep knowledge of NPD companies can be considered as extremely significant for clients of those services. More precisely, seem to lack suggestions on how the elements of the business model should be composed in a coherent set of relationships to achieve effective and efficient NPD service. Thus it is this stream of works we attempt to help with this paper by deepening the study of the organizational and managerial features for a sample of service Italian companies. In particular, what we thought it was interesting to delve mainly into the case of companies that declare to support the entire process

Lazzarotti et al. / International Journal of Engineering, Science and Technology, Vol. 2, No. 9, 2010, pp. 39-53

40

of new product development. If compared to companies offering single stage services, such companies face in fact a high complexity, given that they have to turn an abstract idea into something that is “real”, “concrete”, and ready to be sold. This means that they have to solve not only the problems of each phase of the NPD process, but also those relating to (i) relationships among the various phases of development, (ii) many competencies and resources needed, (iii) interaction and organizational mode with the client during the whole process (Emes et al., 2005; Jurgens, 2000; Veugelers and Cassiman, 1999). As a consequence, the picture that can be drawn analysing such companies provide interesting suggestions because it allows to appreciate the relationships among the several business model elements (and how they should be composed in order to provide a certain set of NPD services, thus giving concrete managerial suggestions in the sense clarified above). Moreover, a similar result can also benefit companies that offer just one or few stages of the process: they should obtain suggestions in order to decline in a different way their specific business model.

Coherently with the paper goal, the empirical study presented here is a multiple case study, aimed at describing how companies offering services for the NPD process organise and manage their business. Twenty-four companies have been studied through interviews conducted with all the top – or project – managers, trough a semi-structured questionnaire.

The empirical study has been conducted by a twofold aim: • to search for TSS companies that support the entire process of new product development and/or, conversely, that provide

services to a smaller part of the innovation process or only on few stages (the other extreme, i.e. only one phase) in order to identify clusters of companies. In other words, we try to identify clusters by using the element in the companies’ business model representing the “completeness of the service” offered (in a continuum that goes from one stage to the whole process);

• once identified clusters, to study if they show differences in terms of the other investigated organisational and managerial features (all the other company’s business model elements). In other words, we consider these other variables as illustrative ones in order to describe each emerged cluster.

The analysis of the gathered data has actually allowed to identify some typical profiles of TSS companies in which the elements of the business model take a particular and coherent combination. These clusters are described in detail in the paper, which is organised into four different sections:

• description of the conceptual context of this study, giving the basic theoretical background, concepts and definitions; • research methodology; • empirical study: description of the case studies and analysis of data gathered; • managerial implications, conclusions and future research.

2. The conceptual context of the study: the KIBS and the TSS within the new product development process

The purpose of this section is to briefly introduce some concepts and definitions useful to understand what our field of empirical investigation is.

Several authors have studied and demonstrated that, in the innovation development field also, interaction with external entities is growing (Tidd, 1995; Veugelers and Cassiman, 1999; Quinn, 1999, 2000; Chiesa et. al., 2004; Lazzarotti and Manzini, 2009). This tendency towards outsourcing innovation has created a new category of services called KIS – knowledge intensive services (Windrum and Tomlison, 1999) – or KIBS – knowledge intensive business services (Miles, 2000; Muller and Zenker, 2001; Knoben and Oerlemans, 2006; Strambach, 2008; Horgos and Koch, 2008; Zenker and Doloreux, 2008) – characterised by a high innovative level and scientific intensity of the outputs. According to Windrum and Tomlison, 1999, ‘private sector organisations that rely on professional knowledge or expertise relating to a specific technical or function domain. KIS firms may be primary sources of information and knowledge or else their services form key intermediate inputs in the products or production process of other businesses’. This kind of service can be applied to several sectors: from banking to real estate, from market research to insurance services. Among KIBS, a more specific subset can be identified, called TSS – Technical and Scientific Services (Abetti, 1989; Howells, 1999; Larsen, 2000; den Hertog and Bilderbeek, 2000; Chiesa and Manzini, 2001; Arora et al., 2001; Chiesa et al., 2008; Chiaroni et al., 2008). According to these authors, TSS are “services which rely upon technical and scientific knowledge and give an output that is, again, technical and scientific knowledge.” In other words, they are service companies that sell technology and scientific knowledge. What joins these companies and differentiates them from others that fall within the definition of KIBS is therefore the nature of knowledge on which they are based and they incorporate into their services, that is technological.

Literature (Windrum and Tomlinson, 1999; Debackere 1999; Chiesa and Manzini, 2001, Chiesa et al., 2008) has also suggested several taxonomies in order to identify existing service firms: for instance, companies are grouped by type of the output provided (e.g. work-in-progress innovation that is an intermediate finding that needs to be further developed to be commercialized as an innovation; single process activity, that means TSS firms carry out, for the client company, a stage of its innovation process; whole new process development process, that means service companies start from an idea and provide their client with a new product ready to be put into production and then commercialized; technologies to develop technologies, in the case that TSS provide technologies that can be used in order to improve the efficiency and the effectiveness of the client’s company’s innovation process); technical and scientific competences, the TSS firm is based on and incorporates in its services, i.e. the technical or scientific domains in which an excellent knowledge level has been reached (e.g. mechanical engineering, genomic, microchip


41

design); the client firm’s sector of activity (e.g. mechanic, electronic, chemical, pharmaceutical); the generality of the output provided by the TSS company, a general service, i.e. aimed at supporting the innovation process of firms from different sectors or a specific service, i.e. addressed to innovative firms of a specific sector. From all these definitions and taxonomies emerge nonetheless clear that TSS services are important supports to the companies’ technological innovation process.

For the purposes of this study, however, we take a narrower perspective and focus particularly on new product development services within the broader process of technological innovation. To this end a recent classification (Chiesa et al., 2008), revised and synthesized in Table 1, can help us. Firstly, it considers as relevant dimension the generality of the output provided by the TSS company, as just defined above. Regarding this dimension, we consider here the generic services without choosing any sectoral specialization. This choice is aimed to avoid results that are significantly biased by industry-specific factors.

Secondly, supported from the wide literature on the subject, it allows us to define the new product development services within the innovation process: four categories of product development activities, highlighted in bold, where companies, which we will study in this paper, are involved.

Table 1. New product development services

Stage of the R&D process supported

Basic Research

Applied Research

Concept generation

Product design

Engineering (process design)

Launch and commercialization

Specific service Generality of the

output provided General service

Among the many existing contributions that provide their own versions of the NPD process (here we just mention the most

important, such as Urban and Hauser, 1993; Cooper, 1994; Kotler, 1997; Jones and Stevens, 1999; Haden et al., 2004; Rundquist and Chibba, 2004; Varela and Benito, 2005; Cooper, 2008; for a review see Trott, 2008), we follow here basically Verganti’s definition (1997) that identifies the detailed activities composing the first three groups of relevant activities, to which we add launch and commercialization (this one, according to Kotler, 1997). In this regard, see table 2 where the detailed list of activities is shown.

Table 2. List of relevant NPD activities Phase Activities

Concept generation

Definition of briefs Analysis of customer needs Competition analysis Definition of the generic product Generation, testing and selection of concept Assessment of the investment in new product Formulation of project plan

Product design

Design - architectural adaptation Choice of technologies and components Choices to make or buy design Definition of the detailed specifications of those components Design modules-components Prototyping and testing of component quality Integration of modules-components Test Product Quality Optimization

Engineering (process design)

Configuration of the production process Design of machines and tools (dies, tools etc.) Development of part programs to control production machines Definition of schedules and work instructions Definition timing and methods and workforce training Pre-implementation Start production

Launch and commercialization

Launch the product Produce and place advertisements and other promotions Fill the distribution pipeline with the product Pricing


42

Another very interesting aspect debated in literature is regarding the choice of the governance mode “right” to cooperate (i.e. cooperation between TSS companies and their customers) as it is demonstrated by very recent contributions (van de Vrande et al., 2006; Ojasalo, 2008). The term “governance mode” refers jointly both to the particular organizational structure (i.e. alliance, outsourcing, etc.) and its ingrained managerial features (i.e. level of integration; reversibility, level of control over activities, etc). Thus, network organizational form and network management essentially belong together. For this area of studies, literature has already given relevant theoretical frameworks to rationalize the involved variables. For example, the broad set of different organizational structures to collaborate has been studied and categorized as well as the factors driving the choice of such governance modes (Chiesa, 2001). However, the application of these frameworks on concrete cases in order to verify their validity is still scarce as it is declared by the same authors cited above. Thus, the analysis of the TSS companies in this study can provide interesting insights also in such direction. Specifically, according to literature suggestions, the decision-making factors that influence the choice of the governance mode can be grouped in three categories, respectively relating to: i) the objectives of the collaboration, ii) the content of the work to carry out in collaboration, iii) the nature of the partners involved.

As concerns the first factor, a broad set of objectives often requires a long-term relationship and relevant resources from each partner, thus leading to governance modes more integrated than pure outsourcing, configuring a sort of alliance. In contrast, when a precise and limited objective is defined, outsourcing is appropriate (Chiesa, 2001; Hermens, 2001). Governance modes more reversible, less controlled and characterized by a low level of contractual formalization have to be chosen also when the objective of the collaboration is “maximise learning from partners” and thus sharing knowledge (Lawson et al., 2009). Informal mechanisms seem in fact to facilitate information flows and know-how diffusion among the partners. As concerns the second factor, i.e. the content of the work in collaboration, the relevance of this for the customer firm’s competitive advantage seems to be an important driver affecting decisions about the governance mode of collaboration. When collaborations concern a firm’s core technological competence, it is critical to keep control over such knowledge (Chiesa, 2001). Moreover, due to the fact that competence building requires time, the horizon of these collaborations is usually long. Hence, when there is a high potential for a firm to create and/or maintain competitive advantage through cooperation, governance modes characterized by high integration and high control level are the most appropriate. On the other hand, collaborations on non-core technologies and competences do not require strong control. Thus, companies tend to maximize flexibility and to minimize time/cost for establishing the relationship. The customer’s familiarity with the content of collaboration is another example of this second factor. If a firm lacks technical or market competencies, literature suggests that more integrated and formalized modes of collaboration (i.e. alliances or also outsourcing but “reinforced” by structured contracts that explicitly include a strong planning and control in order to increase the level of control over activities and results) are preferred since these allow to access to the partners’ complementary resources (scientific, technical, knowledge, managerial capabilities), whilst pure outsourcing normally does not allow it. Lastly, concerning the third factor (i.e. the nature of the partners involved), cultural differences/distances between partners seem to be important (Folta, 1998) as well as the suggestions by transaction costs theory (Williamson, 1985). “Distance” among partners, which come from different countries or sectors, causes information asymmetry leading to very reversible and low-commitment level governance modes, such as outsourcing. The relative bargaining power among partners seems to be another factor. A more powerful partner (usually larger in terms of size) tends to choose more integrated and/or formal modes of collaboration in order to impose the desired direction to the collaboration (Chiesa, 2001). Obviously, the various factors may lead to different requirements causing some trade-offs. For example, collaborations concerned with all the NPD stages (i.e. broad objectives) require low reversibility but if, at the same time, they are carried out with customers characterized by relevant cultural distance, this factor can push in a different direction. Thus, in most cases, for decision making it is necessary to balance opposite forces (Hendry, 1995).

In summary, in this work we focus on services TSS intended as new product development services that support one or more stages of development of an innovative product without having a specialization addressed to a specific industry. We will devote particular attention to managerial and organizational aspects that characterize the TSS as well as the way such companies organize the relationship of collaboration with their customers (i.e. the organizational mode of collaboration).

At this point we have all the elements to study the selected companies that provide these services, after a brief clarification about the adopted methodology. 3. Research methodology

The research method adopted in this work is based on a multiple case study. Despite the widely acknowledge limitations of this approach, especially in terms of reliability and validity (Ginsberg and Abrahamson, 1991; Yin, 2003), the case study method has the ability to capture the full complexity of the studied phenomenon, including its ‘softer’ aspects. Given that the aim of our empirical study was to investigate TSS practices in-depth, the aforementioned advantage of the case study method was a critical factor in selecting the research approach. Information was collected through direct interviews with companies’ management and internal documents were also consulted.

In order to appreciate the main analogies and differences among companies, a structured cross-case analysis was carried out, through which data and information collected have been elaborated, categorised and compared in order to point out analogies and differences, so as to draw a reliable and synthetic picture of the sample analysed. More precisely, data and information gathered through the case studies were manipulated before being analysed. In particular, we applied the following techniques (Miles and


43

Huberman, 1984): (i) data categorisation, which requires the decomposition and aggregation of data in order to highlight some characteristics (e.g., project organization and management in terms of several variables as reported in section 4) and to facilitate comparisons; (ii) a preliminary within-case analysis was performed with the purpose of considering each case study as a separate one and systematically documenting the variables of interest defined in the questionnaire; iii) explanation-building procedures were carried out so that the relationships between some variables were identified (e.g. factors affecting decisions about the organizational mode with customers); (iv) a cross-case analysis was undertaken for comparing the studied variables that emerged in each case study in order to define clusters of companies.

Moreover, the main evidence and findings emerging have been discussed with some of the people interviewed, in order to verify their validity. Only one firm has been excluded by the analysis because clearly emerged - during the empirical research - as unable to offer a various set of NPD services.

The applied research methodology had two main limitations. First, because the empirical base was mainly built up from personal direct interviews with the company’s top manager, the results are susceptible to bias arising from distorted and subjective interpretations and rationalizations. Second, as in most case studies, the empirical research does not permit any systematic generalisation. That said, the aim of this empirical investigation was not to generalise, but rather to offer a detailed description of the phenomenon and to offer some new insights for future investigations, aimed at generalising results (Eisenhardt, 1989). 4. The empirical study 4.1. The sample: the empirical study presented here is a multiple case study, aimed at describing how companies offering services for the whole NPD process organise and manage their business.

Limiting the search to a first step, we decide to start by focusing on a population – and consequently selecting a sample – consistent with these criteria:

• firms that have declared able to support the entire product development process; • firms that are not specialised in supporting NPD within a specific industry; • private-owned firms that have NPD services as core business; • firms able to develop a physical finished good and, among the sectors, excluding those that show specific scientific

peculiarities in NPD processes (Trott, 2008) i.e. software, pharmaceutical, chemical or biotechnological companies. Twenty-five companies with these features have been studied, as reported in Table 3 through: • interviews: more than 3 telephone and in-person interviews were conducted with all the top – or project – managers,

trough a semi-structured questionnaire. The questionnaire is too wide to be included in this paper; however, respondents were asked questions related to: o The NPD company organization as: (i) firm organisation and services offered, (ii) HR management, (iii) knowledge

management, (iv) firm structure, (v) competencies and collaborations; o The commercialisation and internationalisation strategy intended as: (i) sale of NPD services, external

communication, (ii) CRM (iii) clients’ business sector of activity, (iv) client searching, (v) competitors, (vi) location of clients and (vii) pricing;

o The project organization and management, in terms of (i) technical interaction with clients, (ii) organizational mode of collaboration with customers, (iii) commercialization phase, (iv) intellectual property management, (v) performance measurement system, (vi) projects average duration, (vii) projects per years, (viii) project management and (ix) project work organization.

• documents, both internal (provided by interviewed people, such as internal project reports and prototypes) and public (available, for example, on the web-sites of companies, such as presentations and promotions).

The main conclusions of the study are presented in the following section.

4.2. Research findings: in this paragraph, the organizational and managerial features of the studied Italian NPD firms are described. First of all, the main analogies are analysed, i.e. those elements that characterise in a very similar way all the companies studied; then, the significant differences are pointed into evidence, i.e. those elements in the business model that significantly diverge among companies, with the aim to verify whether some clusters of companies can be identified. 4.2.1. Analogies among the Italian NPD firms: firstly, analogies are described in terms of: company’s organisation, commercialisation and internationalisation strategy, project organisation and management.

Company organisation Firm structure: while the firms are showing a different organisation in relation to their dimension (in term of employees), all of

them – even the smallest – follow a common approach: the matrix management and, more often, a strong (project) matrix. A project manager - who is primarily responsible for the project - is always identified. Functional managers provide technical expertise and assign resources on an as-needed basis.


44

Table 3. The NPD firms interviewed Company Revenue Employees 1 Appliances Engineering 1 – 5 mln € 11 – 20 2 Attivo Creative Resource 200.000 – 1 mln € 1 – 10 3 Creanova 200.000 – 1 mln € 1 – 10 4 Design Continuum Confidential 11 – 20 5 Design Group Italia 1 – 5 mln € 11 – 20 6 Disegno Bello 200.000 – 1 mln € 1 – 10 7 DNA Confidential 1 – 10 8 Esseti Confidential 1 – 10 9 Far Design < 200.000 € 1 – 10

10 Fox Bit 1 – 5 mln € 51 – 100 11 James Irvine 200.000 – 1 mln € 1 – 10 12 MR&D Institute > 5 mln € 51 – 100 13 Partec < 200.000 € 1 – 10 14 Pininfarina Extra > 5 mln € 21 – 50 15 Pro Design Italia Confidential 1 – 10 16 Promau 1 – 5 mln € 51 – 100 17 SB3 1 – 5 mln € 1 – 10 18 Sintesi AB 200.000 – 1 mln € 1 – 10 19 Sowden Design 200.000 – 1 mln € 1 – 10 20 Spring Design Confidential 1 – 10 21 Studio Bonfanti 200.000 – 1mln € 1 – 10 22 Studio Primalinea 200.000 – 1mln € 1 – 10 23 SZ Design (Zagato) > 5 mln € 51 – 100 24 Vegni Design Confidential 1 – 10 25 VIP Technologies < 200.000 € 1 – 10

Human resources management: this can considered a key point for the success of the NPD firms, which base largely their

activities on the competencies and experiences of their employees. In particular, the critical factors in HRM for NPD companies are: (i) the recruitment of talents or well-trained personnel; (ii) the continuous improvement of the capabilities; (iii) job environment and team working. Significant time and resources are dedicated to improving performance in these three topics. In terms of profile, usually graduates in Economics, Engineering (mechanical, electronic, management…) and Industrial Design can be found, but also Design licentiates. A significant percentage of employees are very young.

Knowledge management: even if the formal storage of past projects results and solutions is recognised to be effective and efficient, the Italian NPD firms usually do not use sophisticated tools to this aim. Even when designed and realized, these archives are not easily accessible and commonly used. Informal and personal relationships and networks generally represent the most important KM system, together with the storage of prototypes and pictures of past developed products.

Commercialisation and internationalisation strategy Sale of NPD services and external communication: the market of services for NPD is still unknown, even if the externalisation

of NPD seems to be increasingly relevant. Main consequence of this lack of knowledge, shared language and sufficiently precise and widespread classifications is that the marketing of the services offered is a very difficult task for NPD service companies, which have to rely on their own capability of self-introduction to the market. More precisely, it has been recognised that it is very difficult for NPD companies to clearly communicate what they are really able to do. Web-sites and visiting professional fairs represent the main channels for the external communication. As a consequence, information given on the web sites, if used, tries to be rich, detailed and well structured. A section, called “credentials” is frequently used to better clarify the company’s activity and qualification, in which previous projects of new products are described. This difficulty in communication is also recognised as the main barrier in the acquisition of new clients, together with the problem of evaluating the “value” of the services offered.

Customer relationship management: the Italian NPD companies show, on average, 80% of continuative relations against a 20% of single projects committed. All main CRM techniques are well known and widely used.


45

Prices and margins: the complexity of the project determines the price (from few thousands Euros to hundred thousands Euro); the average is around 80.000 € - 100.000 €. In price definition the following parameters are taken in consideration:

• time (in term of hours – or days/weeks – required to perform the project); • kind of activities performed. In fact not all activities have same evaluation (mechanical design is considered less than

electronic design, for instance); • kind of resources employed (human, technical, etc.); • estimated value of the product on the market .

The average expected margin on the projects is around 20%. Project organisation and management Technical interaction with clients: the level of computer-aided support reached in the last years, above all concerning innovative

solutions for remote collaboration, is extremely advanced. Even if software and ICT tools for remote collaboration are well known by companies, they are far from being diffused and widespread adopted. All NPD firms agree that the main cause reside in the client culture, which suggests adopting a more traditional approach. Companies still prefer personal relationships in NPD. As a matter of fact, even companies with a size and a level of project complexity totally adequate to the use of remote ICT tools, base their co-operation with clients upon:

• periodical meetings/contacts with the customer in order to define the concept (through the due brainstormings), to consult together with the technicians and to present the work in progress and the finished project;

• traditional communication tools, such as fax and e-mail. Also within the NPD firm itself, such radical changes are not undertaken, and traditional updates and periodical meetings among

members of the inter-functional team take place. Commercialisation phase: this phase of the NDP process is strictly controlled by the customers and it is never outsourced to

NPD firms, even when such firms offer adequate competences and skills. It is quite evident that this is probably the most critical phase for clients to ensure the appropriability of their innovation.

Intellectual property management: in all the observed companies, by contract, the intellectual property of the new product is owned by the clients. The patenting process is usually outsourced to specialists and considered as a standard service for the clients.

Performance measurement system: typically, the performance measurement system (PMS) in NPD projects takes into consideration conventional economic performance indicators (costs, timing, quality, resources, clients satisfaction, level of sales of the new product compared to forecasting, etc.). In several NPD firms, it has been observed an increasing diffusion of approaches to the measurement of innovative performances (i.e. commitment and creativity of employees, new clients and other qualitative indicators as company reputation) aimed at monitoring the company’s innovative capability, processes and results (Chiesa et al., 2006).

Project management: a project manager – who is primarily responsible for the project – is always identified. Functional managers provide technical expertise and assign resources on an as-needed basis. Project teams are created on the basis of adequate skills and knowledge of the specific customer. Basic approaches of project management (Traditional Project Management as well as Closing Critical Chain Project Management) are well known and widely used and the project manager and the team use the most diffused tools, software and methodologies in the technical-design area (concurrent engineering, Design for X, etc) as well as in the managerial one (forward engineering, WBS, milestones, Critical Path Method – CPM, Gantt diagrams, etc). The project teams usually interact with the project’s client in correspondence of the defined milestones, when the state of the art is verified, the possible delays are defined and the needed corrective actions are identified, and all information about the project is shared. These meetings are also critical for evaluating qualitative “soft” factors, such as the development of experiences, in a business collaborative atmosphere.

4.2.2. Differences among the Italian NPD firms: secondly, differences are here analysed in terms of company’s organisation, internationalisation, commercialisation strategy and project organisation and management.

Company organisation Firm organisation, services offered and related competences: size is significantly different among the studied firms. Companies

can be constituted by a limited staff – from 1 to 10 employees - or be structured with highly remarkable resources (from 51 to 100 employees). In the first case the firm structure results extremely flat, with owners and employees to fulfil basically the same tasks. In small companies, few employees manage the entire project, occasionally creating inter-functional teams with client’s people. Big companies are more structured and present a formalised structure, with the following functions (that correspond to services offered to the customer and related competences, along the NPD process):

• Marketing: this function is dedicated to strategic marketing and it is able to carry out any qualitative and quantitative analysis (trend definition, product placement, market share calculation, etc.);

• Industrial Design (ID): this function generally works in coordination with the Mechanical and the Electronics Design departments in order to merge and coherently integrate the various ID objectives (such as strategic design, product and graphic design and brand development, ergonomics, functionality of shapes, materials, …). This function/unit usually involves much specialised personnel, it uses state of the art knowledge and technology and, hence, it provides a high


46

quality outcome, integrated with the technical solutions adopted. It should be noted that some firms tend to specialize themselves on the more technical aspects of the industrial design. In these “technical oriented” firms, ID service is thus only partially offered, since aspects as brand development or aesthetic design are neglected.

• Mechanical Design: Mechanical Design is usually performed at excellent levels. New technologies are constantly adopted, with the aim to deliver new solutions that better deal with human factors (i.e. specific needs and constrains that derive from the direct interaction among human beings, products and technology) and with the growing sensitiveness to aesthetics.

• Electronics and Software Development: very few companies are actually able to offer this part of the NPD services according to the state of the art knowledge and technology. Indeed, some firms completely lack such competencies. As a consequence, when a new product requires electronic or SW subparts, these firms acquire outside a “shelf solution” or, in some case, collaborate with external sources (in some case with the clients themselves). Also “technical oriented” firms (which focus mostly on the technical aspects of the NPD, as defined above) has proved very partial experiences in this field.

• Engineering: the supply of these services implies for significant investments. Due to the high rate by which the necessary assets become obsolete and the high investments related, only the most developed firms can afford pre-production and rapid-prototyping machinery. Moreover, this set of skills usually includes laboratory test and analysis, production of simple prototypes and supplier selection, competencies normally diffused in all the studied companies.

Competencies and collaborations: the mix of internal/external competencies is extremely various. In big firms the access to external sources of knowledge and technology is very low (just rapid prototyping or software development); these companies relies only on internal workforce. If the case, the external partnerships are arranged with a twofold aim. Sometimes, the external collaborations are stable and they cover the range of services offered by the NPD firm that, in this way, can enlarge and enrich the specialised internal competencies. In other cases – especially where a wide range of services is performed by a considerable internal staff – the occasional external collaborations are useful to support NPD company in periods of intense work.

These collaborators are often professionals or small firms specialised in one phase of NPD process. Thus, if NPD firms can have a maximum of three external established collaborations, other NPD firms can have tens of external partnerships. Rarely, the collaboration involves universities. Usually, partners are involved with different types of collaborations (and consequently with different levels of coordination and integration, according Chiesa and Manzini, 1998), for instance: (i) as members of the team developing the new product, or (ii) as simple suppliers.

Commercialisation and internationalisation strategy Clients’ business sectors: almost all companies support NPD process in several different industries and it has not been noted a

clear association between specific products and specific group of NPD firms (the client’s business sector crosses among firms’ groups). Automotive, telecommunication and electronics are the most diffused (more than 70% of NPD firms have clients belonging to these industries). Anyway, some companies seems to be focalized on some sectors while for other TSS the choice is broader.

Location of the client: NPD firms are positioned close to the national economic neuralgic centres. For these companies, the location in an economically important area is relevant also to be close to potential customers. Furthermore, it allows a NPD company to be better and faster informed about their working field: conferences, seminaries and other similar activities are held usually in such economic centres. Some NPD companies serve clients in Europe and worldwide, above all the biggest ones. Anyway, interviews proved that clients’ location rarely causes managing problems for Italian NPD firms, due to the availability of electronic tools. However, the physical distance can cause problems: the nature of the activities (i.e. creative ones) leads to the need of a direct contact with its own customers.

The internationalisation strategy: the internationalisation strategy of companies seems to be a critical point, since globalisation allows to significantly widening the potential market. Having a geographically wide market immediately points out the “distance” problem. Together with the “physical” problem described above, also cultural, legislative and linguistic differences are factors that tend to undertake growing importance, and above all when the concern different continents. Furthermore, an international market seems to be accessible only by big service firms. As a matter of fact, large dimensions seem to attract better skills, which, in turn, influence the diffusion of the firm’s work. Generally, it has been observed that companies with similar skills but different dimensions (small vs. big) cover different sizes of markets, where the main distinction is between intra (i.e. small companies as Fardesign, Bonfanti) and inter-continental ones (big companies such as Design Group Italia, Attivo Creative Resource).

When the intercontinental market is becoming consistent, local needs start to emerge and thus companies have to be open new offices abroad (as in the case of company “MR&D Institute”). Physical proximity, obtained through new seats, will answer the aforementioned needs of filling cultural, linguistic and legislative gaps. De facto, new seats are never as big and structured as the headquarters and do not have all the skills of the latter either. They focus mainly on marketing, market analysis and design activities - that is on those factors that are more distance-sensitive - while the more technical phases of each project will be forwarded to the head office (Design Group is an example in this sense).

From this point of view, another factor seems to affect the internationalisation strategy: the firms’ main focus of activity, i.e. the phase, within the NPD process, to which the company dedicates its main resources and/or those in which it is considered as excellent:


47

• An orientation to the “soft” design aspects (i.e. style, elegance, interface innovation) of NPD seems to experience a wider geographical feedback than technical ones (for example, this is the case of Pininfarina Extra, Zagato, which have an image and brand appreciated worldwide).

• In contrast, an orientation to technical contents tends to become anonymous, as often lacking of a proper image. Without the necessity of looking for a certain line or style, the attractiveness for customers to face more expensive and difficult collaborations with distant partners seems to disappear (as declared by Pro Design, VIP Technologies). This, of course, limits the possibility for NPD companies of offering their services to geographically far companies. Moreover, the very technical part of the project is often developed in collaboration with the client itself, due to the fact that technical know-how of its products is hardly surpassed by that of the NPD firm. Thus, normally, customers are looking for a geographically closer partner rather than one which is further away.

In conclusion, more design-oriented firms will thus tend to have a more geographically heterogeneous market, while technical oriented ones will basically work with national or even regional clients.

Clients searching: Shortly, the main potential channels for customer retrieval are: brand importance (of the NPD firm), personal acquaintances, word of mouth, marketing/trade dedicated employee, Web site and presence at professional fairs. Theoretically, each kind of NPD firm could define its specific mix of the aforesaid channels, thus distinguishing from the others. Practically, the importance of the personal acquaintances and word of mouth channels is a common point between all kinds of NPD firms, although Web mode seems more diffused for companies providing the entire set of NPD services.

Competitors: firms able to offer a very complete set of NPD services face a strength international competition (IDEO, Well Design, Cambridge Consultants, are the most famous competitors). On the other hand, firms which are specialised in few phases compete mainly on a local basis, with very small firms and professionals.

Project organisation and management Project average duration: in general what influences the duration of a project are the technical (mechanical and electrical, …)

components and phases more than design ones, while the overall length is related to the complexity of the project and, mostly, to the number of phases covered. Normally, firms that cover the entire NPD process have average one-year duration, period that is reduced to 2 months in firms covering only few phases.

Project work organization: inter-functional teams, to which also customers participate, are diffused in almost all TSS companies. Anyway, it is verified by the information gathered, how:

• Smaller companies (maximum 4-5 employees) carry forward projects involving a single internal person - or a couple at most - and organising at times inter-functional teams together with part of its client’s staff (Fardesign, Bonfanti, Gloss Design). Also firms constituted by a range of 6-10 people, work in a similar way, having 2-people-team supervised usually by one of the company managers or seniors (Attivo Creative Resource, Studio Prima Linea). This is a consequence of small firm dimensions and of homogeneous internal skills. In fact, at these levels, the company structure is very flat and similar skills are present, which obstacles the creation of structured teams. Project complexity is usually coherent with these firms’ capabilities, and excessively long and complicated developments are carried forward together with the customer’s personnel.

• Differently, it is with a more consistent, structured and heterogeneous staff that specific-competence-mixed internal workgroups start to be observed. Anyway, joint-work together with the client does not disappear in these cases (MR&D Institute, Design Continuum).

Organizational mode of collaboration: TSS companies providing services in all the NPD stages are linked to their customers by long-term relationships. This leads to more integrated mode of collaboration (e.g. a long- term structured contract is defined to reinforce outsourcing) with respect to the situation of companies engaged in a precise and limited scope (i.e. a specific phase of the NPD process). A similar impact is provided by the fact that the content of the collaboration is strategic or not for TSS customers. Also a low familiarity with the content of work by customers drives the governance mode towards highly controlled forms. On the other hand, the objective of maximisation of learning between partners, quite common in several relationships, seems to lead to less formalized modes in order to encourage information flows and knowledge sharing. As it is perhaps obvious to expect, we note also that cultural distance between partners is claimed by those TSS companies whose customers are international. This factors seems to encourage very reversible and low-commitment organizational mode. Finally, if the customer is larger than TSS company, it seems to prefer more structured modes in order to drive collaboration in the desired direction.

4.2.3. The emerging clusters of NPD companies: all of the interviewed companies have described themselves more or less explicitly, through words and images, as capable of performing any necessary activity to create a functioning product out of an idea. In order to completely develop a new product an NPD firm should at least be skilled in the market analysis, industrial design, mechanical, electric and electronic design and software development areas. But only few companies trace the described profile, while the rest cover just a part of the complete development process. The most widespread missing skills of such companies are the electronic/software development ones. This means they will be able to conceive and design a certain variety of products, but won’t be able to complete (nor autonomously nor through their nets of collaborators) the development of an object that would require electronic or software applications. Lacking of one or more skills to be considered a complete NPD firm, we suggest to name them “Integrated Industrial Design companies” (ID+ in figure 1) according to their own high-level design competences.


48

Additionally, a second factor that must be taken into consideration is the omission or distortion of published information by NPD companies. The NPD companies, during interviews, admit they are involved with just a part of the NP development process clarifying that there is an informative gap between declared and real services. This affects the firm-customer relationship, distorting the information the latter perceives.

Figure 1 summarised the effect of lack of definitions and clustered firms into categories according to their level service and gap between declared and offered services. It highlights also that bigger companies need a lower level of outsourcing.

(1) gap caused by distortion of information and lack of common definitions (2) gap caused by information asymmetry firms – clients

Figure 1. The lack of common definitions and firms’ categories

In detail, the results of the empirical analysis showed that the considered market is rather fragmented in terms of services

offered. However, thanks to the analysis of analogies and differences described above, some clusters can be identified. They can be represented as in Figure 2, considering at first the relationship between the completeness of the service provided (i.e. all the phases of NPD are supported) and the level of one of the managerial variable studied above (i.e. the level of internal competences). They are named as follows:

• Complete new product development firms (identified in the Figure 2 with the letter “C"): companies able to plan and develop a complex product, providing a high level of novelty and supporting the client in all the phases of the NPD process and to offer strategic consulting as well;

• Integrated industrial design firms (ID+) type I: companies which possess competencies in all NPD fields, except for software and electronic or electro-technical functions. In some cases all these competencies are internal, in other cases they rely on an effective external network of partners;

• Integrated industrial design firms (ID+) type II: however they present themselves as able to offer services as in the previous group, these small companies tend to be specialised in one (or more) phase of the NPD because they rely only on internal competencies; in consequence, services offered are decreasing whether internal resources decrease.

Thus, we found that the main difference in terms of competences is concerning the electronic and software skills. The figure can also be interpreted in a dynamical sense and it may represent a sort of path that companies can follow to increase the completeness of the service. In fact it highlights how companies with more expertise are able to offer a more complete service (cluster C); thanks to the use of external expertise, along with those inside, companies are able to maintain the completeness of the service (type I); instead, with the decreasing of internal expertise, if companies do not resort to external competences, completeness of service decreases significantly (type II). In the studied sample all companies started their history as “type II”. Some of them, after a number of years, having observed growing requests by clients in different areas of knowledge, began to fill missing competences through the network of collaborations, transforming themselves into ID+ type I. If these requests started to be established these companies internalized all collaborators, adding electronic and software know-how, transforming into C NPD firms.

Services completeness (declared/real)

Dimensions / internal skills

ID+ type II

IN IN IN IN

OUTOUT

OUT

(1)

(2)

C ID+ type I


49

Figure 2. NPD emerging clusters Secondly, Table 4 summarizes information about the clusters of companies as concerns the other organizational and managerial

factors, allowing a deeper understanding of their business model. Also on these factors, it is possible to appreciate a growing complexity and richness passing organizational factors by firms named C to the ID+, type I and II.

Companies belonging to cluster C are normally big firms with an articulated and complex organizational structure, whilst the other are usually smaller and, above all, showing an informal organization. Consequently, such “complete” firms can boast competences that are organized in functional units able to work together by sharing expertise along the whole NPD process. Thus, the range of services offered is very broad and the level of internal expertise is wide and deep, with advanced skills also in the electronic and software fields. The scope of these enterprises is generally international (in terms of both customers and competitors’ nature) and that is what differentiates them from others in particular, although the clients’ business sectors are usually very similar. In short, the complexity is really more and this is also evidenced by the longer duration of the undertaken projects. Moreover, although the mode of finding customers seem to be rather informal or quite traditional for all types of firms (i.e. personal acquaintances, word of mouth, marketing/trade dedicated employees) more sophisticated means of communication seems to be spreading (i.e. through Web site) in the “complete” companies. Finally, it is nevertheless interesting to note that the reputation of all the studied companies is strictly limited to professionals and there are very few cases where the brand is known and therefore perhaps rewarding (i.e. Pininfarina Extra and Zagato). However, this confirms the importance of enriching the empirical evidence on this type of service companies. As concerns the organizational mode of collaborations with customers, some factors (i.e. the broad set of objectives of collaboration covering all the NPD stages, as well as the content of collaboration, usually strategic for the customers of TSS companies in cluster C) seem to lead to integrated and formalized modes of collaboration. Thus, very often, a structured contract is defined where the main terms of the agreement require that TSS company is assigned a precise output, whose characteristics and functionality are clearly described in quantitative, measurable terms (i.e. timescales, costs and outputs of each TSS task). On the other hand, cultural distance with customers, also quite common, seems to lead in the opposite direction.

Other factors driving the choice of governance mode of collaboration (i.e. the emphasis on learning maximization from partners as well as the customer ‘s familiarity with the content of cooperation) show a different impact among the companies but they are not associable to a specific cluster. Thus we report them in table 4 among the analogies.

Table 4. Organizational and managerial features and firms clusters

Category C ID+ type I ID+ type II

Services offered

All NPD service are available (marketing, industrial design, mechanical design, electronics – software, engineering)

Complete NPD service, excluding electronics and software (marketing, industrial design, mechanical design, partially engineering)

Specialised in one or few services (among: partially marketing, industrial design, mechanical design, partially engineering)

Size Large Medium and small Medium and small

Competences Only internal Internal competencies are, in most of cases, complemented by a network of external expertise

Only internal

MA

JOR

DIF

FER

EN

CE

S

Competitors International NPD firms None or other local NPD firms Local specialised firms

Level of Internal skills

C

ID+

ID / TECH

(1)

(2)

Completeness of NPD services C firms

Electronic and SW skills

High Low

ID+ type I firms

ID+ type II firms


50

Table 4. (cont’d.) Organizational and managerial features and firms clusters Category C ID+ type I ID+ type II

Project average duration

More than one year (long term orientation) 2 – 10 months 2 – 8 months

Location of the clients International International and national Local

Project work organization

Hierarchical; internal inter-functional teams

Hierarchical or flat; internal inter-functional teams

Flat, inter-functional teams with client

MA

JOR

DIF

FER

EN

CE

S

Organizational mode of collaboration

− The broad set of objectives for collaboration leads to more integrated (than pure outsourcing) mode of cooperation

− The content of collaboration, often very critical in terms of competiveness for customers of these TSS companies, leads to high integration and high control level

− Cultural distance with international customers, common in this cluster, leads to reversible governance modes

− No specific requirements derived by customer’s size (similar to TSS)

− The broad set of objectives for collaboration leads to more integrated (than pure outsourcing) mode of cooperation

− The content of collaboration, often very critical in terms of competiveness for customers of these TSS companies, leads to high integration and high control level

− Impact of cultural differences is less evident

− No specific requirements derived by customer’s size (similar to TSS)

− The limited set of objectives leads to pure outsourcing

− The content of collaboration, not so strategic for customers of these TSS companies, does not require high control over activities and results

− Impact of cultural differences is not evident

− Bigger size of the customer (with respect to TSS companies) leads to more integrated and/or formal modes of collaboration

Clients business sectors

More concentrated (sport and medical equipments, household appliances, machinery, illumination, aerospace)

Broader (sport and medical equipments, cosmetics, household appliances, machinery, aerospace, automotive, yachts, motorcycles, telecommunication, toys, etc.)

Broader (sport and medical equipments, cosmetics, household appliances, machinery, aerospace, automotive, telecommunication, motorcycles, toys, etc.)

Clients searching Personal acquaintances, word of mouth, marketing/trade dedicated employees, Web site, presence at professional fairs

Personal acquaintances, word of mouth, marketing/trade dedicated employees, Web site

Personal acquaintances, word of mouth, professional fairs

MIN

OR

DIF

FER

EN

CE

S

Well-known brand No. Well – know only to professionals Only in few cases No

ANALOGIES Firm structure

Human resources management Knowledge management

Sale of NPD services and external communication Customer relationship management

Pricing Technical interaction with clients

Commercialisation phase Intellectual property management Performance measurement system

Project management Maximization of learning from partners leads to a low level of contractual formalization

Low customer’s familiarity with the content of collaboration leads to more integrated and formalized modes of collaboration

5. Conclusions, limitations and future research

In coherence with the main objective stated in the introduction, the paper illustrates a set of Italian firms offering services for New Product Development, from a firm-perspective. The business model adopted by these companies is described in terms of organisation, internationalisation and commercialisation strategy, organisation and management of the NPD projects. This result represents a step further in the literature that tries to study the emerging “market of technology” from a firm-perspective.

The analysis of the firms’ business models can be useful for firms operating in the NPD services market, since it increases the knowledge about this sector and identifies the main features and capabilities that should be developed to pursue such business models.


51

Moreover, it can be relevant for firms searching for services to support their internal NPD processes. As a matter of fact, they usually completely ignore which kind of services they may find available in the NPD service market and how these NPD companies actually carry out their business. In other words, a better knowledge of the NPD service companies may facilitate an efficient and effective relationship with their potential clients. The identification of clusters is particularly relevant from this point of view: each cluster clearly identifies a specific set of services offered and a definite approach to the management of the business, so that each company searching for external support can select the “set” of potential suppliers most adequate to its specific needs and characteristics.

The study obviously has some limitations. First, because of the adopted research methodology, results cannot be statistically generalised. Moreover, even if the internal validity of the empirical results is ensured by the cross-case analysis, the study does not explicitly take into account the effects that other contextual factors (e.g. industry features, appropriability regimes, TSS company’s strategy) are likely to have on the NPD services provision. Therefore, in terms of future research, the aim is threefold in order to deepen the study of:

• NPD services dedicated to specific sectors of activity, in order to better understand the impact of industry-specific characteristics on the development of services for NPD;

• NPD companies in different countries than Italy; • Firms covering one phase of the new product development (design companies, engineering companies, prototyping

companies etc.). These studies would offer the opportunity to conduct cross-industry and cross-country comparisons and to deepen the analysis of

the main organisational and managerial features highlighted in this paper. References Abetti P.A., 1989. Technology: a key strategic resource. Management Review, Vol. 78, No. 2, pp. 37-41. Arora A., Fosfuri A. and Gambardella A., 2001. Markets for Technology: the Economics of Innovation and Corporate Strategy.

MIT Press, Cambridge. Chiaroni D., Chiesa V., De Massis A. and Frattini F. 2008. The knowledge-bridging role of Technical and Scientific Services in

knowledge-intensive industries. International Journal of Technology Management, Vol. 41, No. 3-4, pp. 249-272. Chiesa V., 2001. R&D Strategy and Organization, managing technical change in dynamic contexts, Imperial College Press. Chiesa V. and Manzini R., 2000. Is there a market for technical knowledge? Workshop on innovation in the knowledge economy -

European Perspectives, Nijmegen Business School, 5-6 June. Chiesa V. and Manzini R., 2001. Innovation and the growing market for technical and scientific services. Workshop on

Management and Innovation Services, Maastricht, 5-6 April. Chiesa V., Frattini F. and Manzini R., 2008. Managing and organising technical and scientific service firms: a taxonomy and an

empirical study. International Journal of Services Technology and Management, Vol. 10, No. 2/3/4, pp. 211-234. Chiesa V., Manzini R. and Pizzurno E., 2004. The externalisation of R&D activities and the growing market of product

development services. R&D Management, Vol. 34, No. 1, pp. 65-75. Cooper R. G., 1994. Perspective - Third Generation New Product Processes. Journal of Product Innovation Management, Vol. 11,

No. 1, pp. 3-14. Cooper R.G., 2008. Perspective: The stage-gates idea-to-launch process—Update, what’s new, and nexGen Systems. Journal of

Product Innovation Management, Vol. 25, No. 3, pp. 213–232. Debackere K., 1999. Technologies to develop technology, Nijgmen Business School. den Hertog P. and Bilderbeek R., 2000. The new knowledge infrastructure: the role of technology-based knowledge-intensive

business services in national innovation systems, in Boden, M., Miles, I., Services and the Knowledge-Based Economy. Continuum, London.

Eisenhardt K.M., 1989. Building theories from case study research. Academy of management review, Vol. 14, No. 4, pp. 532-550. Emes M., Hughes I. and Smith A., 2005. Internal invention, external development, R&D Management Conference 2005, Pisa July

5-8. Folta T.B., 1998. Governance and uncertainty: the trade-off between administrative control and commitment, Strategic

Management Journal, Vol.19, pp.1007-1028. Ginsberg A. and Abrahamson E., 1991. Champions of change and strategic shifts: the role of internal and external change

advocates. Journal of Management Studies, Vol. 28, No. 2, pp. 173-190. Haden P., Sibony O. and Sneader K. 2004, New strategies for consumer goods. The McKinsey Quarterly, Web exclusive,

December 2004. Hargadon A. and Sutton R., 1997. Technology brokering and innovation in a product development firm. Administrative Science

Quarterly Dec. 1997 Vol. 42, No. 4, pp. 716-750. Hendry J., 1995. Culture, Community and Networks: The Hidden Cost of Outsourcing. European Management Journal, Vol.13,

No.2, pp.193-200.


52

Hermens A., 2001. Knowledge exchange in strategic alliances: learning in tension, Creativity and Innovation Management, Vol.10, No.3, pp.189-200.

Horgos D. and Koch A., 2008. The internal differentiation of the KIBS sector: empirical evidence from cluster analysis. International Journal of Services Technology and Management, Vol. 10, No. 2/3/4, pp. 190-210.

Howells J., 1999. Research and technology outsourcing. Technology Analysis and Strategic Management, Vol. 11, No. 1, pp. 17-29.

Jones O. and Stevens G., 1999. Evaluating failure in the innovation process: the micropolitics of new product development. R&D Management, Vol. 29, No. 2, pp. 167-176.

Jurgens U., 2000. New product development and production networks. Springer, Berlin. Kelly T., 2001. The art of innovation - lessons in creativity from IDEO, American’s leading design firm. Doubleday, New York. Knoben J. and Oerlemans L.A.G., 2006. Proximity and inter-organizational collaboration: A literature review. International

Journal of Management Reviews. Vol. 8, No. 2, pp. 71-89. Kotler P. 1997. Marketing management analysis: planning, implementation and control. Prentice Hall, New Jersey USA. Larsen J.N., 2000. Supplier-User Interaction in Knowledge-Intensive Businee Services: Types of Expertise and Modes of

Organization, in Boden, M., Miles, I., Services and the Knowledge-Based Economy. Continuum, London. Lazzarotti V. and Manzini R., 2009. Different modes of open innovation: a theoretical framework and an empirical study.

International Journal of Innovation Management, Vol. 13, No. 4, pp. 615-636. Lawson B., Petersen K.J., Cousins P.D. and Handfield R.B. 2009. Knowledge Sharing in Interorganizational Product Development

Teams: The Effect of Formal and Informal Socialization Mechanisms. The Journal of Product Innovation Management, Vol.26, pp.156-172.

MacPherson A.., 1997a. The contribution of external services inputs to the product development efforts of small manufacturing firms. R&D Management, Vol. 27, No. 2, pp. 127-144.

MacPherson A.., 1997b. The role of external technical support in the innovation performance of scientific instruments firms: empirical evidence from New York State. Technovation, Vol. 17, No. 3, pp. 141-150.

Miles I., 2000. Services innovation: coming of age in the knowledge-based economy. International Journal of Innovation Management, Vol. 4, No. 4, pp. 371-389.

Miles M.B. and Huberman A.M., 1984. Qualitative Data Analysis. Newbury Park: Sage. Muller E. and Zenker A., 2001. Business services as actors of knowledge transformation: the role of KIBS in regional and national

innovation systems. Research Policy, Vol. 30, No. 9, pp. 1501-1516. Ojasalo J. 2008. Management of innovation networks: a case study of different approaches. European Journal of Innovation

Management, Vol.11, No.1. Quinn J.B., 1999. Strategic outsourcing: leveraging knowledge capabilities. Sloan Management Review, Vol. 40, No. 4, pp. 9-21. Quinn J.B., 2000. Outsourcing innovation: the new engine of growth. Sloan Management Review, Vol. 41, No. 4, pp. 13-28. Rundquist J. and Chibba A. 2004. The use of processes and methods in NPD: a survey of Swedish industry. International Journal

of Innovation and Technology Management, Vol. 1, No. 1, pp. 37-54. Strambach S., 2008. Knowledge-Intensive Business Services KIBS as drivers of multilevel knowledge dynamics. International

Journal of Services Technology and Management, Vol. 10, No. 2/3/4, pp. 152 -174. Tidd J., 1995. Development of novel products through intraorganizational and interorganizational networks. Journal of Product

Innovation Management, Vol. 12, No. 4, pp. 307–322. Trott P., 2008. Innovation management and new product development. Prentice Hall, New Jersey. Urban G.L. and Hauser J.R., 1993. Design and Marketing of New Products, 2nd edition. Prentice Hall, Upper Saddle River USA. van de Vrande V., Lemmens C. and Vanhaverbeke, W., 2006. Choosing governance modes for external technology sourcing. R&D

Management, Vol. 36, No.3., pp.347-363. Varela J. and Benito L. 2005. New product development process in Spanish firms - typology, antecedents and technical marketing

activities. Technovation, Vol. 25, No. 4, 395–405. Verganti R., 1997. R&D Management. Blackwell Publishers, Oxford. Veugelers R. and Cassiman B., 1999. Make and Buy in innovation strategies: evidence from Belgian manufacturing firms,

Research Policy, Vol. 28, No. 1, pp. 63-80. Williamson O.E., 1985. The Economic Institutions of Capitalism: Films, Markets, Relational Contracting. New York, NY: Free

Press. Windrum P. and Tomlison M., 1999. Knowledge-intensive Services and international competitiveness: a four-country comparison.

Technology Analysis and Strategic Management, Vol. 11, No. 3, pp. 391-405. Yin R. K., 2003. Case study research, design and methods. 3rd ed. Sage Publications, Thousand Oaks. Zenker A. and Doloreux D., 2008. KIBS, perceptions and innovation patterns. International Journal of Services Technology and

Management, Vol. 10, No. 2/3/4, pp. 337-342.


53

Biographical notes Valentina Lazzarotti is Assistant Professor at the Faculty of Engineering of University Carlo Cattaneo – LIUC. She teaches Business Economics and Organization and Management Control Systems at LIUC. She obtained her Master Degree in Business Administration from Bocconi University. Her research interests concern R&D performance measurement and accounting for innovative activities. She has published papers in international journals such as International Journal of Innovation Management and Project Management Journal. Emanuele Pizzurno, PhD, is currently Assistant Professor in the management area at the Faculty of Engineering of the University Carlo Cattaneo - LIUC where he developed most of his academic activities. He is also research fellow and professor at the Scuola Mattei - Eni Corporate University and at the Department of Management Engineering at Politecnico di Milano. The major research and teaching topics concern innovation and technology management and the organization of R&D; he has also long studied environmental strategies and management. On these issues, he is the author of several scientific publications. Received June 2010 Accepted November 2010 Final acceptance in revised form November 2010

MultiCraft


Vol. 2, No. 9, 2010, pp. 54-68



www.ijest-ng.com


Use of Web 2.0 applications in product development: an empirical study of the potential for knowledge creation and exchange in research

and development

K.-I. Voigt1, M. Ernst2*

1 Chair of Industrial Management, University of Erlangen-Nuermberg, GERMANY 2* Chair of Industrial Management, University of Erlangen-Nuermberg, GERMANY

*Corresponding Author; e-mail: [email protected], [email protected], Tel. +49-911-5302-244/-233, Fax. +49-911-5302-238

Abstract Over the past few years the common usage of the internet has dramatically changed towards the so-called Web 2.0. That means a fundamental change of the principles of creating and processing information in the internet. Companies have started to benefit from the emerging chances. Previous researches concentrate on studying of the usage of Web 2.0 tools within companies in general. But the concept of Web 2.0 contains also a great potential for the internal use of such applications, which has not been investigated yet. The potential of the internal integration of employees across several organizational units is important in particular for the process of innovation, especially in research and development. This paper examines the potential of an internal use of Web 2.0 applications for generating and sharing knowledge. An empirical study shows the status quo of the corporate use of Web 2.0 tools within companies. The study evaluates the quality of the content and the frequency of corporate use of Web 2.0 applications as well as the integration of those applications in daily business. Furthermore, the study analyzes the impact of internally used Web 2.0 applications in research and development departments of companies, especially on the emergence of innovations. Keywords: Web 2.0, Enterprise 2.0, knowledge management, research and development (R&D), innovation management, product development 1. Introduction

Because of its characteristics to bring new products to market, innovation management is a very sensitive issue concerning the competitiveness of a company (Vahs and Burmester, 2005). Recently, the so-called “Web 2.0” has become an essential part of internet usage, especially in the private sector (von Krotzfleisch et al., 2008). The resulting change of fundamental principles of editing and processing information concerning aspects like openness, exploration and community (Kranz et al., 2009) is getting more and more important for companies. Thus, Web 2.0 applications are diffusing into ventures continuously and are rapidly establishing themselves in corporate processes as well (Bilgram et al., 2008; McAfee (b), 2009). A corresponding use of such applications seems to be in particular promising in research and development (R&D). The availability of corporate knowledge is of considerable importance for the realization of competitive advantage, not least concerning product development as being an essential part of technology and innovation management (Krause et al., 2007). In this context, Web 2.0 applications are suited in particular to support corporate project work, for example by reducing development team complexity or by further developing the corporate knowledge base, due to their characteristics concerning the linking-up of users or content (Schachner et al., 2009).

Several recent studies deal with the use of Web 2.0 applications by companies, but do not focus on product development, respectively the area of R&D. In this paper, we evaluate the status quo of corporate use of Web 2.0 applications especially in this field. Therefore, we point out the importance of knowledge, respectively knowledge management in R&D (Brem and Voigt, 2009). In the next step, we deduce a conceptual framework of the implementation and suitability of corporate use of Web 2.0 tools. Building on this framework, we present our findings of an empirical study evaluating the corporate use of Web 2.0 applications.

Voigt and Ernst / International Journal of Engineering, Science and Technology, Vol. 2, No. 9, 2010, pp. 54-68

55

After that, the results are discussed in detail and managerial implications are derived. The paper concludes with a short summary of the essential findings and some perspectives for further research.

2. Importance of knowledge in product development – conceptual background

The essential basis of competitiveness of a company is the ability of creating and implementing innovations successfully in the

market (Vahs and Burmester, 2005). In this context, companies among all branches have to deal with an increased importance of time-to-market as a result of the acceleration of competition. The faster a company can realize innovations, the bigger the time lead in the market and the higher the competitiveness (Voigt, 1998; Geulen, 2006). As a consequence, corporate activities have to be focused consequently on the continuous development and placement of market-conform innovations. Besides, empirical studies from practice show that companies, whose innovation processes are organized effectively and efficiently, are more successful than companies without well structured innovation processes (Reichart and Reichart, 2006).

In practice, product development is marked by a geographical, temporal and topical decoupling. As a result of globalization, the development of new products occurs more and more at different locations and in different time-slots. Additionally, the increasing complexity of the development task demands cooperation and collaboration in interdisciplinary (project) teams (Berkhout et al., 2006; Borchert and Hagenhoff, 2004). To establish innovation successfully in the market, companies are increasingly forced to conduct their innovation tasks within networks (Borchert and Hagenhoff, 2004).

Therefore the opening of the innovation processes outwards – for example for the inspiration for new ideas – is as important as the freedom for employees to realize their ideas and to share their knowledge (Ganswindt, 2006). Only the connection of the knowledge of all employees involved in order to foster a common understanding of a new idea enables the emergence of innovations (Neyer et al., 2008). Knowledge and also the exchange of knowledge do represent potential sources of competitive advantages (Bach and Homp, 1998; Boos et al., 2008). Through the active use of the once financed knowledge acquisition or the multi-usage of knowledge, an added value for the company can be created (Reinhold and Michel, 2007). As a result of the usage or further development of the existing knowledge for the given task – in the context of new product development – the existing capacities can be focused on the development of new products (Krause et al., 2007). The sooner knowledge is available along the product development process, the more efficient one can react on changes of the task (Ehrlenspiel, 2005). That means that for product development, the continuous availability of knowledge is of essential importance. Because of that, the management of corporate knowledge does also play a central role. The main challenges are to exploit, to structure, to retrieve and to make available knowledge concerning either products or processes and so exploit unused potentials. Thus, the process of product development can also be seen as a process of knowledge creation (Madhavan and Grover, 1998).

In recent scientific discussions, there is a gap concerning operational instruments of managing innovation networks, which support an effective and efficient achievement of strategic goals (Borchert and Hagenhoff, 2004). So tools are needed, which are able to reverse the geographical, temporal and topical decoupling of product development and support in the same way the linking-up of employees and content. Our paper presents the characteristics of Web 2.0 applications and shows their inherent potential to close or at least to reduce this gap. 3. Web 2.0 within companies

The term “Web 2.0” is not clearly defined and classified in scientific discussion (Langham, 2007). According to current scientific considerations, there are several points of view concerning the distinction of terms. As a consequence the distinction between single applications is not consistent in classification and definition (Bohl and Manouchehri, 2008). Nevertheless, the change in the way of interacting and communicating amongst users can be considered as a common aspect of all these different points of view (Cook, 2008). This can be summarized with the catchphrase “the internet to join” (Hage, 2006). Thus, Web 2.0 tools denote web-based applications, which do not contain a centralized administration and are characterized in particular through the interaction and participation of users (Tapscott and Williams, 2006; Beck, 2007; McAfee (b), 2009). Moreover, those tools encourage a so-called “many-to-many” communication (Gouthier and Hippner, 2008) and change the way of creating, organizing, searching and distributing of information (Hirsch et al., 2009).

Since the end of 2006, the term Web 2.0 has increasingly been used in conjunction with internal corporate processes, concerning internal collaboration and communication regarding especially the exchange of knowledge (N.N.(a), 2006). In general, in current literature, the term “enterprise 2.0” describes the use of Web 2.0 applications within companies (McAfee, 2006). Thus, this term summarizes applications from the internet, which are – due to diffusion processes from private to corporate use – increasingly applied by companies (Lochmaier, 2007). Accordingly, the term “enterprise 2.0” represents different concepts of a new kind of collaboration, fostered by the networking of employees and converted by technological components of Web 2.0 (McAfee, 2006; Zimmermann, 2007; McAfee (b), 2009).

This trend is confirmed by several recent studies: In a study of BITKOM1 in 2008 almost half of the surveyed companies indicated that they had been facing up with the topic Web 2.0 since 2006/2007 (Weber, 2008). According to a McKinsey study

1 Federal Association for Information Technology, Telecommunications and New Media


56

from 2009, 69% of the evaluated ventures are able to register at least one measureable success of their business activity through the use of Web 2.0 (Bughin et al., 2009). Main goal of these enterprises is supporting business activities by the work- or process-oriented provision of knowledge (N.N.(b), 2006). In the long run, companies try to support the linking-up between employees (involved) as well as the linking-up of the content produced by them. Thereby, companies aim to supply their staff with the right piece of information at the right time (Bächle, 2008). The three main aspects that contribute to a maximum of benefit pointed out by the companies evaluated in 2009, is the accelerated access to knowledge (68%), the reduction of communication costs (54%) and the possibilities of faster getting in contact with corporate experts (43%) (Bughin et al., 2009).

Figure 1 shows an overview of different applications of the Web 2.0. For the analyses in this paper, we have chosen those applications, which have – according to recent literature on the one hand (for example Koch/Richter 2007, Back et al., 2008, Weber, 2008) and to a qualitative pre-research of internal use of Web 2.0 (Ernst, 2009) on the other hand – the highest potential to support product development.

Figure 1: Technologies and applications in the context of Web 2.0

However, the studies mentioned above do focus on the use of Web 2.0 applications by companies in general and do not

distinguish between the business areas, in which these tools are used or whether it is an external or internal use or a combination of both across company boundaries (for example Bughin et al., 2008). Given the particular suitability of Web 2.0 applications in product development, we evaluate in this study the internal use of Web 2.0 tools within companies especially in product development in order to close the existing gap in research and to contribute to the scientific discussion. Because the process of product development can hardly be isolated, we have focused our analyses on the R&D department being linked closely to the process of product development itself.

4. Potentials of Web 2.0 applications

4.1 Overview Through the increasing integration of Web 2.0 applications into corporate processes, the cross-departmental and even the cross-

venture integration of employees in the context of knowledge management can be supported in order to encourage the genesis of innovations (Lattemann et al., 2009). The classic role allocation of knowledge management between authors and the target group of this kind of systems (Komus and Wauch, 2008) has been abolished due to Web 2.0 applications (Grossmann and McCarthy, 2007). In an ideal matter, a corporate-wide or even network-wide transfer of knowledge can occur though the disposal of knowledge as well as the consumption of knowledge by every employee involved (Carr, 2004; Gölz, 2007). By a recombination of subject-specific knowledge of employees of different organizational departments or even different network nodes within collaboration and communication processes, a crucial contribution to the success of innovation can be achieved (Kranz et al., 2009) – stimulated by the so-called “power of the group” (Hideyuki, 2007, p. 65).

Structured content constitutes the basis of navigation in and provision of knowledge (Sandkuhl, 2005). Web 2.0 applications are especially appropriate for structuring content through the possibilities of linking unstructured data – as they do occur in particular in early stages of product development for example as a result of unstructured search in idea creation (Koen et al., 2002). An internal use of appropriate Web 2.0 tools can offer several potentials for integrating organizational units of companies. As a result of the social characteristics of these tools, they support the creation and expansion of the user-networks (Gouthier and Hippner, 2008; Komus and Wauch, 2008).

4.2 Characterization of Applications The selected Web 2.0 applications (Figure 1) can be categorized by reference to several criteria. Besides the creation of content,

the possibility of editing existing content by other users is an important characteristic concerning the evaluation of different tools (Beck, 2007). Furthermore, it is also important, that content can be retrieved user-independently. Another aspect is the linking-up of users – e.g. employees within a company as well as employees between networked ventures – as well as the linking-up of

WEB 2.0

Wikis

Blogs

Social Networks Social Tagging

Twitter

Instant Messaging

RSS Podcasts

Virtual Worlds

Prediction Markets

Webservices

Mash-Ups

Social Bookmarking

AJAX


57

content as one of the central ideas of Web 2.0. Taken all together, these four aspects do have a positive influence on the utilization of corporate knowledge. Figure 3 shows a comparison of the selected applications.

Editing of content, which has been provided by other users, is very easy in Wikis (McAfee (a), 2009). In Blogs – because of the assignment to one single author – editing is only possible in an indirect way through comments. Concerning Social Networks this effect can only be realized by the creation of additional groups within the application, e.g. in a forum. Content in Instant Messaging or Twitter cannot be edited by other users, so that Wikis are the only class of applications, in which this is possible in an easy way. Content can be retrieved in case of Wikis, Blogs or Social Networks or at least Twitter either by the use of a tag-cloud or by the use of included search mechanisms. Within Instant Messaging, there is no way to retrieve content and even storing content is only possible with considerable effort (Koch and Richter, 2007). The linking-up of users via Blogs can happen through comments (trackbacks, pingbacks or permalinks) or responding Blog posts. Concerning the operating principle of Wikis, an assignment to the respective reference person of certain topics is not as intuitive as in Blogs – the same as it is in Instant Messaging applications. Within Social Networks and Twitter, the linking-up of the users occurs by the use of the application by fostering the personal, e.g. user-specific network, so that these tools are suited best for connecting people (Koch and Richter, 2007). Concerning the content of Social Network applications, linking-up is not possible in all of them. Instant Messaging is not suited, because content is not easy to store and communication is the main focus. Within Social Networks, the main goal is to link people together and so the linking-up of content is mostly bound to individuals. Concerning Blogs and Twitter, content can be linked-up in a similar way as authors referring to previous entries. Wikis are suited best to link-up content due to the use of links between different Wiki articles via key-words (Alby, 2007). Through an internal use for example in project teams, versioning of several documents with several authors can be handled easily (Newman and Thomas, 2009).

Figure 2: Evaluation of the selected Web 2.0 applications

Taking everything into consideration, Web 2.0 applications contain several potentials to support corporate knowledge

management. So these tools can support research in teams or with partners (Bullinger et al., 2010) and so potentially encourage the realization of innovations. The use of Web 2.0 applications in order to integrate knowledge and therefore employees seems particularly appropriate within the context of R&D. In the following, we evaluate the status quo of the internal use of Web 2.0 tools within companies in their R&D department. It should be examined, if the potentials mentioned above have already been realized in companies. Therefore, we try to answer the question of how Web 2.0 applications are used in daily business of product development. Furthermore, we evaluate the content of the applications and analyze how the content is judged and assessed by the respondents.

5. Empirical Evaluation

5.1 Research Design The aim of this paper is to close the gap in existing studies, which do not deal with the internal use of Web 2.0 applications in

R&D. We investigate in which ways employees can be supported through the use of Web 2.0 tools in linking-up with others in order to collaborate in knowledge creation and exchange.

Therefore, in a first step, we have conducted an explorative, qualitative expert survey in order to find out, what kind of peculiarities are to be taken into consideration concerning corporate implementation and use of Web 2.0 applications. For that reason, we have conducted 13 interviews with experts who have to deal with this topic being in an appropriate management position. The interviews have been conducted in software and industrial enterprises. Software companies have been chosen because they can be seen as pioneers concerning the internal use of such applications. Industrial companies have been chosen

Editing ofContent

Retrival of Content

Linking-up of Users

Linking-up of Content

Blogs O X X X

Wikis X X O X

Social Network O X X O

Instant Messaging - - O -

Twitter - O X X

Legend: X = well suited, O = average suited, - = not suited


58

because of the particular potential of support by Web 2.0 tools: their product structures being characterized by high complexity lead to special requirements for product development concerning interdisciplinary or development team complexity. Based on the findings of this pre-investigation (Ernst, 2009), the design of the further research has been developed.

5.2 Methodology For the implementation of the following quantitative research, an online questionnaire has been designed. The development

procedure of this questionnaire can be separated into five steps. First, it had to be decided about the types and content of the single questions and then they have been formulated in detail. In the third step, the order of the questions and so the structure and final layout of the questionnaire have been determined. In the last step, several pre-tests had been conducted (Wilson, 2003) and the whole questionnaire has been modified and finally finished (Homburg and Krohmer, 2006).

The standardized questionnaire has contained the same questions in the same order with the same possible answers for all participants (Seipel and Rieker, 2003). The questionnaire has contained apart from the introduction three blocks of questions. The first section served the capturing of data concerning the company of the participant and the deployed Web 2.0 applications. In the second section there have been questions about the use of the offered Web 2.0 tools in daily business and in the last section, there have been questions about the evaluation of an explicit contribution of the applications to innovation management.

The questionnaire has consisted of objective answering questions of properties (Böhler, 1992) and subjective questions of attitudes or opinions (Berndt, 1996; Berekoven et al., 2001). Questions of properties serve for the description of important characteristics of the examination unit (for example branch or size of enterprise) or its functional areas (Böhler, 1992). Questions of attitudes or questions of opinions identify on the one hand a common evaluation of areas of interest and on the other hand the implementation of these areas in the surveyed company (Berndt, 1996; Berekoven et al., 2001).

Berekoven et al. propose the use of rating scales to measure attitudes or opinions because of their simple application (Berekoven et al., 2001). The main objective of scaling is to make theoretical aspects quantifiable, which are not observable directly. In order not to overcharge the ability of discrimination of the respondents, there should be predefined four to seven stages. The addition of alternative categories (“I can’t really say”, “no answer”) is not seen as mandatory by Berekoven et al. (Berekoven et al., 2001). Besides rating scales, likert scales have been used within the survey in order measure attitudes of the participants2 (Albers, 2007).

6. Results

We have asked in total 497 people from companies in Germany out of different branches to participate in the survey, on which

this paper is based on, in autumn 2009. We have contacted the participants via internet in a personal mailing. As selection criteria we have used the individual work experience in dealing with Web 2.0 applications on the one hand and on the other hand we have chosen the professional work of the participants in the area of Enterprise 2.0, Web 2.0, product development, project management, technology management or innovation management for selection. The participation rate has reached 45.8% with 174 participants and a termination rate of 23.2%.

In total, companies of all magnitudes have been questioned (n=141), whereby the majority of the participants (39.3%) worked in companies with one to 100 employees during the past business year. 15.6% worked in companies with 101 to 500 employees and 31.2% in companies with more than 1,000 employees.

Concerning the Web 2.0 applications being used, we state that within the surveyed companies (n=116) Wikis are mostly used (84.5%) followed up by Instant Messaging applications (76.7%), Blogs (65.5%) and Social Networks (51.7%). Twitter or a comparable tool have already been in use within 37.1% of the surveyed companies. These results go in hand with the assumptions derived from our previous qualitative survey.

The investigated Web 2.0 applications are used within the surveyed companies in several departments (n=109). Among the participants of the study, Wikis are mostly used in R&D (R&D, 53.8%). The second most common use in the R&D sector concerns Blogs (32.3%) and Instant Messaging tools (29.0%) followed up by Twitter respectively comparable tools (24.1%) and finally Social Networks (23.9%). As some authors in recent literature suggest, the use of Web 2.0 applications in other departments is of subsidiary importance (Figure 3).

2 Attitude is measured through several statements, which are to be evaluated by the participants in a continuum reaching from especially positive to especially

negative, for example „applies completely“ to „doesn’t apply at all“ (Albers, 2007).


59

Figure 3: Distribution of different Web 2.0 applications

The distribution of the use of the applications in R&D is similar to the distribution of the use among the rest of the corporate

departments, but the frequency of use of the single tools is distributed towards some single items (Figure 4). In 44.0% of the cases in R&D, Blogs are only used occasionally. In contrast, Wikis are more often used frequently (30.3%) or very frequently (23.3%) in daily business. In total, more than three-quarter of the respondents are using Social Networks occasionally (27.3%), frequently (27.3%) or very frequently (36.3%). Instant Messaging is used very frequently by 60.6% of the respondents in R&D. Twitter or comparable applications are used in equal parts occasionally, frequently or very frequently with 28.6% each.

Figure 4: Frequency of corporate use of Web 2.0 applications in R&D

0%

10%

20%

30%

40%

50%

60%

70%

80%

Blogs Wikis Social Network

Instant Messaging

Twitter

gesamt %

FuE %

Blogs Wikis Social Network

Instant Messaging Twitter

overall mentions 62 80 46 69 29% 56.9% 73.4% 42.2% 63.3% 26.6%

R&D mentions 25 43 11 20 7% 40.3% 53.8% 23.9% 29.0% 24.1%

overall %

R&D %

How often do you use the offered applications in daily business?

never seldom sometimes frequently very frequently nBlogs

overall 8.7% 15.9% 34.8% 26.1% 14.5% 69in R&D 8.0% 8.0% 44.0% 32.0% 8.0% 25

Wikisoverall 2.3% 14.6% 23.6% 29.2% 30.3% 89in R&D 4.7% 16.3% 25.6% 30.2% 23.3% 43

Social Networkoverall 1.9% 9.4% 20.8% 24.5% 34.4% 53in R&D 0.0% 9.1% 27.3% 27.3% 36.4% 11

Instant Messagingoverall 1.2% 9.8% 9.8% 18.3% 61.0% 82in R&D 0.0% 15.0% 15.0% 10.0% 60.0% 20

Twitteroverall 13.2% 10.5% 23.7% 26.3% 26.3% 38in R&D 0.0% 14.3% 28.6% 28.6% 28.6% 7


60

Besides the frequency of the use of Web 2.0 applications, the associated objectives of the companies respectively the users are of

great importance. The collection of knowledge is mostly connected with Blogs, Wikis and Twitter. Concerning the generation of knowledge, the respondents assign the highest potential to Twitter and Blogs. To bring in ideas into corporate processes and to develop them further, Social Networks are considered to be most target-aimed by the respondents (Figure 5).

Figure 5: Objectives associated with the corporate use of particular Web 2.0 applications

The duration since Web 2.0 applications have been used within companies could possibly have an influence on the frequency,

the tools are used in daily business. The respondents had to answer two questions concerning this topic: On the one hand, the frequency of use has been questioned, on the other hand, we have asked for the time frame since the offered applications have been deployed within the company. In order to analyze these connections and to draw robust conclusions, an originally five-step scale has been reduced into a three-step scale. Thus we can show that in R&D Web 2.0 applications have been most frequently used since two to three years. Thereby most of the respondents are using them occasionally or frequently in daily business (32.6% each). If Web 2.0 applications have been used since four or more years within the company, most of the respondents are using them very frequently in daily business (Figure 6).

3,00 3,50 4,00 4,50 5,00

mean

Blogs

Wikis

Social Networks

Instant Messaging

Twitter

The applications allow the collection of knowledge.

Through the applications knowledge becomes more dynamic and relevant.

The applications serve the set-up of a reference book such as a glossary.

The applications are an instrument to create new ideas.

The applications are an instrument which allows me to participate in the further development of my colleagues’ ideas.

indifferent true completely true

nBlogs=25, nWikis=43, nSocialNetworks=11, nInstantMessaging=20, nTwitter=75-step scale from „does not fit at all“ (1) to „completely true“ (5)


61

Figure 6: Frequency of use of Web 2.0 applications depending on the duration of corporate use in R&D

This frequency is mirrored in particular in the use of Wikis in R&D (Figure 7). If Wikis have been used since four or more

years, in most of the surveyed companies they are used very frequently. But even after two to three years of corporate use of Wikis, they are already frequently in use (36.8%). An analysis of the use of the rest of the considered applications depending on the duration of use is not significant because of the poor database and is therefore not regarded further.

Figure 7: Frequency of use of Wikis depending on the duration of corporate use in R&D

In R&D, content distributed by Web 2.0 applications is mainly for informational purpose in the meaning of a reference book e.g.

a glossary (Figure 8). This is especially the case of the use of Blogs (50.0%), Wikis (31.6%) and Social Networks (40.0%). Communication by Instant Messaging is seen as essential for daily business respectively for the maintenance of business activity by most of the respondents (47.4%). Content, which is exchanged in Twitter, is mainly project specific (40.0%).

Figure 8: Content within different Web 2.0 applications in R&D

How often do you use the applications in daily business?

Since when have the mentioned applications been used in your company?

since less than/exactly

1 year

since 2 or 3 years

since at least 4 years n

never 0.0% 9.3% 0.0%

106

seldom 32.4% 4.7% 3.2%occasionally 23.5% 32.6% 22.6%frequently 17.6% 32.6% 29.0%very frequently 26.5% 20.9% 45.2%

How often do you use the applications in daily business?

Since when have the mentioned applications been used in your company?

since less than/exactly

1 year

since 2 or 3 years

since at least 4 years n

never 0.0% 10.5% 0.0%

43

seldom 45.5% 10.5% 0.0%occasionally 27.3% 26.3% 23.1%frequently 9.1% 36.8% 38.5%very frequently 18.2% 15.8% 38.5%

The content is mainly…

necessary for daily business/ the

maintenance of business activity

purely informative such as a glossary

specific for certain projects

All the answers are true. n

Blogs4.5% 50.0% 13.6% 31.8% 22

Wikis7.9% 31.6% 21.1% 39.5% 38

Social Network10.0% 40.0% 30.0% 20.0% 10

Instant Messaging47.4% 21.1% 26.3% 5.3% 19

Twitter20.0% 20.0% 40.0% 20.0% 5


62

Concerning the quality of the content in Web 2.0 applications, 27.3% of the respondents report that it was very high in Blogs. In

contrast to that, 40.9% state that quality varies according to specific use cases or information needs of the user. In the case of Wikis, users are much more convinced of the quality of the content. In our survey, 30.8% report content of very high quality and 28.2% content with high quality in their corporate Wikis. Only 15.4% experience different quality depending on specific use cases. The quality of content communicated through Social Networks is classified average or depending on specific use cases by 40.0% of the respondents each. In accordance to that, the quality of content in Instant Messaging tools is equally seen as average or depending on specific use cases (26.3% each) or even high (21.1%). The quality of content within Twitter or comparable applications is assessed high by 40.0% of the respondents. This shows that content, which is distributed or exchanged via Wikis in R&D, does have the highest quality perception (Figure 9).

In order to ensure this kind of quality, the surveyed companies have taken different measurements. The most frequent stated measurement (78.2%) is the responsibility of the authoring employee to ensure the quality of content. Self-cleaning properties of the corporate community (45.5%) play also an important role in this context as well as the appointment of an employee, who is responsible for certain areas of content, for example a so-called “Wiki gardener” (34.5%). Other measurements like editorial teams or the evaluation of content by users are of little significance among the used applications.

Figure 9: Evaluation of quality of the content within Web 2.0 applications

Asked for the liability of the contained content, respondents state the following: In case of 37.7% of the surveyed companies, the

content is fully liable. 18.9% of the respondents report that content is liable after consulting the author and 3.8% have to talk to a superior. In 22.6% of the cases Web 2.0 applications are used in R&D, content is only informative but never liable. But also 17.0% of the respondents have not yet considered liability at all.

Through an enhanced use of Web 2.0 applications, established applications could possibly be substituted. In our survey, most of the respondents answer that there is no substitution (32.7%), but that Web 2.0 applications are a useful addition to existing applications. Nevertheless, 26.9% are of the opinion that mainly telephone calls and e-mail applications are substituted by the use of Web 2.0 tools. Others point out that it is rather e-mail applications (17.3%) or rather telephone calls (13.5%), which are substituted by Web 2.0 tools (Figure 10).

The quality of the content within the applications is…

very low low average high very high depends on use case/ need of information n

Blogs4.5% 4.5% 9.1% 27.3% 13.6% 40.9% 22

Wikis5.1% 5.1% 15.4% 28.2% 30.8% 15.4% 39

Social Network0.0% 0.0% 40.0% 10.0% 10.0% 40.0% 10

Instant Messaging5.3% 5.3% 26.3% 21.1% 15.8% 26.3% 19

Twitter0.0% 20.0% 20.0% 40.0% 0.0% 20.0% 5


63

Figure 10: Substitution of established applications by the use of Web 2.0 applications

Scientific literature suggests – as we show above – that in particular in R&D the use of Web 2.0 has a positive influence on the

generation of new ideas. In our survey, 33.3% of the respondents, who are using Blogs in R&D (n=55), support this statement. In 14.3% of the cases, a considerable increase in the number of new ideas can be observed. In contrast to that, 28.6% of the respondents cannot measure any changes in the number of new ideas.

This picture remains the same regarding every single application. In 41.7% of the cases of using Wikis, the number of generated ideas increased, but simultaneously 33.3% of the respondents report that there is no change in this number. The use of Instant Messaging has led to more ideas in 33.3% of the cases, but also 27.8% do not support this proposition and 20.0% cannot make a statement at all. Employees of companies, where Twitter is used in R&D, report mostly no change in the number of ideas (50.0%) followed up by 25.0% who record a very strong increase of newly generated ideas. If Social Networks are used in R&D, 40.0% of the respondents state that the number of new ideas has increased by the use of Web 2.0 applications and even 30.0% think that there has been a very strong increase (Figure 11).

Figure 11: Change in number of new ideas due to corporate use of Web 2.0 applications

Does the use of Web 2.0 applications lead to a substitution of existing and established applications within your company respectively within your personal daily business?

n = 52

No, Web 2.0 applications are a useful addition to existing applications. 32.7%

Yes, telehone calls and e-mails have been substituted. 26.9%

Yes, mainly e-mails have been substituted. 17.3%

Yes, e-mails and MS-Office applications have been substituted. 15.4%

Yes, mainly phone calls have been substituted. 13.5%

Yes, e-mails and MS-Office applications have been substituted. 3.8%

Yes, mainly MS-Office applications have been substituted. 9.6%

Yes, e-mails and MS-Office applications and phone calls have been substituted. 9.6%

Another kind of substitution can be observed. 9.6%

No, there is no relation between Web 2.0 applications and existing applications. 7.7%

Through the use of Web 2.0 applications the number of new ideas has been…

extremely reduced reduced uninfluenced increased significantly

increased

I cannot answer this

question.n

Blogs

0.0% 4.8% 28.6% 33.3% 14.3% 19.0% 21Wikis

2.8% 0.0% 33.3% 41.7% 2.8% 19.4% 36Social Network

0.0% 0.0% 10.0% 40.0% 30.0% 20.0% 10Instant Messaging

0.0% 5.6% 27.8% 33.3% 5.6% 27.8% 18Twitter

0.0% 0.0% 50.0% 0.0% 25.0% 25.0% 4


64

Concerning the discussion of new ideas within the company, half of the respondents, who are using Blogs in R&D (52.4%), are of the opinion that the discussion of new ideas has become more transparent respectively enforced and/or more controversial due to corporate use of Blogs (Figure 12). The respondents using Wikis support this statement in 47.2% of the cases and so do 50.0% of the ones, who are using Social Networks. Concerning the use of Twitter in R&D, the respondents think that the discussion of new ideas has intensified (50.0%), respectively has been made possible at all (50.0%).

In contrast to that, the use of the other applications does not affect the discussion of new ideas. Most of the respondents (38.9%) state that the use of Instant Messaging has not led to a change in the way, new ideas are discussed.

Figure 12: Change in the way of discussing new ideas due to the corporate use of Web 2.0 applications

As last question, we evaluated the assessment of the respondents concerning the impact of the corporate use of Web 2.0

applications on the speed of innovation of their company (n=49). 46.9% of the respondents, who use at least one Web 2.0 tool in R&D, state that the speed of innovation has increased due to the use of Web 2.0 applications within their company. 30.6% report that there has not been any change of the speed of innovation and 20.4% cannot make a statement about this topic.

7. Discussion and Managerial Implications

The empirical results show that Web 2.0 applications can absolutely be used to support product development. At first among

others, this can be deduced from the frequency of the use of Web 2.0 applications in R&D in comparison to other corporate departments. Nevertheless, Web 2.0 applications are used in a disproportionally frequent way among smaller or large companies. According to other studies (Seegmüller, 2008) the corporate use of Wikis and Blogs is most frequent, which is reflected in our results for the R&D department as well.

The assumed objectives of a corporate use of Web 2.0 tools in order to support product development have been confirmed by most of the respondents. The focus however varies with the use of specific applications. Overall, the highest agreement has been reached regarding the target of collecting corporate knowledge, which is also consistent with the identified suitability for knowledge management in current literature (Buhl, 2008). The study shows that this objective is mostly connected with the use of Blogs and Twitter. If companies want their employees to externalize their tacit knowledge, Blogs might be suitable applications because of the various posts being characterized by personal experience and knowledge of the authors.

In this context, it is interesting to compare the goals of the corporate use with the content of corporate Web 2.0 applications. This can give deeper insights in the real use of Web 2.0 tools in daily business. Especially the goal of building up a glossary is least mentioned, but in contrast to that, most of the respondents report that a high percentage of the content is only informative in the sense of a reference book. A possible explanation could be the imbalance between active authors and passive users (Töpfer et al., 2008). The first group contributes actively in creating and editing content, following the principle of participation, whereas the second group only participates passively as consumers of content. This would explain the perception of the offered Web 2.0 applications being a reference book. Companies or at least managers should encourage their employees to contribute actively to create content of the applications and there should be a clear commitment to the use of such applications in daily business on both sides, the (top-)management and the employees. Nevertheless, the employees should get involved voluntarily and not be forced to do so.

Through the application the discussion of new ideas has been…

made more difficult uninfluenced

more transparent, more intense and/or more controversial

made possible at all

I cannot answer this question. n

Blogs4.8% 23.8% 52.4% 4.8% 14.3% 21

Wikis2.8% 19.4% 47.2% 8.3% 22.2% 36

Social Network0.0% 20.0% 50.0% 20.0% 10.0% 10

Instant Messaging0.0% 38.9% 33.3% 11.1% 16.7% 18

Twitter0.0% 0.0% 50.0% 50.0% 0.0% 4


65

Concerning the objectives of the corporate use of Social Networks, it is of further interest, that the most often identified target is to bring in new ideas into the company. It can be assumed, that the potentials resulting of personal networking are of higher importance compared to the possibility of writing and maybe discussing in detail about ideas for example in Blogs or Wikis. The respondents seem to prefer getting in contact via Social Networks and perhaps discussing their ideas “offline”.

Regarding corporate use of Twitter or comparable applications, we can emphasize that this kind of application is less used in companies. This might be a result of the novelty of this class of applications and the yet insufficient diffusion in corporate use. So, unfortunately, only less significant conclusions can be drawn: In best case, first tendencies can be observed, indicating the potentials that could possibly be realized due to future use of this kind of applications in R&D. For instance, companies try to use Twitter in order to bring new ideas into the company and to contribute to higher relevance and dynamic. For this reason, companies ought to monitor the evolutions concerning Twitter and the related applications in order to increase their chances to identify new trends and to correctly estimate the current potentials.

Within this current study, we could show that there is a relation between the duration of usage and the frequency of use in daily business. We assume the longer the duration, the higher the frequency of use of Web 2.0 applications for daily business purposes, as it is clearly visible in the case of Wikis. This suggests that Wikis can be established very well at the workplace. A reason for that might be, that Wikis are worked on basically by a large group of users and because of that, network effects can be realized. In general, it can be shown, that the acceptance of Web 2.0 applications increases by time and has led to high acceptance after four years. Furthermore the study points out, that – after some uncertainties in the beginning of corporate use of Web 2.0 applications, which can be characterized by seldom or occasional use – the so-called critical mass of users must have been reached because of the applications being an essential part of daily business after this period. According to our previous empirical results, it might not only be the critical mass, but the personal benefits employees can realize by using Web 2.0 applications. So the use and the acceptance of such applications is positively influenced by employees having some sense of achievement, for example savings of time or identification of previously unknown experts on certain topics.

In addition, a correlation between quality assurance and evaluation of quality within the different tools can be assumed. A possible reason for some respondents to escape into “variation of quality in dependence of use case” could be that there is no common definition of quality or quality criteria, since the evaluation of quality highly depends on the subjective perception of the content, which is influenced by personal experience and expectations. Secondly, it could also be concluded that there is no evaluation of quality by users at all. Some companies report that authors, who are aware of the fact, that the created content is visible by a broader audience in the department or even the company, feel obliged to ensure the quality of their contributions. Finally, another explanation for this phenomenon could be the conscious decision of companies to motivate employees to participate in creating content and not discourage them by some kind of “quality control” (Ernst, 2009). So managers should clearly define some use cases, in which they want such applications to be used and have clear commitment to the use of Web 2.0 applications in daily business.

Through the use of Web 2.0 applications within the surveyed companies, first tendencies of substitution of established applications can be observed. The substitution of telephone calls and e-mail can be explained with the availability of the content for a greater audience. Other employees can get some basic information before contacting the authors directly at all. Furthermore, we assume that a part of the communication is done by Instant Messaging or Social Networks, because of their high diffusion rates, instead of using telephone or e-mail. This will lead to faster and more efficient processes in R&D because of shorter ways of communication and shorter response time.

Concerning the impact of corporate use of Web 2.0 applications on innovation management, it has not been possible to make a significant statement yet. In our study, there are some tendencies visible, which suggest a positive influence at least in the future. Our respondents do experience some positive influence by the use of Web 2.0 applications, which becomes obvious through the increasing number of new ideas and of transparency in discussing them. Furthermore, a corresponding tendency can be observed, that the speed of innovation is felt higher since Web 2.0 tools have been used. However, to make significant statements, the applications seem not to having been used long enough. But companies using Web 2.0 applications with the goal of increasing the amount of ideas should be prepared to handle the evaluation and selection of the emerging ideas (Voigt et al., 2010).

8. Conclusion and further research

In our study – based on a literature review and an explorative pre-research – we could show that corporate use of Web 2.0

applications can realize several potentials concerning the generation and the exchange of knowledge within companies. Our empirical quantitative research of the status quo of corporate use of Web 2.0 applications can close the gap of existing studies, which do not focus on frequency and usage of such tools in R&D. Based on the evaluation of quality of the content and the frequency of use, it has been pointed out that the use of Web 2.0 applications and the therewith exchanged knowledge are important for daily business and so for maintenance of business activity. Concerning support of the emergence of innovations stimulated by Web 2.0 applications, we have found some tendencies, which indicate a positive influence. So are the increase of the number of new ideas and the higher transparency in discussing them through the use of Web 2.0 tools. Some statements of our study are only of limited significance due to the small data base concerning applications like Social Networks or Twitter or comparable applications. It remains to be analyzed, how corporate use of these applications will develop in the future. In a few


66

years, there might be the chance for further research tailored to Social Networks and Twitter or comparable applications. In this context, it is of particular interest to investigate the presence of tendencies of substitution among the Web 2.0 applications themselves, for example if microblogging has some influence on the use of Blogs or Instant Messaging. Furthermore, it could be analyzed, which factors influence the motivation of employees to participate. Our findings show clearly the use in daily business and it can be assumed, that one reason is the relevance of the content. So far, there are no further significant insights of motivation factors of employees in this field. Based on the findings of our research, further research could gain clues on evaluating for example the question which application fits best on which value-added step in order to support companies in implementing successfully Web 2.0 applications along their value chain.

Nomenclature IJEST International Journal of Engineering, Sciences and Technology R&D research and development n universe based on the current question N universe of the survey Acknowledgement Special thanks goes to Dr. S. John (Dipl.-Kfr.), who has been working as Senior Research and Teaching Assistant at the University of Erlangen-Nuremberg and received her PhD in 2010. Her feedback and her support have been very worthy during the realization of this study. References Albers, S. 2007. Methodik der empirischen Forschung, 2nd ed. Wiesbaden. Alby, T. 2007. Web 2.0 - Konzepte, Anwendungen, Technologien, München. Bach, N., Homp, C. 1998. Objekte und Instrumente des Wissensmanagements. Zeitschrift Führung + Organisation (zfo), Vol. 67,

No. 3, pp. 139-146. Bächle, M. 2008. Ökonomische Perspektiven des Web 2.0 - Open Innovation, Social Communities und Enterprise 2.0.

Wirtschaftsinformatik (WI), Vol. 50, No. 2, pp. 129-132. Back, A., Gronau, N., Tochtermann, K. 2008. Web 2.0 in der Unternehmenspraxis - Grundlagen, Fallstudien und Trends zum

Einsatz von Social Software, München. Beck, A. 2007. Web 2.0: Konzepte, Technologien, Anwendungen. Beck, A., Möhrike, M., Sauerburger, H. (ed.). Web 2.0, HMD

Praxis der Wirtschaftsinformatik, No. 255, pp. 5-16. Berekoven, L., Eckert, W., Ellenrieder, P. 2001. Marktforschung – Methodische Grundlagen und praktische Anwendung,

Wiesbaden. Berkhout, A.J., Hartmann, D., van der Duin, P., Ortt, R. 2006. Innovating the Innovation Process. International Journal of

Technology Management, Vol. 34, Nos. 3/4, pp. 390-404. Berndt, R. 1996. Marketing 1 – Käuferverhalten, Marktforschung und Marketing-Prognosen, Berlin et al. Bilgram, V., Brem, A., Voigt, K.-I. 2008. User-centric innovations in new product development - systematic identification of lead

users harnessing interactive and collaborative online-tools, in: International Journal of Innovation Management, Vol. 12, No. 3, pp. 419-458.

Böhler, H. 1992. Marktforschung, Stuttgart. Bohl, O., Manouchehri, S. 2008. Vertrauensaspekte beim Einsatz von Social Software. von Krotzfleisch, H.F.O., Bohl, O. (ed.).

Wissen, Vernetzung, Virtualisierung, Köln, pp. 449-460. Boos, M., Rack, O., Schauenburg, B. 2008. Wissenskommunikation in computergestützten Gruppen - Theoretischer Hintergrund

und empirische Befunde. Information Wissenschaft & Praxis, Vol. 59, No. 1, pp. 41-48. Borchert, J.E., Hagenhoff, S. 2004. Anforderungen an Instrumente des operativen Innovationsmanagements in Netzwerken,

Göttingen. Brem, A., Voigt, K.-I. 2009. Integration of Market Pull and Technology Push in the Corporate Front End and Innovation

Management - Insights from the German Software Industry. Technovation – The International Journal of Technological Innovation, Entrepreneurship and Technology Management, Vol. 29, No. 5, pp. 351-367.

Bughin, J., Manyika, J., Miller, A. 2008. McKinsey Global Survey Results - Building the Web 2.0 Enterprise, Brüssel San Francisco.

Bughin, J., Chui, M., Miller A. 2009. How companies are benefitting from Web 2.0. McKinsey Global Survey Results, pp. 10-17. Buhl, H.U. 2008. Online-Communitys - der Weg zur Gruppenintelligenz oder zur Gruppenignoranz und kollektiven

Verdummung?. Wirtschaftsinformatik (WI), Vol. 50, No. 2, pp. 81-84.


67

Bullinger, A.C., Hallerstede, S.H., Renken, U., Soeldner, J.-H., Möslein, K.M. 2010. Towards Research Collaboration - A Taxonomy of Social Research Network Sites. Proceedings of the Sixteenth Americas Conference on Information Systems, Lima, August 2010.

Carr, N.G. 2004. Does IT matter?, Boston. Cook, N. 2008. Enterprise 2.0 - How Social Software Will Change the Future of Work, London. Ehrlenspiel, K. 2007. Integrierte Produktentwicklung – Denkabläufe, Methodeneinsatz, Zusammenarbeit, 4th ed., München Wien. Ernst, M. 2009. Unpublished working paper at the Chair of Industrial Management at the University of Erlangen-Nuremberg. Ganswindt, T. 2006. Treibstoff der Wirtschaft. Dietrich, L.; Schirra, W. Innovationen durch IT - Erfolgsbeispiele aus der Praxis -

Produkte - Prozesse - Geschäftsmodelle, Heidelberg, pp. 57-64. Geulen, E. 2006. Innovationsstrategie im Wandel der Zeit. Dietrich, L.; Schirra, W. (ed.). Innovationen durch IT - Erfolgsbeispiele

aus der Praxis - Produkte - Prozesse - Geschäftsmodelle, Heidelberg, pp. 37-56. Gölz, S. 2007. Wissensmanagement 2.0 - Projekterfahrungen der OSCAR GmbH. OSCAR.trends Magazin, No. 2, pp. 28-32. Gouthier, M.H.J. Hippner, H. Web 2.0 - Anwendungen als Corporate Social Software. Hass, B.H., Walsh, G., Kilian, T. (ed.).

Web 2.0 - Neue Perspektiven für Marketing und Medien, Berlin Heidelberg, pp. 91-100. Grossmann, M., McCarthy, R.V. Web 2.0: Is the Enterprise Ready for the Adventure?. Issues in Information Systems, Vol. 8,

No. 2, pp. 180-185. Hage, S. 2006. Der Web-Meister. http://www.manager-magazin.de/it/artikel/0,2828,449911,00.html, viewed 2009-04-08. Hideyuki, F. 2007. "InnovationCafe", an In-house Social Network Service (SNS) Used in NEC. NEC Technical Journal, Vol. 2,

No. 2, pp. 63-67. Hirsch, C., Hosking, J., Grundy, J., Chaffe, T., MacDonald, D., Halytskyy, Y. The Visual Wiki: A New Metaphor for Knowledge

Access and Management, Proceedings of the 42nd Hawaii International Conference on System Sciences, 2009. Homburg, Ch., Krohmer, H. 2006. Grundlagen des Marketingmanagements – Einführung in Strategie, Instrumente, Umsetzung

und Unternehmensführung, Wiesbaden. Koch, M., Richter, A. 2007. Enterprise 2.0 - Planung, Einführung und erfolgreicher Einsatz von Social Software in Unternehmen,

München. Koen, P., Ajamian, G., Burkart, R., Clamen, A., Davidson, J., D’Amore, R., Elkins, C., Herald, K., Incorvia, M., Johnson, A.,

Karol, R., Seibert, R., Slavejkov, A., Wagner, K. 2002. Providing Clarity and a Common Language to the „Fuzzy Front End“. Research and Technology Management, Vol. 44, No. 2, pp. 46-55.

Komus, A., Wauch, F. 2008. Wikimanagement - Was Unternehmen von Social Software und Web 2.0 lernen können, München. Krause, F.-L., Franke, H.-J., Gausemeier, J. (ed.) 2007. Innovationspotenziale in der Produktentwicklung, München Wien. Kranz, J., Janello, C., Picot, A. 2009. Die Rolle von Web 2.0-Prinzipien im Innovationsprozess. Information Management und

Consulting, Vol. 24, No. 2, pp. 9-47. Lochmaier, L. 2007. Führung und der intelligente Schwarm. http://www.manager-magazin.de/it/cio-spezial/0,2828,513549,

00.html, 2007-10-30, viewed 2009-04-08. Madhavan, R., Grover, R. 1998. From Embedded Knowledge to Embodied Knowledge: New Product Development as Knowledge

Management. Journal of Marketing, Vol. 62, pp. 1-12. McAfee, A.P. 2006. Enterprise 2.0: The Dawn of Emergent Collaboration. MITSloan Management Review, Vol. 47 (2006) No. 3,

(Reprint Number 47306), pp. 21-28. McAfee, A.P. (a) 2009. Enterprise 2.0: new collaborative tools for your organization’s toughest challenges, Boston. McAfee, A.P. (b) 2009. Shattering the Myths About Enterprise 2.0. Harvard Business Review, November 2009, pp. 1-6. Newman, A.C., Thomas, J.G. 2009. Enterprise 2.0 Implementation, McGraw-Hill. N.N.(a). 2006. Web 2.0: Kooperieren, nicht kontrollieren. Computerwoche, 2006-12-20, p. 21. N.N.(b). 2006. Vom Web 2.0 zu Enterprise 2.0. Computerwoche, 2006-07-07, pp. 26-27. Langham, M. 2007. Enterprise 2.0. Entwickler Magazin, No. 1, pp. 131-136. Lattemann, C., Stieglitz, S., Kupke, S. 2009. Deutsche Unternehmen auf dem Weg zum Web 2.0?. HMD Praxis der

Wirtschaftsinformatik, No. 267, pp. 18-26. Neyer, A.-K., Doll, B., Möslein, K.M. 2008. Prototyping als Instrument der Innovationskommunikation. Zeitschrift Führung +

Organisation (zfo), Vol. 77, No. 4, pp. 210-216. Reichart, S., Reichart, M. 2006. Erfolgsfaktor Innovationsprozess bei der Siemens AG. Zeitschrift Führung + Organisation (zfo),

Vol. 75, No. 3, pp. 163-168. Reinhold, D., Michel, K.-U. 2007. Wissensmanagement vs. Competitive Intelligence - Die Notwendigkeit einer Symbiose beider

Disziplinen für die Schaffung von strategischen Wettbewerbsvorteilen. Information Wissenschaft & Praxis, Vol. 58, No. 6-7, pp. 327-332.

Sandkuhl, K. 2005. Wissensportale - Merkmale, Architekturen und Perspektiven. Informatik Spektrum, Vol. 28, No. 3, pp. 193-201.

Schachner, W., Tochtermann, K., Giesswein, M. 2009. Quo vadis Web 2.0?, in: Wissensmanagement - Das Magazin für Führungskräfte, Vol. 11, No. 2, pp. 20-24.


68

Seegmüller, K. 2008. Wissen teilen ist nicht nur innerhalb des Unternehmens sinnvoll, sondern auch zwischen Kunden und Lieferanten – Enterprise 2.0 braucht Chef 2.0. Computer Zeitung, No. 47.

Seipel, C., Rieker, P. 2003. Integrative Sozialforschung - Konzepte und Methoden der qualitativen und quantitativen empirischen Forschung, Weinheim München.

Tapscott, D., Williams, A.D. 2006. Wikinomics: How Mass Collaboration Changes Everything, Portfolio. Töpfer, A., Silbermann, S., William, R. 2008. Die Rolle des Web 2.0 im CRM – Wie kann durch interaktives Internet die

Beziehung zum Kunden verstärkt und verbessert werden?. Töpfer, A. (ed.) Handbuch Kundenmanagement – Anforderungen, Prozesse, Zufriedenheit, Bindung und Wert von Kunden, 3rd ed., Heidelberg.

Vahs, D., Burmester, R., 2005. Innovationsmanagement – Von der Produktidee zur erfolgreichen Vermarktung, 3rd Ed., Stuttgart. Voigt, K.-I. 1998. Strategien im Zeitwettbewerb - Optionen für Technologiemanagement und Marketing, Wiesbaden. Voigt, K.-I., Ernst, M., Schindler, M. 2010. Strategic Foresight - A Web 2.0 Based Concept for the Integration of Foresight

Activities into Innovation Management, 5th International Innovation Lab Conference, Nuremberg, October. von Krotzfleisch, H., Mergel, I., Manouchehri, S., Schaarschmidt, M. 2008. Corporate Web 2.0 Applications. Hass, B. H., Walsh,

G., Kilian, T. (ed.): Web 2.0 - Neue Perspektiven für Marketing und Medien, Berlin Heidelberg, pp.73-87. Weber, M. 2008. Enterprise 2.0 - Analyse zu Stand und Perspektiven in der deutschen Wirtschaft, Berlin. Zimmermann, H.-D. 2007. Web 2.0 und seine Bedeutung für die Unternehmen. Innovation Management, No. 1, pp. 42-45. Biographical notes Prof. Dr. Kai-Ingo Voigt is a Full Professor at the Friedrich-Alexander University of Erlangen-Nuremberg, Germany, where he has held the Chair of Industrial Management since 1998. He received his PhD in 1991 from the University of Hamburg. His current research focuses are Strategic Management, Innovation and Technology Management, Management of (Industrial) Services and Production and Operations Management. Dipl.-Wirtsch.-Ing. Markus Ernst received his diploma degree in Industrial Engineering from the University of Erlangen-Nuremberg in 2009. Since then, he has been working as Senior Research and Teaching Assistant at the University of Erlangen-Nuremberg. His research interests are Technology and Innovation Management as well as Enterprise 2.0. Received June 2010 Accepted September 2010 Final acceptance in revised form November 2010

MultiCraft


Vol. 2, No. 9, 2010, pp. 69-82



www.ijest-ng.com


Controlled temperature grinding under modified atmosphere for Almond (Prunus Dulcis) paste production

G. Aiello, G. La Scalia*, L. Cannizzaro

1*Dipartimento di Tecnologia Meccanica,Produzione ed Ingegneria gestionale, University of Palermo, ITALY

*Corresponding Author: e-mail: [email protected], Tel +39-091238618663

Abstract

The quality of the raw material and the processing conditions have a great influence on the nutritional properties of finished products. This research is focused on almond paste production and aims at developing a new production process to deliver the high nutritional content of the raw almonds into the finished product. Raw almonds are composed mainly of fats (approx. 50%) and proteins (approx. 25%), and are particularly rich in omega 6 fatty acids, and are hence exposed to denaturation and oxidation phenomena during the traditional grinding/homogenization process. The study involved the analysis of six different local cultivars based upon the main nutritional indicators (protein content, vitamins, and fatty acids) which resulted in the determination of the cultivar tuono as the one with the best nutritional properties. The further step consisted in establishing an innovative production process involving controlled atmosphere and refrigerated grinding with the aim of preserving the nutritional value of the raw material. Finally an experimental comparison of the product obtained with the proposed production process and the traditional method showed an average incremental gain of 27% and 21% in the protein and fat content, respectively.

Keywords: Almonds; Food Processing Aspects; Lipid Oxidation.

1. Introduction In the past, the development of industrial food processing has sometimes privileged productivity rather than nutritional properties of the products, even arising some concern for consumer health and safety. With the demographic imperative of an aging population worldwide, there is an understandable emphasis today in the food industry to manufacture products that can be labelled with claims for health promotion, quality and safety (Chen et al., 2006). The establishment of production processes preserving the nutrient content of foods is hence the new frontier of process innovation in the food industry. This paper refers to the grinding and homogenization of almonds which is commonly employed in several production processes. These processes severely damage the raw material resulting to significant loss of nutritional content, as for example in production of almond milk, which is an aqueous dispersion of almond powder. The evidence of beneficial influences of almond consumption on human health has been confirmed by several researches (Lapsley, 2003; Chen et al., 2006 ), however industrial production processes generally fail in delivering the nutritional content to the finished products. The traditional (“homemade”) production cycle in fact originates an aqueous dispersion which is unstable at room temperature, unless extremely small particle size distribution is achieved. In addition the presence of water originates favourable conditions for micro-biological growth which drastically reduces the shelf-life of the product. Another critical issue is that almonds are rich in omega 6 fatty acids, which are significantly affected by the lipid oxidation phenomenon during grinding and homogenization processes. These are the main reasons why industrial almond milk production is difficult. In this paper a production cycle is proposed which preserves the nutritional content in the raw material to the final product. The study involves a preliminary investigation of the raw material and a subsequent analysis of the process conditions that affect the quality and nutritional content of the product. Once the raw material with the highest nutritional content has been selected, an innovative refrigerated and oxygen-free homogenization process is established. An experimental analysis has been finally carried

Aiello et al./ International Journal of Engineering, Science and Technology, Vol. 2, No. 9, 2010, pp. 69-82

70

out to determine the nutritional value of the product thus obtained, and a comparison with the traditional products taken as reference has shown the effectiveness of the proposed approach. 2. Materials and Methods 2.1 Raw materials analysis The sweet almond, (Prunus Dulcis L.) is a stone fruit commercially cultivated in Mediterranean countries, Armenia, Iran, California (U.S.A.), and Australia whose beneficial properties are nowadays recognized (Lapsley, 2003; Chen et al., 2006; Mandalari et al., 2008 ). Such properties originate mainly from the content and composition of fatty acids, proteins, and vitamins. The antioxidant activities of almonds have emerged in various studies (Wu et al., 2004; Amarowicz et al., 2005) and their consumption has been associated with several benefits, as for example in reducing risk of coronary heart disease. Nutritional values however slightly vary according to the genotype and fatty acid composition and they can be influenced by ecological conditions, variety, location, and technical-cultural practices. In the present research, in order to obtain a high nutritional almond paste, the selection of the raw material with highest nutritional value is required. For such purpose 6 different cultivars (Tuono, Ferragnès, Fascionello, Romana, Genco and Pizzuta d’Avola ) have been analyzed and their content of proteins, fatty acids and vitamins has been measured. The beneficial properties related to these elements are briefly described below. Vitamin E. Among dry seeds and fruits, almonds are the best source of Vitamin E in the form of alpha-tocopherol, with a percentage of approx. 250 mg/kg. Alpha-tocopherol is the most active component of the Vitamin E and the most powerful antioxidant in the lipid (fat) phase of the human body (Burton, 1989; Niki et al., 1989). Vitamin E cannot be synthesized by the body and must therefore be supplied in the diet or through supplementation. The extremely critical role of alpha-tocopherol in protecting against free-radical reactions becomes apparent when considering the vast number of diseases (e.g. Senile dementia, Alzheimer, Atherosclerosis and other circulatory diseases) thought to be caused by these reactions (Ames, 1983; Cross et al.. 1987). Recent studies have also shown that a low vitamin E concentration in human blood is associated with an overall increased risk for many cancers, including breast and lung cancer (Wang et al., 1989; Stahelin et al., 1989; Menkes et al., 1986). Proteins. Almonds are a good source of proteins, with a content of approx. 25%. Almond protein profile is mainly constituted by AMP or amandin (Sathe Shridhar et al., 2002). Despite the long history of global cultivation and ready consumer acceptance of almonds, data on almond protein quality are still researched. Amandin is considered as an allergen, probably due to its high content of nitrogen (approx 19%), however although allergy to nuts has been frequently reported, there is no reliable data on the identification of amandin as an almond allergen. To date, evidence suggests that amandin is not as highly allergenic as the proteins found in peanuts or walnuts ( Roux et al., 2001). Finally amandin is reported by some authors as a highly digestible protein and antigenically stable toward various food processing methods although it has poor nutritional value (Venkatachalam et al., 2002). Lipids and Fatty acids. Fatty acids are categorized by the number of double bonds present between the carbon atoms in their carbon chain in “saturated”, “monounsaturated”, and “polyunsaturated”. Polyunsaturated fatty acids (PUFA) can be further divided into omega-3 and omega-6 fatty acids, based on the location of the first double-bond in the carbon chain. Certain omega-3 and omega-6 PUFA are considered to be Essential Fatty Acids (EFAs) because they are necessary for health, but cannot be synthesized by the body, it is therefore important to supply them through daily dietary intake. Alpha-Linolenic Acid (ALA), an omega-3 fatty acid, and Linoleic Acid (LA), an omega-6 fatty acid, are the predominant essential fatty acids in humans. Omega-3 fatty acids have a balancing effect on omega-6 fatty acids. Both are essential nutrients, but they should be consumed in equal proportions. Lipids are the main component in all nuts reaching approx. 50% of weight in almonds, with oleic and linoleic acids representing more than 80% of the total fatty acids while linolenic acid is present in extremely small percentage. There are many health benefits attributable to essential fatty acids, for example it is generally accepted that omega-3 fatty acids help to reduce the levels of triglycerides in the body, thus decreasing the risk of heart disease. Omega-6 fatty acids have been shown to be beneficial in the reduction of cholesterol levels when they are substituted for saturated fats in the human diet. The benefit in consuming omega-6 fatty acids therefore lies in the fact that they reduce the incidence of coronary artery disease. Amygdalin. Amygdalin is a cyanogenic glycosides which determines the difference between bitter almonds (Prunus Amygdalus), containing 3 to 5% of amygdalin and sweet almonds containing only traces of amygdalin. Amygdalin is thought to have beneficial effect in the cure of cancer although it is considered highly poisonous because it can be hydrolyzed to yield deadly hydrocyanic acid (HCN). Bitter almonds hence are not used for food preparation. In the present research Amygdalin content has been evaluated to detect and avoid cultivars which might be unsafe for food processing. Almond samples were collected from six different cultivars: Tuono, Ferragnès, Fascionello, Romana, Genco and Pizzuta d’Avola. 'Ferragnès' is the result of the European almond cultivar breeding programmes, it was obtained in France in 1960 by crossing the local 'AÏ’ with the Apulian 'Cristomorto' (Grasselly and Crossa-Raynaud, 1980). The positive traits of 'Ferragnès' are its late blooming, satisfactory shelling percentage, absence of doubles and good kernel features. On the other hand, the negative characteristics are its tree weeping habit, and especially self-incompatibility, meaning that without cross-pollination a viable crop is highly unlikely. 'Genco' is another recent Apulian cultivar while 'Tuono' (Grasselly et al., 1992) is an ancient Apulian variety which has the defect of producing medium to high percentage of double kernels. Romana, Pizzuta d’Avola and Fascionello are Sicilian cultivars: the first one is considered to have the best sensorial quality, the second and the third have low market value due to the irregular shape of the kernels. An experimental campaign and a statistical analysis of the results was performed in order to


71

select the cultivar with the best nutritional profile to be employed for subsequent processing phase. For experiments twenty samples of approx 50 g from each cultivar were acquired and analyzed accounting a total of 120 samples. Samples were extracted from 10 different batches (2 replicates for each batch) of the same cultivar grown in the same crop year but gathered in different days. All batches came from experimental fields in the same geographic area (central Sicily) cultivated with the same methods. Samples were powdered at room temperature using a mortar and pestle and stored in an airtight container at −20 °C. Statistical analysis was conducted taking the average protein, lipids, amygdalin and vitamin E content of the 2 replicates of each sample thus obtaining 10 values for each cultivar. For each set of 10 values thus obtained the average and the standard deviation of the samples were determined. Also the error bars have been evaluated as the differences of the maximum and minimum values achieved and the average previously calculated. The same samples were also used to determine the fatty acids profile. Also in this case the results in each replicate were averaged thus obtaining 10 values for each cultivar. The average and the standard deviation of the 10 values thus obtained were calculated and the error bars were evaluated considering the maximum and minimum values achieved. 2.2 Particle size analysis A particle size analysis is required to investigate the problem of emulsion stability. The methods here considered for particle size measurement are sieves, sedimentation, electrozone sensing and laser diffraction. Sieves can be used for large particles and are not suitable for fatty acids emulsions. Sedimentation has an applicable range of 2-50 μm but it is affected by significant measurement errors. Electrozone requires expensive calibration and the measurement of small particles is quite difficult. Laser diffraction (LD) is a method finally employed for particle size measurement, and it is based on the properties of particles to scatter light. This method has become the preferred standard in many industries for characterization and quality control. In this study particle size analysis was carried out by means of Malvern Particle Size Analyzer - Mastersizer 2000, which uses a He/Ne Laser 633 nm (red) for materials in the range 0.02μm to 2000μm. For experimental analysis the process was fed with pure almonds of the same cultivar tuono and two sampling points have been established at two different stages of the production (namely after the cutter mill operation and after the ball mill operation) process to monitor the particle size distribution during the two steps of grinding/homogenization process. The experimental procedure involved the extraction of 4 set of samples with 4 replicates each in both the sampling points, resulting in 32 samples of 20 g each. Sample replicates were taken at time intervals of 15 mins, with the same processing parameters (milling chamber temperature, cutter rpm, duration). All the samples were produced in the same processing conditions and all the replicates were obtained with the same batch of raw material, however different lots have been employed under different sets of experiments. Results were analyzed comparing the shapes of the particle distributions obtained. 2.3 Nutritional content analysis The assessment of nutritional content is based upon the evaluation and the comparison of proteins and lipids percentage in the raw material and in the finished product. The nutritional content of the product thus obtained was evaluated. Determination of the protein content have been conducted by the Kjeldhal method (UNI ISO 8968-1:2002). Protein content denaturation is generally the consequence of high temperature or pressures during processing and, depending on the denaturation effect can be measured by standard tests as Protein Dispersibility Index (PDI), Nitrogen Solubility Index (NSI) and indirectly by Water Absorption Index (WAI). In this research, however, only the protein content has been evaluated analogously to similar researches (Ignário et al., 2007) by means of the Kjeldahl method. This methodology leads to a rough evaluation of the protein denaturation effect, and the results here presented, consequently, must be considered preliminary. The employment of more accurate techniques requires a more detailed analysis of the typology of denaturation effects. Total fat content has been determined by releasing lipids using acid hydrolysis (UNI 22605-1992). The Fatty Acids analysis was then carried out by converting them into their methyl esters which were finally analyzed by gas chromatography according to ISO 5508:1990 method. Concerning the lipids and protein content, the protocol was carried out using 5 samples of the previously analyzed raw material of cultivar tuono, which emerged as the local cultivar with highest protein and fat content. Also an homogeneous process setup was established by fixing a constant set of process parameters. Production of almond paste was hence started, according to traditional recipe with sugar addition after homogenization (approx. 30% in weight), until steady state was reached. Four replicates for each sample point were carried out resulting in a total of twenty samples (20 g each) of finished product. The replicates were extracted each 15 mins. after the achievement of the steady state condition, with the same batch of raw material. Experimental tests for the different set of samples were carried out in different days but with the same process setup after complete cleanup of the machines. The samples thus obtained were analyzed and the total protein and fat content was determined. 3. Results of the raw material analysis

Average values of the above indicators (Vitamin E, fats, proteins, and amygdalin) for the 20 samples examined are given in Table 1.

72

Fothe samindivalu

ThsuitmetmonfattysignThe

Aiello

or the analysis variability of

mples has beenicators is givenue of each indi

his representattable for the pthodology. Acnounsaturated y acids. Amonnificant for pale percentage o

o et al./ Interna

Table 1 –

fascionello

romana

tuono

genco

ferragnès

pizzuta

average

and selection the nutritional

n evaluated andn in Figure 1 foicator for all th

tion gives a clepurpose of thiccording to thefatty acids, ma

ng selections, dlmitic, palmitoof linoleic aci

ational Journal

– Comparison o

Vitamin E (m

203.33 (±

196.34 (±

227.22(±3

189.52(±3

202.08(±3

213.07(±3

205.2

of the raw matl properties. Ind reported in Tfor all the cultive cultivars con

ear informatiois research. Ae profile of faainly oleic aciddifferences in leic and stearicid was lower,

l of Engineerin

of the cultivarsNutr

mg/kg) Amy

33.8) 7

36.6) 7

33.3) 7

39.0)

37.9) 8

32.5) 7

6

terial, howevern order to obtaTable 1. In addvars considere

nsidered.

Figure 1 -

n of the nutritA detailed anal

atty acids (Tabd, and low amofatty acid comc acids. The pr

ranging from

ng, Science and

s according to tritional conten

ygdalin (mg/kg

78.09(±11.4)

78.38(±10.9)

79.99(±11.9)

76.7(±11.6)

80.03(±11.5)

78.48(±11.8)

78.61

r, the average vain a robust prdition a graphd. Such figure

- Nutritional pr

tional profile olysis of the fable 2 and Figounts of satura

mposition were roportion of ole

m 19.72 to 21

d Technology,

their nutritionant

g) Proteins (%

20.2(±1.5

22.2(±1.5

25.5(±1.5

24.1(±1.5

23.6(±1.5

22.6(±1.6

23.03

value is not sufroduction proc

hical representa shows in a rad

rofile

of the differentatty acids profg. 2) the kerneated (palmitic a

highly signifieic acid was th.25%. Experim

Vol. 2, No. 9, 2

al content indic

%) Total lipi

59) 48.52(±

50) 47.44(±

56) 50.01(±

53) 46.2(±

54) 46.62(±

65) 48.99(±

47.9

fficient since itcess, hence, thation of the vadar graph the d

t cultivars and file has been els examined and stearic) anicant for oleic he highest, rangmental results

2010, pp. 69-82

cators

ids (%)

±4.9)

±5.0)

±4.9)

±5.0)

±6.0)

±5.0)

96

t gives no inforhe standard devalues of the afodeviations from

helps in seleccarried out wshowed a hig

nd polyunsaturaand linoleic acging from 68.2showed that

2

rmation about viation of the

forementioned m the average

cting the most with the same gh amount of ated (linoleic) cids, and less 26 to 70.22%. the cultivars

73

concultthe alwcha

4. A

Thblanin padd

Aiello

nsidered are qutivar within thocorrectness of

ways be characracteristics con

fascion

romana

tuono

genco

ferragn

pizzuta

average

Almond Paste

he traditional mnched sweet alparticular focusditional elemen

o et al./ Interna

uite similar accose analyzed df the methodolocterized prelimnsidered and fo

palmitic

nello 5.89(

a 5.93(

6.17(

5.78(

nès 5.92(

a 5.87(

e 5

Production P

manufacture olmonds mixed ses on the prodnts in order to

ational Journal

cording to thedoes not have aogical approachminarily. In thor this reason th

Table 2– Com

c C16:0 % pa

(±0.44)

(±0.37)

(±0.34)

(±0.38)

(±0.68)

(±0.45)

.93

Process

of almond milkwith saccharoduction of a de

o improve the

l of Engineerin

eir nutritional va crucial influenh as in fact whhe case presehis cultivar wa

mparison of the Fatty

almitoleic C16

0.58(±0.10)

0.53(±0.11)

0.63(±0.09)

0.32(±0.11)

0.56(±0.14)

0.58(±0.11)

0.53

Figure 2-

k involves the se and a certaiehydrated almotexture, taste,

ng, Science and

values, thus lence on the qua

hen the developented, the culas chosen for fu

cultivars accory acids profile

:1 % stearic

) 2.07(

) 2.10(

) 2.19(

) 1.93

) 1.53(

) 2.07(

1

- Fatty acids pr

preparation ofin percentage oond paste withappearance, o

d Technology,

eading to the cality of the finapment of a foodltivar “tuono” urther processi

rding to their fe

C18:0 % ole

(±0.45) 69

(±0.42) 68

(±0.52) 70

3(±43) 69

(±0.63) 70

(±0.47) 69

.98

rofile

f a mother almof water or milh a water conteor to extend th

Vol. 2, No. 9, 2

conclusion thaal product. Thisd product is un

dominates alng.

fatty acids

eic C18:1 %

9.54(±1.43)

8.26(±1.98)

0.22(±1.17)

9.27(±1.42)

0.11(±1.50)

9.18(±1.30)

69.43

mond paste as alk. The producent of about 10he shelf life. T

2010, pp. 69-82

t the choice os conclusion d

ndertaken raw mll others acco

linoleic C18:2

20.14(±1.64

21.02(±1.92

21.55(±1.67

19.72(±1.51

20.40(±2.30

20.41(±1.53

20.54

a product of thction process h0% by weight,Traditionally t

2

of a particular oes not affect material must

ording to the

2 %

)

)

)

)

)

)

he crushing of here described , and various there are two


74

manufacturing processes which may be distinguished, one being a combined process of cooking and crushing, and the other an evaporation-crushing process. According to the first process, named the “French” process, dry blanched almonds are grated and then subjected to pre-crushing, for the purpose of tearing a large number of cells which enclose the almond oil, and thereby obtain pure almond paste. The various sugars, saccharose, syrup are then mixed with a certain quantity of water to produce a direct solution, while a cooking step enables the quantity of water to be reduced and the consistency of the paste required for the manufacture to be obtained. The products thus issuing from the two preceding steps are introduced into a combined cooker and malaxator, followed by cooling and crushing. It is in the course of this step that the various perfumes, alcohols and other additives, are added to the mixture. According to the second evaporation-grilling process, also termed the “German” process, the blanched white almonds are mixed with crystalline sugar, and the mixture is then subjected to crushing. Thereafter, water is added to facilitate, during the grilling, Maillard reaction which thus develops the formation of the aromatic substances, and the evaporation of a certain quantity of water. Cooling is then necessary, and is generally achieved by a current of cold air, and the product thus obtained is termed the mother almond paste. Added to this mother paste are confectioner's sugar, glucose syrup and other additives which, when mixed, result in the final almond paste. The two processes clearly generate differences in the rheological and organoleptic properties of the finished products since the Maillard reaction is developed in different conditions. Almond milk is finally obtained by mixing the almond paste, produced according to the processes above described, with water, thus resulting in an aqueous emulsion of an organic liquid (an oil). Organoleptic characteristics of almond paste depend on its formula and method of manufacturing, however the most important factor, be it from flavour, appearance and texture, is oil separation in the product, which is a consequence of the leakage of oil from almond paste. Emulsions made by mixing of pure immiscible liquid phases are in fact very unstable and separate rapidly ( Meunier and Mengual, 1996). Only micro-emulsions are thermodynamically stable dispersions, which means that they form spontaneously and are stable indefinitely. Most macro-emulsions require the input of considerable amounts of energy for their production and can only be stable in a kinetic sense. The kinetics of the emulsion breaking process are governed by three different mechanisms: brownian flocculation, sedimentation, and creaming. In order to improve their stability, it is therefore crucial to identify the major instability mechanism for the specific food emulsion of interest. In the case here considered, brownian flocculation can be neglected since it affects extremely small particles (nanometer size). On the contrary, sedimentation and creaming, which result from the action of gravitational force on phases that differ in density, are responsible for the separation of fatty acids (oil) from the aqueous phase of the composition. Sedimentation and creaming have been extensively analyzed using Stokes law which describes the velocity of the upward or downward motion of a droplet as a function of droplet radius (Dickinson, 1992):

( )2

212

92

μρρ

⋅−⋅⋅⋅

=rgV

Where V= velocity of separation rate of creaming (cm/sec), g= gravity acceleration, r= droplet radius (cm), ρ1=density of the dispersed phase (g/cm3), ρ2=density of the continuous phase (g/cm3), µ2=viscosity of the continuous phase (Pa sec). From Stokes law it is shown that the rate of a droplet to move to the top (V) is directly proportional to the density difference of the oil phase from water phase and to the square radius of the droplet. It is also inversely proportional to the viscosity of the water phase. Of these factors, only ρ1 and r can be manipulated in order to achieve emulsion stability. A variety of methods have been developed in order to stabilize oils by preventing separation. Typically, current stabilization of oils involves the use small droplet size, and / or weighting agents to increase the density of the oil phase. As a result, current stabilization technologies require complex homogenization equipments and stable surfactants. Recently, methodologies have been developed to stabilize fatty acid oil droplets having a wide range of particle sizes (about 5 microns to about 20 microns, rather than only around 0.1 microns as is typically used in emulsified oils) without surfactants using for example pectin and alginate compounds (WIPO Patent No. 03/003849 A2) . For fatty acids emulsion stability, hence a homogenization process has been setup to achieve a 5 microns average particle size. It is generally recognized, however, that during the homogenization, the disruption of cells and resulting increase in surface area promotes oxidative deterioration and higher rates of microbiological and enzymatic activity (Lethuaut et al., 2002), although this effect has not been confirmed by other researches (Charikleia et al., 2007). Since traditional almond paste processing involves the addition of water at some phase, resulting moist causes the paste to deteriorate rapidly if other preservative measures (for example chilling, freezing and heat processing) are not taken. Oxidative deterioration is typically initiated by Lipoxygenases (LOX) that are dioxygenase enzymes containing nonheme iron protein. Lipoxygenase is distributed throughout many seeds, but the enzyme is inactive because of its limited contact with oxygen. Breaking of cell structure during the size reduction operation causes the Lipoxygenase to catalyze reactions of polyunsaturated fatty acids with oxygen (Hamberg and Samuelsson, 1967; Hamberg and Hamberg, 1980). Deactivation of almond lipoxygenase has not only theoretic but also practical importance because they are significant quality deteriorating agents. Due to Lipoxygenase activity in almonds there are changes in the colour, flavour during size reduction. Additionally the release of cellular materials provides a suitable substrate for microbiological growth and this can also result in the development of off-flavours and aromas and safety related issues. The duration of size reduction process and the delay before subsequent preservation must be accurately controlled to achieve the desired texture. The relationship between the size of food particles and perceived texture is discussed by


75

Stanley and Tung (1976); and by Sherman (1976). The control of off-flavors, therefore, requires inactivation of lipoxygenase enzyme. Since lipoxygenase is heat sensitive, its in-activation is most commonly accomplished by thermal processing. At temperatures above 60°C, the half-lives of the various lipoxygenase enzymes rapidly decrease with increasing temperature. Recent researches (Buranasompoba et al., 2007) report that even 2 minutes at 55°C may suffice to inhibit lipoxygenase. However, heat treatment also reduces the nitrogen solubility index and protein dispersibility index. Such approach, hence, tends to degrade the end product nutritionally or functionally. Protein denaturation is commonly defined as any non-covalent change in the structure of a protein. This change may alter the secondary, tertiary or quaternary structure of the molecules. One of the most common effects of denaturation is the loss of solubility which can be related to the loss of a great number of desirable characteristics of the protein. The role of many process designs and food additives is hence to maintain protein solubility. 5. New Process Setup and Results On the basis of the above considerations, a new production process is here proposed which aims at obtaining a stable emulsion of almond fatty acids. Emulsion stability is obtained by reducing the average particle size up to 5 μm, and the production process presented is designed to preserve the nutritional content, the flavour of raw material, in an industrial product with suitable shelf life. As discussed before the most critical aspects in almond processing are:

• emulsion stability; • protein denaturation; • lipid oxidation.

This study proposes a low temperature almond paste processing process and is focused on the grinding process, which is the main cause of nutritional loss in the finished product. Traditional grinding processes can be carried in wet or dry conditions. In general wet grinding process is faster and more energy efficient, as in fact wetting agents break down surface tensions of aggregated particles and absorb excess heat produced. However, as stated before, the presence of water typically increases microbiological growth and lipid oxidation. For such reasons, a “dry” grinding process is here adopted which involves two subsequent phases of cutting and homogenization. According to such procedure, raw material is grinded without the addition of water. The humidity and the temperature of processing are in fact the parameters which most directly influence the quality of the resulting product. Previous studies on the effects of the water activity on lipoxidation, using specific methods of detection, have mostly supported the commonly accepted scheme described by Labuza (1971), according to which the rate constants decrease with increasing aw values, up to about 0.3, when the tendency is reversed. However, some authors have reported that this is not the case in all situations; and recent studies (Tazi et al., 2009) describe the effects of aw in almond paste at various heating temperatures, according to the recently developed integrative analytical method of lipoxidation based on the detection of the first stage peroxides (CL) and advanced stage carbonyls (TBARS). This study demonstrates that the conjoint effect of temperature and aw on lipoxidation is much more complex than the classical theory describes, and at low temperatures (60 to 80°C) the Kinetics of the reaction increases with aw.. Nevertheless, a detailed analysis of such phenomena should involve a better knowledge of how temperature affects the development of lipid oxidation oil-water emulsions, and should take into account additional process parameters in a specific optimization context, as reported for example in De Pilli et al (2008) for a similar extrusion-cooking process. The cutting process is carried out in a cutter mill where the milling action is produced by a rotating assembly that uses sharp knives or blades to cut the particles. A subsequent homogenization process, trough a ball mill, has been considered to obtain a uniform particle distribution. In addition, in order to reduce the effect of protein denaturation caused by temperature rise during grinding, water-cooled milling chambers have been employed. A final issue concerns the inhibition of off-flavours related to the lipid oxidation phenomenon during grinding. Since this problem is well-known to food manufacturers, some technologies such as grinding vacuum, inert gases (nitrogen) or deoxygenating agent have been developed. In this research, in order to inhibit the effect of lipid oxidation a vacuum milling process has been setup for experiments. Experimental test configuration is given in figure 3 and 4. The process involves an initial blanching phase (1) and the subsequent milling phase subdivided in two steps carried out in the cutter mill (2) and in the ball mill (3) respectively. The product obtained is then filtered into a sieve (4) and stored into a silo (5).

76

Aexpfat cexpdiscTheaveintrthe invais re

Aiello

Fi

An experimentaperimental prodcontent) of the

perimental rotacussed in the pre experiment drage values an

rinsic effect of homogeneity oariability of theported in figu

o et al./ Interna

gure 4- the pro

al analysis has duction procese raw material aary cutter mill revious paragr

design involvednd shape factorrandom factor

of the complete indicators, th

ure 5a for better

ational Journal

Figur

oposed product

been finally ps. The experimand the final pand ball mill

raph. A thermod the evaluatiors. The indicatrs. The results te set of samplhe complete dir comprehensio

l of Engineerin

re- 3 Refrigera

tion cycle: (1) B

performed in omental analysisarticle size dishomogenizer

couple for temon of the partitors thus obtaithus obtained es, and the negstributions of ton.

ng, Science and

ated vacuum m

Blanching (2)

order to evaluas has been pertribution of proboth equipped

mperature moniicle size distribined have beenfor the comple

gligible effect the full set of

d Technology,

milling chamber

cutter mill (3)

ate the quality rformed by meocessed almond with a vacutoring was alsobution for all tn averaged amete protocol wof the differenexperiments (

Vol. 2, No. 9, 2

r

Ball Mill (4) s

of the produceasuring the nunds. The procesuum pump ando mounted in ththe samples, a

mong the replicwere extremely nt raw material16 samples ext

2010, pp. 69-82

sieve (5) silo

ct obtained by utritional valuess has been card a refrigeratiohe milling chamand the compaates, in order similar, thus d

l lots. Due to thtracted after th

2

means of the e (protein and rried out in an on system, as mber.

arison of their to reduce the

demonstrating he substantial he cutter mill)

77 Aielloo et al./ Internaational Journall of Engineerin

a-

b

c-Cumu

ng, Science and

- Knife mill

b-Ball mill

ulative knife m

d Technology,

mill

Vol. 2, No. 9, 22010, pp. 69-82

2

78

Thandvariwas Exthe distdistdist Thshoto a

(*)A

(**)

Ththe pastto dfoun

Aiello

he particle sized 500 μm. Finiations and dids also extractedxperiments condistribution is

tribution belowtribution was otribution he comparisonws that the tra

an extent of app

Almond Board

)USDA Nation

he protein conformulation o

te recipe involdenaturation cand in the finis

o et al./ Interna

e distributions nally, the tempd not exceed 45d and the cumunfirm that the s completely rew 10 μm. In obtained and th

n of the proteinaditional almonproximately 35

d of California

nal Nutrient Da

tent in fact turnof the almond plves the additioan hence be esshed paste, as i

ational Journal

range betweenperature meas5°C confirmingulative functionfinal processinemoved, with this case also

he cumulative d

n content of rand paste manuf5% only.

Table 3 – Com

atabase

ns from 25% opaste and to th

on of 30-35% otimated to be an fact the cont

Protein

Tot Lip

l of Engineerin

d-Cum

Figure 5- Pa

n 1 μm and 100sured during thg the achievemn was calculateng phase in thean overall par

o, the milling distribution wa

aw almonds anfacturing proce

mparison of sel

of raw materialhe denaturatioof sugar and aabout 30%. Antent of lipids of

referen

raw

almond

n % 25.3

pids 49.5

ng, Science and

mulative ball m

article size distr

00 μm and havehe refrigerated

ment of a steadyed. These curvee ball mill allowrticle size belochamber temp

as calculated. T

nd almond pasess delivers the

lected indicato

l to 8% in the an effects. Rouromatic componalogously, apf raw almonds

nce

w

d(*)

Referen

almon

paste(*

3 8.81

5 27.75

d Technology,

mill

ribution

e a first peak ad cutting procy state conditioes are shown inws to achieve aow 100 μm anperature was b

These results, g

ste on the basie protein conte

rs with referen

almond paste. Tughly speakingounds to the puprox 56% of ts is 50% and b

nce

nd

**)

Percen

differen

65.18%

5 43.93%

Vol. 2, No. 9, 2

at 6 μm and a scess was appron. The averagn fig. 5c. an homogenize

nd a single peabelow 40°C. A

given in fig 5b

is of referenceent of the raw m

nce values

The remaining g we can consiure almond pathe fat content ecomes 27%. B

nt

nce

%

%

2010, pp. 69-82

econdary peakoximately 43°

ge percent distr

ed product whak at 5 μm andAgain the aveand 5d, again

ed values, givematerial to the

difference of 6ider that tradit

aste, the loss of in the raw ma

Based on the c

2

k between 400 °C with little ribution curve

ere the tail of d 65% of the erage particle show a stable

en in Table 3, final product

65% is due to tional almond f proteins due aterial can be

comparison of


79

referenced nutritional value, hence, almond paste results as a low nutritional content product, although the raw material has good initial nutritional properties. The fat and protein content of the product obtained according to the process here proposed was evaluated by means of a suitable experimental protocol carried out according to the procedure stated before in paragraph 2.3. The average protein content of the four replicates for the five sampling point are depicted in Figure 6 a, which also shows the error bars for each set of samples and the total average value. The same results are also given for the total fat content in Fig. 6 b.

(a)

(b)

Figure 6- Error bars for each set of samples and the total average value

Obtained results show that the process described in this study is effective in preserving the nutritional content of the raw material in the finished product, as in fact the protein content measured in the almond paste ranged from 10.7 % and 12 %, with an average value of 11.2% and a standard deviation of 0.59. The corresponding gain, compared with the reference values, ranges from 25.8% to 28.9% with an average value of approx 27%. Similarly, the lipids content measured in the 5 samples ranged from 34.2% to 32.6% with an average value of 33.6±0.5%. The corresponding gain is 21.07%. The comparison of protein and lipid content obtained by the experimental process and reference data is shown in Fig. 7 a and 7 b.

9

9.5

10

10.5

11

11.5

12

12.5

1 2 3 4 5 avg

Proteins %

29

30

31

32

33

34

35

36

1 2 3 4 5 avg

Tot Lipids (%)

80

6. C ThcolowhitechfattycrudtradCurhighselethe temby oby mconandprocRegfoodentidenaddof othe

Aiello

Conclusions

he consumers’our, aroma, tasich allow to dhnologies in foy oils emulsionde raw almondditional producrrently availablhly nutritional ecting a suitablnutritional con

mperature belowoxygen-free prmeans of a two

ntrol and oxyged 21% of total cesses, as for gister, 2007). Td safety to theire food chain.

naturation phenditional substanomega 6 fatty aproduct obtain

o et al./ Interna

Fi

’ concern abouste and texturedeliver higher ood processing ns has been invds with a highction process ile methods forfoods because

le raw materialntent of raw maw 50°C througrocessing. Expo step cutting en-free conditiolipids. There iexample the

The present rese employment

Additional resnomena mightnces. For examacids in the rawned should be

ational Journal

gure 7-Compa

ut the quality oe but also nutriquality producan lead to hi

vestigated. Such content of prs generally carr the effective e they require l with high nutraterials can be

ghout the entireerimental resuand homogenions allowed tois also nowadamandatory paearch relies onof selected raw

search towardst improve the

mple, in the casw material, thuassessed in or

l of Engineerin

arison of protein

of the products itious, and safcts. The purpogher quality pr

ch production proteins and fatrried out at higelimination orconditions tha

ritional conteneffectively con

e production pults show that ization process reduce the eff

ays the trend ofasteurization on preserving thew materials an the statistical results obtain

se considered ous obtaining a der to depict a

ng, Science and

(a)

(b)

n and lipid con

nowadays reqfe. As a result, ose of this resroducts. In parprocess well fittty acids whichgh temperaturer reduction of at cause denat

nt and by applynserved in the

process. Off-flait is possible to. The establishfects of denatuf ensuring food

of raw almonde nutritional va

nd to the impleoptimization oned. Also nutomega 3 fatty abetter product

a complete pict

d Technology,

ntent with refer

quires the indufood industry

search is to drticular the milts to the purpoh however aree (80-90°C) inoff-flavours ar

turation of proying proper profinal product.

avours generato achieve smalhment of an exuration and oxidd safety by me

ds enforced byalue during fooementation of sof process paratritional valuesacids should b. Finally, the imture of the pro

Vol. 2, No. 9, 2

rence values

strial productsy is researchingdemonstrate thalling process fose of the researe not conserven order to inhibre not compatiteins. This resocessing technoSuch result is oed by lipid oxl particles distr

xperimental prodation, recovereans of high tey the Californiod processing, safety monitor

ameters and a ms should be be added to balmprovement o

oblem here ana

2010, pp. 69-82

not only to beg new productiat the employor the productirch since it is c

ed in the final bit microbioloible with the psearch demonsologies, a signiobtained by ma

xidation have bribution (5 micocess involvingring 27% of themperature or hia Almond Bodemanding thering systems thmore detailed abalanced by alance the signifof organoleptic alyzed. Organo

2

e attractive in ion processes

yment of new on of almond carried out on product. The gical growth.

preparation of strates that by ificant part of aintaining the

been inhibited cron average) g temperature

he protein loss high pressure oard (Federal e assurance of hroughout the analysis of the adding proper ficant content properties of

oleptic quality


81

assessment of processed foods involves the preliminary definition of a sensorial profile described by specific criteria such as taste, aroma, texture, and the selection of pertinent indicators and testing procedures for each criteria. Such indicators are generally qualitative, requiring sensory analysis carried out by panel-tests for their evaluation, which is out of the scope of this paper. The development of a new process is in fact the result of an optimization approach based on objective indicators, such as those proposed in the present study, and a subjective parameters which require qualitative evaluation criteria and methods. It is premature to address the latter topic until a reference sensory profile is individuated for the product considered. Acknowledgement The authors are grateful to Mr. Lo Castro for his valuable experience and to Stramondo s.r.l. for providing the samples, to Leonardo Engineering for providing research support, to Prof. Bonina and his staff at the department of pharmaceutical sciences, University of Catania (Italy) for their valuable scientific support, and to Alfatest (Italy) for particle size measurement. References Amarowicz R., Troszynska A. and Shahidi F., 2005. Antioxidant activity of almond seed extract and its fractions, Journal of Food

Lipids, Vol. 12, pp. 244-358 Ames, B.N. 1983. Dietary Carcinogens and Anticarcinogens. Science, Vol. 221, pp. 1256-64 Buranasompoba A., J. Tangb, J.R. Powersa, J. Reyesb, S. Clarka, B.G. Swansona, 2007. Lipoxygenase activity in walnuts and

almonds, LWT - Food Science and Technology Vol. 40, No. 5, pp. 893-899. Burton, G.W., and Ingold, K.U. 1989. Vitamin E as an in Vitro and in Vivo Antioxidant. Ann. N.Y. Academy of Science 570, 7-

22 Charikleia P. Dimakou & Sotirios N. Kiokias & Ioanna V. Tsaprouni & Vassiliki Oreopoulou, 2007. Effect of Processing and

Storage Parameters on the Oxidative Deterioration of Oil-in-Water Emulsions , Food Biophysics, Vol. 2, pp. 38–45 Chen C.Y., Lapsley K. and Blumberg J., 2006. Perspective a nutrition and health perspective on almonds, Journal of the Science of

Food and Agriculture, Vol. 86, pp. 2245–2250. Cross, C.E. 1987. Oxygen Radicals and Human Disease. Annals of Internal Medicine, Vol. 107, pp. 526-45 De Pillia T., Jouppilab K., Ikonenc J., Kansikasd J., Derossia A. and Severinia C., 2008. Study on formation of starch–lipid

complexes during extrusion-cooking of almond flour, Journal of Food Engineering, Vol. 87, No. 4, pp. 495-504 Dickinson, E. 1992. An Introduction to Food Colloids. Oxford University Press, Oxford. Federal Register / Vol. 72, No. 61 / Friday, March 30, 2007 / Rules and Regulations. Grasselly, Ch. and Crossa-Raynaud, P. 1980. L'Amandier. Maisonneuve et Larose, Paris. Grasselly, Ch., Olivier, G. and Niboucha, A. 1992. Le caractère 'autocompatibilité' de l'amandier dans le programme de l'INRA. ln:

dh Colloque GßEMPA, Nimes, France, Rapport EUR 14081fr. Hamberg, M., and Samuelsson, B. 1967. On the specificity of the oxygenation of unsaturated fatty acids catalyzed by soybean

lipoxidase. Journal of Biological Chemistry 242, 5329 5335. Hamberg, M., and Hamberg, G. 1980. On the mechanism of the oxygenation of arachidonic acid by human platelet lipoxygenase.

Biochemical and Biophysical Research Communications 95, 1090-1097. Ignário R.,Caetano S. and Lannes S. 2007. Preparation of powdered egg yolk using a mini spray dryer, Ciência e Tecnololgia de

Alimentos, Campinas, 27, 787-792. Labuza, T. P. 1971. Kinetics of lipid oxidation in foods. CRC Critical Reviews in Food Technology, 2, 355–405. Lapsley K. G., 2003. Almond health benefits research, IFT Annual Meeting – Chicago. Mandalari, G., Nueno-Palop, C., Bisignano, G., Wickham, M.S.J., Narbad, A., 2008. Potential prebiotic properties of almond

(Amygdalus communis L.) seeds, Applied and Environmental Microbiology, Vol. 74, pp. 4264-4270. Menkes, M.S., Comstock, G.W., Vuilleumier, J.P., Helsing, K.J., Rider, A.A. and Brookmeyer, R., 1986. Serum Beta-carotene,

Vitamins A and E, Selenium, and the Risk of Lung Cancer. The New England Journal of Medicine, Vol. 315, pp. 1250-54. Meunier G. and Mengual O., 1996. A new concept in stability analysis of concentrated colloidal dispersions (Emulsions,

Suspensions, Foams, Gels), 4th World Surfactant Congress, Vol. 4, pp. 300 – 314. Niki, E., Yamamoto Y., Takahashi M., Komuro E., Miyama Y., 1989. Inhibition of oxidation of biomembranes by Tocopherol.

Ann. N.Y. Academy of Science, Vol. 570, pp. 23-31. Roux KH, Teuber SS, Robotham JM, Sathe SK. 2001 Detection and stability of the major almond allergen in foods. Journal of

Agricultural and Food Chemistry; 149, 2131-6. Sathe Shridhar K., Wolf Walter J., Roux Kenneth H., Teuber Suzanne S., Venkatachalam Mahesh , Sherman, P. 1976. The textural

characteristics and dairy products. In: J. M. De Man, P. W. Voisey, V. F. Rasper and D. W. Stanley (eds) Rheology and Texture in Food Quality. AVI, Westport, Connecticut, pp. 82–404.

Stanley, D. W. and TUNG, M. A. 1976. Microstructure of food and its relation to texture. In: J. M. De Man, P. W. Voisey, V. F. Rasper and D. W. Stanley (eds) Rheology and texture in Food Quality. AVI, Westport, Connecticut, pp. 28–78.


82

Sze-Tao Kar Wai, 2002. Biochemical characterization of amandin, the major storage protein in almond (Prunus dulcis L.) Journal of Agricultural and Food Chemistry, Vol. 50, pp. 4333-4341.

Stähelin HB, Gey KF, Eichholzer M, Lüdin E, Brubacher G. 1989. Cancer mortality and vitamin E status. Ann. N.Y. Academy of Science Vol. 570, pp. 391-99. Tazia S, Plantevina F, Di Falco C , Puigservera A and Ajandouza E H, 2009. Effects of light, temperature and water activity on the

kinetics of lipoxidation in almond-based products", Food Chemistry, Vol. 115, No. 3, pp. 958-964. Venkatachalam M, Teuber SS, Roux KH, Sathe SK 2002. Effects of roasting, blanching, autoclaving, and microwave heating on

antigenicity of almond (Prunus dulcis L.) proteins. Journal of Agricultural and Food Chemistry Vol. 50, pp. 3544–3548. UNI ISO 8968-1:2002 UNI 22605-1992 Wang YM, Purewal M, Nixon B, Li DH, Soltysiak-Pawluczuk D. 1989. Vitamin E and Cancer Prevention in an Animal Model.

Ann. N.Y. Academy of Science Vol. 570, pp. 383-91. WIPO Patent No. 03/003849 A2, Fatty Acid Compositions having superior stability and flavour properties, assigned to the Procter

and Gamble Co., Filed 16/01/2003. Wu X., Beecher G.R., Holden J.M., Haytowitz D.B., Gebhardt S.E. and Prior R.L., 2004, Lipophilic and hydrophilic antioxidant

capacities of common foods in the United States. Journal of Agricultural and Food Chemistry Vol. 52, pp. 4026–4037. Biographical notes Dr. Giuseppe Aiello graduated in Production Engineering at the University of Palermo, where he currently holds a position as researcher. His research activity is mainly concerned with production planning and supply chain management, with a focus on innovative technologies for agro-industrial productions and logistic systems for perishable products. he is also a consultant in the same filed and he has been responsible for a number of programs tailored to specific companies Prof. Luigi Cannizzaro is a full time professor at Dipartimento di Tecnologia Meccanica, Produzione e Ingegneria Gestionale (DTMPIG) at the University of Palermo. His Professorship focuses on mechanical engineering and industrial plant management. Prof. Cannizzaro’s industry experience includes extensive senior management consulting in the areas of strategic management, marketing planning, competitive analysis, and cost evaluation studies. Dr. Giada La Scalia graduated in Mechanical Engineering at the university of Palermo, where she also received a PhD in Production engineering. After a one year experience at the University of Texas Medical Branch (UTMB); she is currently attending a post-doc scholarship in Palermo. Her research activity is mainly focused on the development of new processes and technologies for industrial production systems. Received February 2010 Accepted October 2010 Final acceptance in revised form November 2010

MultiCraft


Vol. 2, No. 9, 2010, pp. 83-93



www.ijest-ng.com


The impact of new product introduction on supply chain ability to match supply and demand

R. Crippa1, L. Larghi2, M. Pero3*, A. Sianesi3

1*TruEconomy Consulting BV, Stationsstraat, 2, 4000 AM Tiel,, The NETHERLANDS

2*Livia Larghi, BTicino SpA, Viale Borri 231, 21100 Varese, ITALY

3* Politecnico di Milano - Department of Management, Economics and Industrial Engineering, Via Lambruschini, 4B, 20156, Milano, ITALY *Corresponding Author: e-mail: [email protected], Tel +39-02-23992819, Fax.+91-512-2590063

Abstract Supply chain managers should redesign their supply chains in response to the introduction of a new product. In particular, to reach performance targets, they should align the supply chain to the new product features. The objective of this paper is to highlight the negative effects of mis-alignment between product features and supply chains and to propose a set of mis-alignment indicators, along with an action plan to align supply chains to new products. To this end, an in-depth case study has been performed. In the analyzed company the introduction of a new product line was not followed by a proper redesign of the logistics network, thus reducing supply chain performance. The mis-alignment has been evaluated against a new indicator. Moreover, the main product features that should have been taken into account when redesigning the network, i.e. internal and external variety, and innovativeness, have been highlighted. Finally, a two steps methodology to define a set of coordinated action between product development department and supply chain mangers have been proposed. Keywords: Variety; Alignment; Supply Chain; New Product Introduction

1. Introduction Nowadays, firms should be able to launch into the market a growing number of new products. However, there is no reason why a supply chain that is optimal for a given set of product lines stays optimal when the level of variety changes. Therefore, each time a new product is launched in the market, the supply chain should be redesigned so to be able to deliver the new product efficiently and effectively to the market. Product features affect supply chain performance, while being defined during New Product Development (NPD). The magnitude of the effects of product features on supply chain performance is determined by supply chain decisions concerning supply chain structure (Blackhurts et al. 2005), supply chain strategy, e.g. agile or lean (Childerhouse et al. 2002), or the degree of collaboration among the actors of the supply chain (Doran et al. 2007). Figure 1 depicts these relations, i.e. the main framework which this paper is based on. In particular, it shows that NPD process results in new products characterized by a set of product features. Supply chain managers design the supply chain and define its features. Supply chain performance depends on the matching of supply chain features and product features. Alignment is reached when supply chain performance is maximized. For instance, Swatch can profitably offer a wide variety of products since it has implemented both a modular design of its products and it has adopted flexible manufacturing systems (Montreuil and Poulin 2005). In fact, without flexible manufacturing systems, Swatch could not exploit completely the benefits of modular design, and vice-versa. Within this context aligning supply chain design decisions with NPD decisions has become crucially important to maintain high supply chain performance and to boost product launch effectiveness (Van Hoek and Chapman 2006, Fine, 1998; Lee and Sasser, 1995). However, despite the complex interdependencies among product design and supply chain design decisions have been recognized as early as Hoekstra and Romme (1992), until Fine (1995) this insight did not enter the realms of competitive strategy nor capture the attention of top management (Forza and Rungtusanatham, 2005). In particular, the drawbacks of a mis-alignment between product design decisions and supply chain design decisions have not been investigated in depth, nor indicators to measure

Crippa et al. / International Journal of Engineering, Science and Technology, Vol. 2, No. 9, 2010, pp. 83-93

84

NPD-Supply Chain Management (SCM) alignment have been developed so far. Therefore, the objective of this paper is to fill these gaps in literature. In particular, the aim of this work is to highlight the need for alignment through the exploration of the negative business effects of misalignment between NPD and SCM. Moreover, the paper proposes a set of mis-alignment indicators, along with some possible actions to mitigate the negative impact on performance of a mis-alignement.

Figure 1. Proposed framework representation The paper is organized as follows. In sections 2 we set out a theoretical review of studies that deal with the relations among product features, SCM decisions and supply chain performance. In particular we focus on the analysis of the literature about product structure, product variety and innovativeness. In section 3 we present the methodology used, i.e. in depth case study research. In facts, a case study that shows the worsening of performance when NPD and SCM are not aligned and the mis-alignment indicator used to assess such a mis-alignment are presented in section 4. In section 5 we discuss the case study implications while providing some conclusions and the directions for future research. 2. Background An analysis of the literature shows that the main product characteristics that affect the relations among product features, supply chain features and clients needs are: product variety and product structure, i.e. product architecture and bill of materials, and product innovativeness. Table 1 summarizes the supply chain related variables, along with performance impacted by product features. 2.1 Product variety Nowadays, customers demand for customized products forces firms to increase product range, i.e. increase the variety they offer in the market. Product variety encompasses both external variety, i.e. the range perceived by the clients, and internal variety, i.e. the diversity of components and semi-finished products (Pil and Holweg 2004). Product variety is defined during NPD process. This decision affects supply chain performance. For instance, when product variety increases, direct manufacturing costs, manufacturing overhead, delivery times and inventory levels increase as well (Ocampo y Vilas and Vandaele 2002, Ramdas and Sawhney 2001, Fisher and Ittner 1999, Fisher et al. 1999, MacDuffie et al. 1996). Brun et al. (2006) introduce and define the concept of behavioural costs as “those costs which arise because of the reaction of people to “excessive” variety”. In particular, these costs are due to human and/or organisational mechanisms which prevent the available variety to be effectively tackled and deployed. They rise in all those cases when people think the decisional task to choose among various options is not that relevant or could take much time to be completed, so that they exploit less variety than the designed one. To deal with higher variety some tools, e.g. information systems, web-based platforms or flexible automated systems (Coronado et al. 2004, Jiao et al. 2005, Forza and Salvador 2002), should be implemented, thus increasing costs as well (Fisher and Ittner 1999). Prasad proposes a rough index to measure of cost of variety connected to not only manufacturing costs but also plant layout or supplier changes (Prasad 1998). The magnitude of the impact of variety on the supply chain performance depends on SCM choices. For instance, the impacts of variety on a firm depends on its inherent flexibility (Ramdas 2003, Berry and Cooper 1999) and centralization degree of final assembly (Tynjälä and Eloranta 2007) . De Silveira (1998) develops a framework for the choice of the proper flexibility strategy to deal with high product variety in manufacturing environment. Some empirical and conceptual researches extended this concept to some aspects of SCM (Salvador et al. 2002, Randall and Ulrich 2001). 2.2 Product structure and innovativeness Product design is one of the product-related drivers which impacts the most SCM decisions and supply chain performance (Salvador et al. 2002). Indeed, product design information is needed for generating manufacturing plans and schedule, and also for creating a packing plan for shipment (He et al. 2006). Two representations of product design are mainly addressed: product

Product features

Performance

New Product Development

Supply Chain Design and Management

Supply chainfeatures


85

architecture and Bill of Materials. Product architecture plays a pivotal role among NPD and SCM (Krishnan and Ulrich 2001). Some relations among product architecture and SCM have been investigated (Fixson 2005), in particular, focusing on sourcing (Novak and Eppinger 2001, Hsuan 2001), postponement strategy and implementation decisions (Hsuan Mikkola and Skjøtt-Larsen 2004, Van Hoeck 2001, Lee and Tang 1998, Feitzinger and Lee 1997) and supply chain structure (Salvador et al. 2004, Fine 1995).

Table 1. Literature analysis

Product feature(s) Supply Chain related variable(s) Performance Reference(s)

Bill of Material Supply chain structure Total cost of supply chain

Huang, Zhang and Liang, 2005; Blackhurts, Wu and O’Grady, 2005; Lee and Sasser, 1995

Architecture Sourcing, postponement strategy, supply chain structure

Costs, service level

Hsuan Mikkola and Skjøtt-Larsen, 2004; Novak and Eppinger, 2001; Hsuan, 2001; Van Hoeck, 2001;Lee and Tang, 1998; Feitzinger and Lee, 1997; Fine, 1995

Variety and architecture Sourcing, production scales, supply chain configuration

Operational performances

Salvador, Rungtusanatham and Forza, 2004; Salvador, Forza and Rungtusanatham, 2002;

Manufacturing Direct costs, overhead, delivery lead times, inventory

Ocampo y Vilas and Vandaele, 2002; Ramdas and Sawhney, 2001; Fisher, Ramdas and Ulrich, 1999; Fisher and Ittner, 1999; MacDuffie, Sethuraman and Fisher, 1996

Supplier change Costs Prasad, 1998

Information systems Costs and demand mismatch

Coronado et al. 2004, Jiao et al. 2005, Forza and Salvador 2002

Variety

Manufacturing flexibility Costs De Silveira, 1998

Product innovativeness Supply chain strategy Operational performance and service level

Fisher, 1997; Childerhouse et al. 2002

Mathematical models that support designers in choosing the best Bill of Materials, or generic Bill of Materials, that minimizes the total cost of the supply chain have been proposed as well (Huang et al. 2005, Blackhurst et al. 2005, Lee and Sasses 1995). In these models, supply chain structure is defined concurrently with the product, among a set of possible configurations. There is a strong relation among product structure and product variety. Variety is mainly addressed in NPD literature in the main trade-off “variety – commonality”, i.e. the architecture definition phase (Ulrich and Eppinger 2000), or in the platform definition one (Huang et al. 2005, Farrell and Simpson 2003, Martin and Ishii 2002, Krishnana and Gupta 2001, Fisher et al. 1999, Robertson and Ulrich 1998). Product architecture decision affects the commercial variety that can be proposed in the marketplace at a given cost (Ulrich 1995). As far as innovativeness is concerned, it is the degree of newness of a product. It has been studied mainly in relation to supply chain strategy definition (Fisher, 1997; Childerhouse et al. 2002), although the empirical work by Caridi et al. (2009) shows the impact of product innovativeness on supply chain operative choices too. 3. Methodology An in-depth case study has been performed. Since NPD is a project based activity, i.e. discontinuous process, we used a approach based on the analysis of the discontinuities introduced by NPD. A discontinuity is the introduction of a change in the product range of a firm, e.g. a new product or a new product line. We called it a “delta” approach, as we analyzed the variables in terms of “differences” or “deltas” between the value of each analyzed variable after and before the discontinuity, i.e. the point in time when the new product is introduced. The need to perform such an analysis guided the choice of the case study. In facts, the firm has been selected for the high and growing variety of its product range and for it has recently introduced a new product line and it has deeply changed the structure of its product offer. The unit of analysis is the supply chain. Interviews have been carried on with Supply Chain Director and Manufacturing Plant Manager on the basis of a questionnaire. Documentary analysis and data analysis have been performed as well. Four main issues have been investigated: (i) the features of the new product introduced in terms of variety, modularity, innovativeness and sales; (ii) the features of the supply chain before and after the introduction of the new product, in terms of both supply base and


86

manufacturing capabilities; (iii) the effects on supply chain performance; and (iv) the actions (when undertaken) to mitigate the negative effects on performance. For confidentiality reasons, company names referred in the case study are imaginary and some figures have been rearranged, being careful in avoiding any alteration in performed analyses, findings and problem defining and solving processes actually happened. 4. The Industrial Example 4.1 Company Profile Company A is the Electronics Division of ELETECH Group, an European multinational company in the medium-to-low-voltage electrical appliances sector. Company A designs, engineers and manufactures products for communication, e.g. porters, video-porters, and domotics, e.g. touch screen control stations, dimmers, sensors and command/control devices based on open information technology architectures which enable full protection and control in domestic, industrial and commercial applications. Company A offers domotics systems (more than 2,000 products generating a yearly turnover of around 150 million Euros), fully integrated in style and connection with the rest of ELETECH catalogue (more than 60,000 products). Its products are marketed under various group brands, functionally and aesthetically targeted to specific markets and brand policy strategies. Being innovative has always been both one of the company’s strengths and the very core of its lifestyle. “Should it may be something new to study or develop in electrical appliances sector, we must come first in doing it” was the motto of one of the co-founders. Much of ELETECH historical growth was due to this credo, strengthened in the past decade by targeted acquisitions all over the world. Company A experienced in the years between 2002 and 2006 a significant volume increase, at a Compound Annual Growth Rate higher than 15%, with more and more relevance in new targeted markets. Nearly 16% of Company A turnover comes from products in their early lifecycle phase, and almost all of them are strategically kept inside Company A plants: outsourcing policies are generally applied to higher volume, mature items. 4.2 Company A supply chain before the introduction of the new product Company A main plant is located in Italy, as well as product marketing, design, engineering and logistics departments. In the Italian plant, two activities are performed: (i) assembly of boards by inserting components and electronics controls, and (ii) finished product assembly. The first set of activities is performed on automated assembly lines, the latter on semi-automated and manual assembly lines. Company A also acts as coordination unit for other manufacturing facilities in Europe and Far East. Company A policy is to keep internally the production of complex products, therefore Company A subcontracts only the production of simple products or standard high value products. Part of the mature products are manufactured in low-cost countries. Company A plays a focal role in the group electronics purchasing. Company A purchasing categories are four:

(i) standard electronics components are supplied from East Europe or Far East regions. (ii) customized plastic components are partly supplied from Italian (local) suppliers. The parts mostly visible to customers

or where aesthetics elements are important are supplied from the other ELETECH associate companies (inter-site flows).

(iii) packaging are supplied from Italian (local) suppliers. These are the same for all ELETECH associate companies. Company A clients are the distributors. Domestic market is served through ELETECH channels, other countries are served through Company A’s affiliates companies in the other countries. The last group accounts for half of the sales volumes. 4.3 The features of the new product In the 2nd half of 2005, a new top-level, stylish product family was introduced in Company A offer. In the same period Company A product offer shifted towards more integrated systems. The new product family encompasses domotic solutions for domestic application, e.g. switches, touch screens and command station for control of lighting in the houses. The new product line presents a wide variety of choices in terms of colours, materials and shapes of the external parts of the switches and control panels, along with different internal technologies. The introduction of the new product line was not followed by a redesign of Company A supply chain. As it will be detailed in the following, this led to a decrease in the main operational performance and increased difficulties in following demand mix variation. A detailed analysis of data regarding the features of the new product introduced, in terms of variety, modularity, innovativeness and sales, outlined that the characteristics of the new products that mainly impacted supply chain performance and determined the difficulties were: (i) items sales distribution, (ii) bill of materials complexity and (iii) product and production process technologies novelty. A Pareto analysis on Company A 2006 cumulative sales evidences that, instead of the expected 50% on products count, the so called “C-class” items represent more than 67% of the catalogue. The items that fall between the 95% and 100% of cumulative sales are called C-class items. Company A clients purchase systems. Therefore all the items, including C-class, must be marketed with high service level standards. Unavailability of a single product may heavily impact perceived non-fulfilment of client needs. The new product line is composed by items characterized by an higher number of both links and levels in the bill of materials than before. This increases managerial complexity in planning and managing parts procurement and products manufacturing.


87

The new product line is composed by a high percentage of electronics components. The firm does not own internally the competence to produce these components, therefore it had to increase the percentage of purchase from external suppliers. In addition, some of these components, e.g. touch screens, SIM cards holders, are used in other consumer electronics products, e.g. cameras, camcorders, smart phones. The consumer electronics market is dominated by big players. In this market, Company A has relatively low purchasing volumes, thus having relatively low bargaining power for negotiating lots and lead-times. So, Company A is more exposed to supply risks in cases of components shortages on the market. In addition being consumer electronics products’ lifecycle extremely short, Company A is forced to follow the pace of components quick obsolescence, increasing the amount of technological modifications to its products and unwittingly coping with obsolescence rates functionally unnatural for domotics competitive arena. New, i.e. never used before by the firm, production process technologies have been introduced with the new product line. Many of these are involved in manufacturing processes directly managed by Company A main plant. Table 3 shows the effects on technologies saturation of the introduction of the new line in the Company A main plant. In 2003 Company A main plant managed 14 different technologies, with low (LO) to medium (ME) capacity saturation. Capacity saturation is expressed by planned weekly capacity against theoretical maximum one. In 2005 the number of technologies to be managed were 23. Capacity saturation shows decline in mature technologies, i.e. pre-forming and THT components mounting, and increasing in the saturation of recent ones (SMD-based assembly), with high (HI) to very or extremely high (HI+; HI++) rates. Particularly notable is the effect of components miniaturisation, leading to the intense use of SMD micro-components and double-sided Printed Circuit Boards. Following state-of-the-art design trend, more and more "Top Level" products aesthetics involve painting and chromium-plating technologies, supplied, as well as plastic moulding, by other ELETECH facilities. This leads to an increase in inter-site dependency for Company A main plant.

Table 2. Complexity increase in technologies portfolio and capacity saturation

Technologies Capacity Saturation level

[planned weekly capacity / theoretical maximum weekly capacity]

Phase Kind of technology 2003 2004 2005 2006 2007 Pre-forming ME LO LO LO LO THT components assembly ME ME ME ME LO SMD Printed Circuit Boards assembly - Standard LO LO ME HI HI+ SMD Printed Circuit Boards assembly - BGA LO LO ME HI SMD Printed Circuit Boards assembly – Complex Printed Circuit Boards

LO ME HI

Flexible Printed Circuit Boards assembly LO LO LO SMD Printed Circuit Boards assembly – micro components

LO

Printed Circuit Boards tropicalization LO ME ME HI+ HI+ Firmware uploading LO ME HI+ HI+ Printed Circuit Boards tuning LO ME HI+ HI+ Pr

inte

d ci

rcui

ts b

oard

s ass

embl

y

In-circuit testing – No test point LO ME ME Tampoghraphy LO LO HI HI+ HI++ Ultra sound soldering LO LO LO ME ME Keys/Buttons sub-assembly LO ME HI Gas detectors final assembly LO LO LO LO LO Electromechanical manual sub-assembly LO ME ME ME ME Electromechanical automatic assembly LO ME HI HI+ HI++ KIT final assembly LO ME ME HI HI Accessories and add-ons assembly LO ME ME HI HI+ Displayed equipped items final assembly LO LO ME HI Radio controlled items final assembly LO LO LO Instruction sheet printing LO LO LO LO LO Compact Disk burgning LO LO ME HI+ HI++ En

d ite

m fi

nal a

ssem

bly

and

pack

agin

g

Label printing LO ME ME HI+ HI+ Technologies count 14 18 23 23 24

Legend: LO = Low, ME = Medium, HI = High, HI+ = very high, HI++ = extremely high


88

4.4 The effect on supply chain performance After the introduction of the new product line, a reduction in the Company A service performance occurred. This reduction is measured against increased difficulties in reaching service level targets, measured against both Line Item Fill Rate and percentage on average weekly gross requirements, and Master Planning (Master Production Schedule) accuracy, which flickered around an average of 75%, against a Materials Requirements Planning (MRPII) standard target of 95%. Further analyses on these key performance indicators evidenced that, mainly, service level difficulties were originated by in-sourced (or partially subcontracted) items. This reflects Company A policy to concentrate internally most part of manufacturing complexity (Table 2), coming from low-volume and phase-in items. Main explanations on poor Master Production Schedule performance were to be largely found in short-term production schedule variations and purchased materials unavailability, as Figure 2 (related to Master Production Schedule non-performance causes) shows. In particular, materials unavailability grew from 36% of non-performance causes in 2004 to more than 52% in 2006. Information system reports show that material unavailability was mainly due to planning problems at the suppliers and suppliers production capacity exceeding. The incidence of delays in transportation, material defects and delays due to late or wrong communication of information on quantities or means of production, e.g. moulds, have not increased from before the introduction of the new product.

Master Production Schedule non-

performances causes

2006 Work Order

% [Work

order/total]Materials unavailability 3,084 52,4%Priority variation 1,855 31,5%Materials poor quality 251 4,3%Workforce unavailability 63 1,1%Machine unavailability 59 1,0%Force majeure 52 0,9%Quality inspection delay 3 0,1%Miscellaneous 513 8,7%Total 5,880 100%

52,4%

31,5%

16,0%

Materials unavailability

Priority variation

Others

Figure 2. MPS non-performance causes of Company A Master Plan

The worsening of the key performance indicators was not due to increased capacity saturation. As a matter of facts, as Figure 3 highlights, apart from some outlining peaks (generally quickly recovered) Company A manufacturing was able to track volume growth and variability.

3,000.00

2,500.00

2,000.00

1,500.00

1,000.00

500.00

0.00

MANUFACTURING

DEMAND

volu

me

units 3,000.00

2,500.00

2,000.00

1,500.00

1,000.00

500.00

0.00

MANUFACTURING

DEMAND

volu

me

units

Figure 3. Demanded vs. Manufactured volumes at Company A


89

Created and transported varieties dynamics and their alignment with customer orders were therefore investigated. Data analyses show that almost the entire product range is actually requested by clients. Therefore, created variety and customers orders are “equal”, i.e. all the variety offered on the market was actually ordered by the clients. A measure of transported variety that could be comparable to customer orders was needed. A measure was applied to Company A 2003-2006 weekly operations key figures: demand and manufacturing (in its widest meaning of purchasing and/or internal production) of finished goods. This measure is known in Company A as Tracking Ratio (TKR) and is calculated as follows (1):

TKRi = MCi / DCi (1) where: i = ith working week MCi = count of different manufactures items in week ith

DCi = count of different demanded items in week ith

TKR measures the rate between the variety manufactured and customer orders. Operational steps implemented for computing Tracking Ratio were the following. First we got historical demand and manufacturing weekly data (triplet: Item/date/quantity), then we counted different demanded and manufactured items per period. Finally we divided manufactured by demanded item count. The analysis performed at Company A identified a progressive decline in Tracking Ratio starting from mid 2005, and that phenomenon becomes even more evident by plotting DCi, i.e. the count of different demanded items in week ith, and MCi, i.e. the count of different manufactures items in week ith, as in Figure. 4.

mid 2005

MANUFACTURED ITEMS PER WEEK

DEMANDED ITEMS PER WEEK

1,400.00

Item

s co

unt

1,200.00

1,000.00

800.00

600.00

400.00

200.00

0.00

mid 2005

MANUFACTURED ITEMS PER WEEK

DEMANDED ITEMS PER WEEK

1,400.00

Item

s co

unt

1,200.00

1,000.00

800.00

600.00

400.00

200.00

0.00

Figure 4. The increasing gap between demanded and manufactured variety

By this analysis, along with the performances analysis of section 4.4, mis-alignments in terms of efficiency and effectiveness have been therefore measured. In facts, the number of different products ordered were not delivered in the time and at the costs requested. The graph in Figure 4 represents by MCi, i.e. the count of different manufactures items in week ith, the the sub-array [Number of different product, time] that Company A supply chain is able to deliver, whereas DCi, i.e. the count of different demanded items in week ith, represents the variety requested by clients in time, i.e. the customer orders. By comparing the rate of different products that the supply chain delivers to the rate requested, effectiveness mis-alignment has been measured. Operational performance targets in terms of capacity saturation or material stock levels were not reached neither. In fact, at the aggregate level the total amount of items produced equals the total amount of items sold (as it has been shown in Figure 3), but since the requested


90

mix of product variants is different from the mix of product variants actually delivered (as it has been shown in Figure 4), stock level of those products that were not requested, but produced, has increased. Figure 4 shows also that Company A supply chain is not able to deliver more variety than the actual one, as average MCi, i.e. the count of different manufactures items in week ith, is horizontal. 4.5 The applied counter-measures to reach alignment In order to reach alignment, Company A has made some analysis and undertaken some long term and short term actions. Meetings between marketing managers, product developers and supply chain managers have been held to discuss the actions and define a roadmap. A two steps methodology has been defined: (i) Requested variety definition and (ii) Matching product features and supply chain features. In the first step, i.e. requested variety definition, marketing managers analyze jointly with product developers the expected requested variety, i.e. identification of unexpected demand growth scenarios, main risk areas in terms of volumes, demand peaks and variety. In this way a list of products that can present managerial problems for the supply chain is defined, e.g. products with strong expected demand variation and/or high uncertainty on the demand mix. The products in this list are called “risky products”. In the second step, i.e. matching product features and supply chain features, product developers and supply chain managers define an action plan to concurrently change the product structure and/or the supply chain so to guarantee transported variety to equal requested one, for the defined list of risky products. For instance, reverse engineering techniques can be adopted to define product architecture and bill of materials so to make feasible the application of late differentiation by increasing commonality and standardization, e.g. in the packaging and instruction booklets, and to reduce the supply market risk. Other examples of actions are: demand forecast techniques support the identification of products with high demand uncertainty for which higher safety stocks might be needed; to support faster re-planning of the supply chain, new planning systems can be introduced. Table 3 summarizes the actions taken in the specific case. It should be noted that the basic structure of the supply chain has not been changed and supplier involvement in the product development is low. This is due to the firm policy to keep development inside the company and to manage complexity internally. The same firm policy applies to the risky products, which will be in any case kept inside Company A manufacturing facilities. The results in terms of performance improvements are definitely positive: notwithstanding the persistence of strong growth rates and even increased demanded variety, Company A late orders reached their historical minimum in last two years.

Table 3. Actions taken in Company A

Step Area Analysis Action

1 Requested variety definition Market analysis and

forecast of future trends

Definition of “risky” products list (where risky products are those that may generate managerial problems for the supply chain, e.g. products with

strong expected demand variation and/or high uncertainty on the demand mix)

Product feature

Reverse engineering and analysis of the product structure of the above

identified “risky” products

Definition of possible architecture and bill of materials modification to the apply to “risky

products”

Analysis of demand variance expected for

risky products

Setting of specific “Strategic Stocks” (apart from usually computed Safety Stocks) on critical

components more likely involved in unexpected variety and volume growth Plan

Analysis of present and emerging planning needs

Renewal of the Planning System: introduction of an Advanced Planning and Scheduling (APS) application

Analysis of manufacturing flows Manufacturing flows simplifications

Make Analysis of machine loadings

Internal capacity increase on most critical (saturation and/or short-term outsourcing difficulties)

technologies Part commonality and product components

analysis Alternative sources opening

2

Matching product features and supply

chain features

Source Analysis of demand variance expected for

risky products

Pre-identification of product clusters for tactical outsourcing, in case of sudden demand increase on

risky items


91

5. Conclusions Nowadays, to remain competitive firms should be able to sustain innovation by coordinating the two processes of NPD and SCM. NPD-SCM alignment is fundamental for reaching NPD-SCM coordination. Literature analysis shows that interrelated product features are of interest when addressing NPD-SCM alignment are product structure, product variety and product innovativeness. Variety, both internal and external, is created during NPD process while SCM decisions can affect the ability of a supply chain to deliver efficiently and effectively such variety to the marketplace. A difference among created variety, transported variety and customer orders results in lower supply chain performances, i.e. a mis-alignment is measured. When this occurs the determinants should be sought in the mis-alignment between new product features, SCM practices and customer needs. A framework that outlines these relations have been proposed. The case study supports the framework. Indeed Company A experienced an enlargement in the product offer and some radical innovations to its traditional products, i.e. an increase in the bill of materials complexity, and in the variety and number of product and process technologies that should be managed by the firm’s supply chain. The consequences of these phenomena on supply chain were not at first fully evaluated thus resulting in lower operational and service level performances. The weekly analysis of unavailable items evidenced that the long-term cause of perceived difficulties in keeping required service level was to be sought in overall rigidity in following demanded mix. Finally, the case study highlights the negative effects in terms of fulfilment of client needs and operational performances of a lack of alignment between new product features and SCM. The root cause of this lack of alignment was a the introduction of a new product line whose implications on SCM were not simultaneously planned in full. Company A case study suggests that the product features that should be taken into account in SCM choices are the internal and external variety of the new product line, i.e. bill of materials complexity and number of different products, and the innovation content of the new product line. As a matter of fact, the innovativeness, i.e. novelty for the firm, of the technologies to be managed at product and production process level represents a major problem for the supply chain, e.g. the purchasing department. In addition, a two step approach to define counter-measures to overcome mis-alignment has been proposed. In the first step the requested variety is defined, whereas in the second one, the supply chain features matching the new product features are draw out. It must be noted, that the actions should be defined by a team composed by supply chain, marketing and product development managers. We acknowledge that more empirical research should be done on the framework. In particular, relevant measures and indicators that case-by-case are needed to evaluate alignment should be identified. This can be the theoretical basis for developing a methodology to support NPD-SCM alignment and can pave the way to the definition of new managerial approaches, new models and solutions to reach alignment. References Berry, W. and Cooper, M., 1999. Manufacturing flexibility: methods for measuring the impact of product variety on performance

in process industries. Journal of Operations Management, Vol. 17, pp. 163-178. Blackhurts, J., Wu, T. and O’Grady, P., 2005. Pcdm a decision support modeling methodology for supply chain, product and

process design decision. Journal of Operations Management, Vol. 23, pp. 325-343. Brun, A., Capra, E. and Miragliotta G., 2006. Behavioural costs in manufacturing: how to balance standardization and variety

costs. Proceedings of XIV International Working Seminar on Production Economics - Innsbruck, Austria, Feb., pp. 20-24. Caridi, M., Pero, M. and Sianesi, A. 2009, The impact of NPD projects on Supply Chain complexity: an empirical research,

International Journal of Design Engineering, Vol. 2, No. 4, pp. 380 – 397 Childerhouse, P., Aitken, J. and Towill, D., 2002. Analysis and design of focused supply chain. Journal of Operations

Management, Vol. 20, pp. 675-689. Coronado, A.E., Lyons, A.C., Kehoe, D.F. and Coleman, J., 2004. Enabling mass customization: extending build-to-order concepts

to supply chains. Production Planning and Control, Vol. 15, No.4, pp. 398-411 De Silveira, G., 1998. A framework for the management of product variety. International Journal of Operations and Production

Management, Vol. 18, No.3, pp. 271-285. Doran, D., Hill, A., Hwang, K., Jacobs, G. and Operations research group, 2007. Supply Chain modularisation: cases from the

French automobile industry. International Journal of Production Economics, Vol. 106, No. 1, pp. 2-11. Farrell, R. and Simpson, T., 2003. Product platform design to improve commonality in custom products. Journal of Intelligent

Manufacturing, Vol. 14, pp. 541-556. Feitzinger, E. and Lee, H.L.,1997. Mass customization at Hewlett Packard: the power of postponement. Harvard Business Review,

Vol. 75, No 1, pp. 116-121. Fine, C., 1995. Clockspeed. Winning Industry control in the age of temporary advantage. Perseus Book Fisher, M. 1997, “What is the right supply chain for your product?”, Harvard Business Review , Vol 75, No. 2, pp. 105-116. Fisher, M. and Ittner, C.,1999. The impact of product variety on automobile assembly operations: empirical evidence and

simulation analysis. Management Science, Vol. 45, No.6, pp. 771-786. Fisher, M., Ramdas, K. and Ulrich, K., 1999. Component sharing in the management of product variety: a study of automotive

braking system. Management Science, Vol. 45, No. 3, pp. 297-315.


92

Fixson ,S.K., 2005. Product architecture assessment a tool to link product, process and supply chain decisions. Journal of Operations Management, Vol. 23, pp.345-369

Forza, C., Rungtusanatham, M. 2005. Coordinating product design, process design, and supply chain design decisionsPart A: Topic motivation, performance implications, and article review process. Journal of Operations Management, Vol. 23, No 3-4, pp. 257-265.

Forza, C., Salvador F., 2002. Managing for variety in the order acquisition and fulfillment process: The contribution of product configuration systems. International Journal of Production Economics, Vol. 76, pp. 87–98.

Hoekstra, S., Romme, J., 1992. Integrated Logistics Structures: Developing Customer Oriented Goods Flow. McGraw-Hill, London, UK.

Hsuan Mikkola, J., Skjøtt-Larsen, T., 2004. Supply chain integration: implications for mass customization, modularization and postponement strategies. Production Planning & Control, Vol. 15, No. 4, pp. 352-361.

Hsuan, J., 2001. Impacts of supplier-buyer relationships on modularization in new product development. European Journal of Purchasing and Supply Chain Management, Vol. 5, pp.197-209.

Huang, G., Zhang, X.Y. and Liang, L., 2005. Towards integrated optimal configuration of platform products, manufacturing processes, and supply chains. Journal of Operations Management, Vol. 23, pp. 267-290.

Krishnan, V. and Gupta, S., 2001. Appropriateness and impact of Platform-Based Product Development. Management Science, Vol. 47, No.1, pp. 52-68.

Krishnan, V. and Ulrich, K., 2001. Product development decisions: a review of the literature. Management Science, Vol. 47, No. 1, pp. 1-21.

Jiao, J., Zhang, L., Pokharel, S., 2005. Coordinating product and process variety for mass customized order fulfilment. Production Planning and Control, Vol. 16, No. 6, pp. 608-620.

Lee, H., Sasser, M., 1995. Product universality and design for supply chain. Production Planning & Control, Vol. 6, No. 3, pp. 270-277.

Lee, H. and Tang, C., 1998. Variability reduction through operational reversal. Management Science, Vol. 44, No.2, pp. 162-172 MacDuffie, J.P., Sethuraman,K. and Fisher, M., 1996. Product variety and manufacturing performance: evidence from the

international automotive assembly plant study. Management Science, Vol. 42, No.3, pp. 350-369. Martin, M. and Ishii, K., 2002. Design for variety: developing standardized and modularized product platform architectures.

Research in Engineering Design, Vol. 13, No.4, pp. 213 – 235. Montreuil, B., Poulin, M., 2005, Demand and supply network design scope for personalized manufacturing. Production Planning

and Control, Vol. 16, No. 5, pp. 454-469. Novak, S. and Eppinger, S., 2001. Sourcing by design: Product complexity and the Supply Chain. Management Science, Vol. 47,

No.1, pp. 189-204. Ocampo y Vilas, C. and Vandaele, N., 2002. A cost and operations based product heterogeneity index. International Journal of

Production Economics, Vol. 79, pp. 45–55. Pil, F. and Holweg, M., 2004. Linking product variety to order-fulfillment strategies, Interfaces, Vol. 34, No. 5, pp. 394-403. Prasad, B., 1998. Designing products for variety and how to manage complexity. Journal of Product and Brand Management, Vol.

7, No. 3, pp. 208-222. Ramdas, K., 2003. Managing product variety: an integrative review and research directions. Production and Operations

Management, Vol. 12, No. 1, pp. 70-101. Ramdas, K. and Sawhney, M., 2001. A cross-functional approach to evaluating multiple line extensions for assembled products,

Management Science, Vol. 47, No. 1, pp. 22-36. Randall, T. and Ulrich, K., 2001. Product variety, supply chain structure and firm performances: analysis of the US Bicycle

Industry. Management Science, Vol. 47, No.12, pp. 1588-1604. Robertson, D. and Ulrich, K., 1998. Planning for product platforms. Sloan Management Review, Vol. 39 (Summer), pp. 19-31. Rungtusanatham, M. and Forza C., 2005. Coordinating product design, process design, and supply chain design decisions. Journal

of Operations Management, Vol. 23, No. 3, pp. 257-260. Salvador, F., Forza, C. and Rungtusanatham, M., 2002. Modularity, product variety, production volume, and component sourcing:

theorizing beyond generic prescriptions. Journal of Operations Management, Vol. 20, pp.549-575 Salvador, F., Rungtusanatham, M. and Forza, C., 2004, Supply-chain configurations for mass customization. Production Planning

and Control, Vol. 15, No. 4, pp. 381-397. Tynjälä, T., and Eloranta E., 2007. Investigating the effect of product variants, and demand distributions on the optimal demand

supply network setup. Production Planning and Control, Vol.18, No.7, pp. 561 – 572. Ulrich, K., 1995. The role of product architecture in the manufacturing firm. Research policy, Vol. 24, pp. 419-440. Ulrich, K., Eppinger, S., 2000. Product Design and development. McGrawHill Van Hoek, R.I., 2001. The rediscovery of postponement a literature review and directions for future research. Journal of

Operations Management, Vol. 19, pp. 161-184. Van Hoek, R. and Chapman, P., 2007. How to move supply chain beyond cleaning up after new product development. Supply

Chain Management: An International Journal, Vol. 12, No.4, pp. 239–244.


93

Biographical notes Roberto Crippa - Social and Economical Disciplines cum laude Degree at Bocconi University, Milan (Italy), APICS/CPIM certification and Master cum laude in General Management at Politecnico of Milan (Italy), Roberto Crippa devoted to supply chain management 22 years of professional career. He worked in multinational Management Consulting and manufacturing Companies. Consultant Partner in the Board of Assologistica/Cultura & Formazione, Senior Advisor at C-Log (Research Centre for Logistics, LIUC –University Carlo Cattaneo of Castellanza, Italy) and lecturer at LIUC University, he is presently Director, Italian Operations at TruEconomy Consulting. Livia Larghi - Born in 1972, in 1998 she got her Master Degree in Production and Management Engineering at Politecnico di Milano. From April 1999 to November 2000, she has worked as Quality Control manager in the holding of a small textile company. From November 2000, she has been working in Bticino. In this company, she has worked in different departments: Methods and Organization, Supply Chain Processes Improvement and Extended Supply Chain. Since April 2010, she works in the Purchasing department, where she is manager of New products and technologies purchasing marketing. Margherita Pero - Researcher at Politecnico di Milano. In 2010 she got her PhD at Politecnico di Milano (Italy), with a thesis dealing with New Product Development and Supply Chain Management Alignment. In June 2004 she got her Master Degree with honours in Production and Management Engineering at Politecnico di Milano. She is lecturer at the course of “Analysis and redesign of business processes”, she is junior lecturer of "Operations and supply chain management" at Bachelor level and "Production Plants Design and Management" and “Sourcing and Purchasing” at the Master of science degree. Andrea Sianesi - Full professor at the Politecnico di Milano, Associate Dean at MIP, the business school of Politecnico di Milano and member of the managing board of ASFOR the Italian association for the development of managerial education. In the past he's been researcher and associate professor at the Politecnico di Milano and was appointed to teach at Brescia University, LIUC and Bocconi University. In 2008/09 he has been visting professor at Tongji University, Shanghai, China. At Politecnico di Milano he teaches "Operations and supply chain management" in the BSc degree course and "Suppy Chain Management" in the MSc degree course. He also teaches Supply Chain Management both in the MBA and Executive MBA at MIP. His researches are focused on Operations and Supply Chain Management topics, he has published 6 books and over 60 articles on international journals and conference proceedings and participated to a high number of international and national research projects. Received June 2010 Accepted November 2010 Final acceptance in revised form November 2010

MultiCraft


Vol. 2, No. 9, 2010, pp. 96-106



www.ijest-ng.com

2010 MultiCraft Limited. All rights reserved

Implementation of critical success factors in construction research and development process

U. Kulatunga*, D. Amaratunga and R. Haigh

School of the Built Environment, The University of Salford, UK *Corresponding Author: e-mail: [email protected], Tel +44 0161 295 6943

Abstract Construction research and development (R&D) process has a number of issues that affect its success. These issues imply that Critical Success Factors (CSFs) of construction R&D process are not properly addressed. Not knowing CSFs could lead to not implementing them and not paying proper attention for them. The study investigates CSFs of construction R&D process and their implementation/consideration during the R&D process. A comprehensive literature review was used first to develop construction R&D process. CSFs and their implementation/consideration were evaluated by a questionnaire survey. Construction R&D process was derived with four phases namely Initiation, Conceptualizing, Development and Launch and Management activities that support coordination and resourcing of R&D process. Study revealed that, as a whole there is a gap between the importance of success factors against their implementation/consideration as majority of CSFs are not properly implemented compared to the importance attached to them. Keywords: Construction R&D process, Critical success factors, Implementation, Consideration 1. Introduction Research and Development (R&D) has been identified as an overarching strategy for construction industry to address its challenges (Barrett 2007; Hampson and Brandon, 2004) such as to improve the efficiency and effectiveness of construction processes and materials; address growing concerns of environmental considerations and health and safety issues; comply with sustainable development requirements; and address cost, time, and quality parameters of construction projects. Fairclough (2002) suggests that innovation driven by R&D as a way forward if the society needs to be benefited from a modern, efficient, high quality construction industry. Not limiting the importance within the UK, R&D is being identified as a key factor which develops the construction industries worldwide (Fox and Skitmore, 2007). Despite the importance of R&D activities for the growth of the construction industry, there are number of issues, which affect its success. A low level of investment can be identified for UK construction R&D when compared with countries like France, Japan and Scandinavia (Gann, 2000) and when compared with other sectors like manufacturing (Department for Business Enterprise and Regulatory Reform, 2007; Institute of Civil Engineers, 2006; DTI, 2006; Dulaimi et al, 2002; Fairclough, 2002; Seaden and Manseau, 2001; Laing, 2001; Egan, 1998). One of the main reasons for low investment is improper reporting of R&D expenses (Seaden and Manseau, 2001) and inadequate mechanisms to evaluate the successfulness of activities (Lorch, 2000). People question the value of R&D when clear links between its benefits and the financial commitments are not established. Further, when the expectations of the participants of construction R&D activities are not met, a low level of contribution from industrial partners is evident (Barrett and Barrett, 2003). Moreover, lack of feedback on the progress and success of R&D activities and lack of communication between the parties involved (Dulaimi et al, 2002) have reduced the interest and attraction for contributors to ongoing construction R&D activities. It is being evident that construction R&D activities lack effective communication, feedback and validation procedures, and coordination between the parties involved in the process (Gann, 2001; Lorch, 2000). Above issues imply that factors that are critical for the success of construction R&D process are not properly addressed. Lack of knowledge and understanding of Critical Success Factors (CSFs) could lead to repercussions of paying insufficient attention on them. Further, not knowing the CSFs could result in focusing on factors which are less important for the success of construction

Kulatunga et al. / International Journal of Engineering, Science and Technology, Vol. 2, No. 9, 2010, pp. 94-106

95

R&D process. This highlights the value of proper identification of CSFs. Studies carried out in other disciplines suggest that there could be a gap between the factors that are important and those that are implemented (Sun and Wing, 2005) as lack of knowledge of the success factors could lead to lack of implementation/consideration in practice. Even though there are a number of studies carried out on identifying CSFs related to R&D in other disciplines (see Cooper and Kleinschmidt, 2007; Sun and Wing, 2005; Roberts, 2002; Sawhney and Prandelli, 2000; Cooper, 1999; Lester, 1998), paucity of studies is evident in evaluating CSFs of construction R&D process and their actual implementation/consideration. Therefore, this study explores the implementation/consideration of CSFs in construction R&D process. The paper is structured as follows. Firstly, the paper derives phases of construction R&D process via a comprehensive literature review. Secondly, it provides the research methods used for the study. Following this, CSFs of construction R&D process are presented. Implementation/consideration of CSFs is analyzed next followed with a discussion based on the importance and implementation/consideration of CSFs of construction R&D process. 2. Construction Research and Development Process The life cycle of a new venture (new product/process/services) can be divided into a number of distinct phases. The exact division of these phases is governed by the complexities of the final output, management structure of the organization etc. (Aw, 2005). The development of a new venture can involve a number of activities which are carried out by multidisciplinary teams, different departments and are influenced by various decisions. By considering these factors Saren (1984) identifies five types of models that represent life-cycle of a new venture.

• departmental stage models: based on the departments or functions which hold responsibility for the tasks carried out in the innovation process;

• active stage model: based on the activities that are performed; • decision stage model: represent the innovation process as a series of evaluation points to decide if the work should go

ahead or be abandoned; • conversion process model: based on the concept that the innovation process is a conversion of inputs to outputs; and • response model: focuses on the individuals’ or organisations’ response to change of ideas or project proposals in terms of

acceptance or rejection of ideas or proposals. There are strengths and weaknesses within the above models. The departmental stage model has the disadvantage of handling the idea in isolation within departments, and is characterized by the lack of ownership of the idea (Lim et al, 2006). The involvement of cross functional expertise and activities carried out during each stage is identified in the active stage model. However, this model assumes straightforward progression without indicating any alternative paths available (Saren, 1984). Further, the activities are supported by relevant departments thus passing the tasks from one department to the next (Takeuchi and Nonaka, 1986). The activities are seen, therefore, as the responsibility of the departments, creating similar drawbacks to the departmental stage models. The decision stage model consists of specific decision points to evaluate the success of activities and can be incorporated in the department stage and active stage models. Saren (1984) claims that the aforementioned models indicate that the new venture moves in a rational manner. The conversion process model takes the standpoint that conversion of inputs to outputs avoids assigning the responsibility to separate departments (Hart and Baker, 1994), avoids the sequential approach and the presence of activities (Saren, 1984). The response model is based on the responses to a change of idea/proposal thus evaluating the factors which influence the decision to move ahead or to reject (Hart and Baker, 1994). In addition to the above models which represent the involvement of different decisions, activities, departments, and responses, the life cycle of a new venture can be divided into number of distinctive phases. Pillai et al (2002) divide it into three phases: project selection phase (initial screening, detailed evaluation, project selection); project execution phase (effective resource management to accomplish project goals within the stipulated time and cost); and project implementation phase (focusing on customer satisfaction and return on investment). Further, there are number of models proposed by various authors depicting various activities in a new venture development (see Table 1). It is noticeable that the phases of those models proposed by different authors follow a similar pattern, whilst activities coincide with one another.


96

Table 1: Phases and activities involved in new venture development

By reviewing the characteristics of the models, the authors categorize the phases of development of new venture into four categories as Initiation, Conceptualization, Development and Launch (Figure 1).

Initiation Conceptualisation Development Launch

Figure 1: Phases of a new venture

The initiation phase involves idea generation regarding the new venture. This is followed by the conceptualization phase, which involves identifying the requirements of the parties involved and available resources and carrying out an analysis to check the feasibility of the new venture. The third phase involves the actual development and piloting of the new venture to test its validity. Finally the product will be launched at the launch phase. Some models consider a maturity phase where they examine the effect of the new venture on the market (see Price, 2004). Table 1 summarizes leading models of new venture development in relation to the identified four categories. For the new venture to be successful within its life cycle, it requires a number of management roles, such as effective coordination of activities, communication, resource management and evaluation of output against the goals. By combining the phases of the new venture with the management activities that R&D process needs for its success, authors derived R&D process as shown in Figure 2.


Management

Input Output

Figure 2: Construction R&D process

When designing the R&D process pertaining to this study, the concepts of “active stage” and “conversion” models were used (see Saren, 1984). Agreeing with Saren (1984) the authors also believe that the R&D process should not be a rational or sequential one. Nevertheless, the authors believe that the identification of activities involved within the phases of R&D process would help to prioritize them and lead to the successful accomplishment of them. The identification of activities involved during different phases would facilitate effective controlling and monitoring of the activities. It ensures the establishment of milestones and short term goals for their accomplishment, during a particular phase, and direction of the team members towards those goals. Though it is recommended to overcome the phase based approach and to integrate the phases, Sun and Wing (2005) comment that such integration could dilute the essential activities involved in R&D work. Thus, the model designed for this study combines the characteristics of the active stage and conversion process models acknowledging the iterative processes, while representing the activities involved within each phase for ease of understanding of the R&D work. Below diagram shows the issues (Refer Section 1) mapped against the construction R&D process.

Snelson and Hart (1991) Theije et al. (1998) Loch and Tapper (2000)

Cooper (2001) Price (2004) Moultrie et al. (2006)

Initiation Idea generation Screening ideas

Concept stage

Generate idea

Discover scope

Opportunity recognition

Project generation

Conceptualisation Concept development Business analysis

Specification stage Basic design stage Detail design stage

Select fund Generate concept Define specs

Business case

Opportunity focusing Commitment of resources

Requirement capture Concept design

Development Product development Test marketing

Engineering stage

Design Test

Develop Test and Validate

Market entry

Implementation

Launch Commercialisation

Launch Launch

Full Launch and Growth

Maturity Maturity and expansion Liquidity event


97


Management

Input Output

Low level of investment

Lack of clear objectives

Objectives not addressing the

needs

Insufficient involvement of the industrial

partners

Low level of applicability

Lack of awareness of resource utilisation

Lack of evaluation

mechanisms

Lack of collaboration

Lack of coordination/

communication

Lack of

feedback

mechanisms

Figure 3: Issues within the construction R&D process

Having identified the construction R&D process, next section discusses the research methodology used form the study. 3. Research Methodology 3.1 Data Collection The scope of the study is based on collaborative construction R&D activities carried out between universities and industry. University-industry partnerships are acknowledged as a better way of carrying out construction research activities as they blend theory with practice to gain much success for the research effort (Gilkinson and Barrett, 2004; Calvert and Patel, 2003). During the first stage of the study, a comprehensive literature review was carried out to derive construction R&D process. The development of the R&D process critically evaluated new product development models and their characteristics (Refer Section 2). During the second stage of the study, identification of CSFs of construction R&D process and their actual implementation/consideration was evaluated. To identify CSFs related to construction R&D process, 13 semi-structured interviews were carried out (five principal investigators, five researchers and three industrial partners). By using NVivo software, the interview transcripts were coded to identify the success factors revealed by the interviewees. In addition to the semi-structured interviews, an extensive literature review was carried out on the CSFs in other disciplines. Success factors gathered from empirical investigation via semi-structured interviews and literature review were combined to prepare the questionnaires (Refer Table 2 for the response rate of the questionnaire survey). Within the questionnaire, the success factors were structured according to the phase of the construction R&D process derived from the literature review namely Initiation, Conceptualization, Development, Launch and for Management.

Table 2: Response rate for the questionnaire survey Category Number of

questionnaires sent

Number of responses received

Response rate

Principal Investigators: represent the university and manage and lead the R&D process

& Researchers: represent the University and carries out research work related to the project

55 34 62%

Industrial Partners : representatives from construction organizations who contributes to the R&D process

74 26 35%

Both importance and implementation/consideration of success factors during their lifecycle was gathered from the questionnaire by using five scale Likert scales (Refer Table 3). The Likert scale to evaluate importance of success factors comprised of Very Important, Important, Moderately Important, Of the little Important and Unimportant where as the Likert scale to evaluate implementation/consideration of success factors comprised of Always, Very Often, Sometimes, Rarely and Never (Refer Table 3). In addition to the above values, no opinion/not applicable columns were added to both Likert scales to avoid respondents giving incorrect answers due to lack of knowledge or opinion for a particular question (Krosnick, 2002).


98

Table 3: Sample of the questionnaire survey indicating the format and values assigned for Likert scale 1. Initiation Phase: This involves the idea generation to select the most suitable option for a research project The extent of importance The extent of consideration/ implementation

Uni

mpo

rta

nt

Of

little

im

port

ance

Mod

erat

ely

impo

rtan

t

Impo

rtan

t

Ver

y Im

port

ant

No

Opi

nion

N

/A

Nev

er

Rar

ely

Som

etim

es

Ver

y of

ten

Alw

ays

No

Opi

nion

N

/A

Understand the market and its dynamics Establish the research problem clearly Selecting a competent team Leadership of the principal investigator Commitment of the principal investigator Consider funding bodies’ requirements Consider industrial partners’ requirements Consider researchers’ requirements Other (please specify)

3.2 Data Analysis CSFs of construction R&D process were evaluated by considering the responses received regarding the importance of success factors. Responses received for the questionnaire survey regarding the importance of success factors were subjected to two filtering stages to evaluate CSFs of construction R&D process. During the first filtering stage, success factors that received an overall mean value (total mean value received from principle investigators, researchers and industrial partners) less than four were excluded from further analysis. This filtering was done on the premise that mean value less than four indicates unimportant (value 1), of the little important (value 2) or moderately important (value 3) success factors (refer Table 3 for the values assigned for the questionnaire survey). For those success factors obtained an overall mean value including four and above were subjected to the second filtering stage by using the Wilcoxon signed rank test. The Wilcoxon signed rank test is a non-parametric method to test the differences of two related variables when the subject (dependant category) is measured on two occasions or under different conditions (Hill and Lewicki, 2007; Pallant, 2001). By taking a consecutive pair of data, the Asymptotic significance was calculated. The Asymptotic significance shows an estimate of the significance of differences within attributes being tested (Pallant, 2001). Generally, Asymptotic significance less than 0.05 is considered as indicating a significant difference between the attributes being tested. Accordingly, the paired data which showed an Asymptotic significance < 0.05 was considered as responses having a significant difference regarding the importance of the success factors, hence such factors were considered as not critical for the success of construction R&D process (refer to Table 4 for total mean values and Asymptotic significance of CSFs). After identifying the CSFs, their implementation/consideration during the construction R&D process was done by analyzing the total mean values obtained from the questionnaire regarding the “implementation of success factors” (refer to Table 4 for the mean values obtained for the implementation/consideration of success factors). The above section discussed the research methodology used for the study. Section below provides the CSFs of construction R&D process. 4. Findings 4.1 Critical success factors of construction research and development The study developed a number of CSFs for construction R&D process by analyzing the questionnaire survey. Summary of CSFs of construction R&D is presented in Table 4.

Table 4: CSFs and their implementation/consideration during the R&D process

Initial Phase Importance of the success factors

Implementation/ consideration of the success factors

Mean Rank Asymptotic Significance Mean Rank

Establish the research problem clearly 4.79 1 N/A 3.97 2

Commitment of the principle investigator 4.56 2 0.06 3.80 3

Select a competent team 4.48 3 0.51 3.59 8


99

Table 4 (cont’d): CSFs and their implementation/consideration during the R&D process

Initial Phase (cont’d) Importance of the success factors



Leadership of the principle investigator 4.28 4 0.08 3.69 6

Consider industrial partners’ requirements 4.27 5 0.99 3.72 4

Consider funding bodies’ requirements 4.26 6 0.87 4.02 1

Understand the market and its dynamics 4.13 7 0.45 3.72 5

Consider researchers’ requirements 3.79 8 3.66 7 Conceptualising Phase

Check the feasibility of the project 4.75 1 N/A 3.82 3


Committed and cooperative team members 4.53 3 0.55 3.60 9

Establish clear and realistic goals/ deliverables/ milestones 4.51 4 0.99 3.79 4

Adequate resources/financial support 4.44 5 0.52 3.75 6 Allocation of responsibilities to team members inline with competencies 4.39 6

0.61 3.44 12

Establish a plan to disseminate research results 4.39 7 1 3.90 2


Having a skilled team 4.30 9 0.76 3.66 8

Establish clear method to measure success 4.30 10 1 3.20 16

Consider industrial partners’ requirements 4.30 11 0.95 3.52 10

Consider funding bodies’ requirement 4.28 12 0.97 3.98 1

Absence of lengthy bureaucracy 4.00 13 0.03 2.93 18

Early involvement of industrial partners 4.00 14 3.34 13

Comprehensive briefing process 3.98 15 3.33 14

Recognition for team members 3.92 16 3.21 15

Consider researchers’ requirements 3.84 17 3.46 11

Fast decision making process 3.72 18 3.18 17

Development Phase

Committed and cooperative team members 4.59 1 N/A 3.64 6


Adequate resources/financial support 4.56 3 0.91 3.79 3

Having a skilled team 4.51 4 0.55 3.57 7

Meet funding bodies' requirements 4.51 5 0.99 3.93 1

Share a common understanding about the work 4.41 6 0.29 3.52 8

Well establish operational procedure 4.39 7 0.91 3.36 12

Meet industrial partners’ requirements 4.39 8 0.98 3.46 9

Momentum/ motivation of the team 4.38 9 0.91 3.39 10

Flexibility and responsiveness to change 4.38 10 1 3.38 11


100

Table 4 (cont’d): CSFs and their implementation/consideration during the R&D process

Development Phase (cont’d) Importance of the success factors




Absence of lengthy bureaucracy 4.11 12 0.02 3.08 15

Meet researchers’ requirements 4.08 13 3.75 4

Recognition for team members 4.02 14 3.21 13

Fast decision making process 3.95 15 3.10 14

Having a risk mitigation strategy 3.95 16 2.78 17

Testing the market 3.92 17 3.00 16

Launch Phase

Effective dissemination of the results 4.52 1 N/A 3.54 4

Meet funding bodies' requirements 4.49 2 0.73 3.90 1

Having a well established dissemination/ marketing plan 4.48 3 0.88 3.33 6

Meet industrial partners’ requirements 4.40 4 0.46 3.64 2

Launch the output within the planned time frame 4.36 5 0.93 3.41 5

Comprehensive project review and feedback 4.05 6 0.03 3.28 7

Meet researchers’ requirements 3.89 7 3.56 3

Refinement of the output after launch 3.84 8 3.00 8 Management

Effective communication 4.70 1 N/A 3.59 2

Effective collaboration 4.62 2 0.28 3.52 4

Effective planning, controlling, and organising of activities 4.52 3 0.29 3.54 3

Continuous reviews 4.48 4 0.53 3.66 1

Effective resource management 4.34 5 0.19 3.31 6

Effective management of the people 4.33 6 0.85 3.38 5

Having an external person to do reviews 3.98 7 3.16 7

Evaluating post delivery success 3.95 8 2.98 8

Having a separate project administrator 3.43 9 2.89 9 * Success factors written in Italic letters are none-critical factors

Asymptotic Significance values are not shown for non-critical factors For the detail descriptions about above CSFs, please refer Kulatunga et al. (2009). 4.2 Implementation/consideration of critical success factors By considering overall mean values (Table 4) of CSFs and their implementation/consideration during the R&D process Radar diagrams were prepared (Figure 4 to Figure 8). These figures compare the importance and implementation/consideration of success factors during initiation, conceptualizing, development and launch phases and at management.


101

1

2

3

4

5Establish the research problem clearly

Commitment of the principal investigator

Select a competent team

Leadership of the principal investigator

Consider industrial partners’ requirements

Consider funding bodies’ requirements

Understand the market and its dynamics

Consider researchers’ requirements

Importance of the success factors Implementation/consideration of the success factors

Figure 4: Comparison of the importance of success factors against their implementation/consideration at the initiation phase At the initiation phase all the success factors except for “considering funding bodies’ requirements” have obtained mean values less than 4 for their implementation/ consideration. It can be noted that “considering the funding bodies’ requirements”, “establishing the research problem clearly” and “commitment of the principal investigator” are being identified as the most implemented/considered factors while “selecting a competent team” and “considering researchers’ requirements” as the least implemented/considered factors. Figure 5 illustrates the importance and implementation/consideration of success factors at the conceptualizing phase. Within this phase, all the success factors have acquired a mean value less than 4 for their implementation/ consideration. Nevertheless, similar to the initiation phase, “considering funding bodies’ requirement” has been ranked as number one indicating higher consideration given it. “Establishing a plan to disseminate research results” and “checking the feasibility of the project” is ranked second and third respectively, while “a fast decision making process” and “absence of a lengthy bureaucracy” as the least implemented/considered factors.

1

2

3

4

5 Check the feasibility of the project


Committed and cooperative team members

Establish clear and realistic goals/deliverables/ milestones

Adequate resources/financial support

Allocation of responsibilities to teammembers inline with competencies

Establish a plan to disseminate researchresults


Having a skilled team

Establish clear method to measure success

Consider industrial partners’ requirements

Consider funding bodies’ requirement

Absence of lengthy bureaucracy

Early involvement of industrial partners

Comprehensive briefing process

Recognition for team members

Consider researchers’ requirements

Fast decision making process


Figure 5: Comparison of the importance of success factors against their implementation/consideration at the conceptualizing

phase


102

Similar to the initiation and conceptualizing phases, all the success factors have obtained mean values of less than 4 at the implementation/consideration during the development phase (see Figure 6). Again, “addressing the requirements of the funding body” has been ranked number one, while “commitment of the principal investigator” and “having adequate resources” have been ranked two and three according to their implementation/ consideration.

1

2

3

4

5 Committed and cooperative team members


Adequate resources/financial support

Having a skilled team

Meet funding bodies' requirements

Share a common understanding about thework

Well establish operational procedure

Meet industrial partners’ requirements

Momentum/ motivation of the team Flexibility and responsiveness to change


Absence of lengthy bureaucracy

Meet researchers’ requirements

Recognition for team members

Fast decision making process

Having a risk mitigation strategy

Testing the market


Figure 6: Comparison of the importance of success factors against their implementation/consideration at development phase

At launch, addressing the funding bodies’ and industrial partners’ requirements have been selected as the factors that were mostly implemented/ considered (see Figure 7). The success factors “refinement of the output after launch” and “carrying out project reviews and feedback” are identified as being the least implemented/considered factors. Corresponding to the other phases, at the launch phase also all the success factors obtained their mean values less than 4.

1

2

3

4

5 Effective dissemination of the results

Meet funding bodies' requirements

Having a well established dissemination/marketing plan

Meet industrial partners’ requirements

Launch the output within the planned timeframe

Comprehensive project review andfeedback

Meet researchers’ requirements

Refinement of the output after launch


Figure 7: Comparison of the importance of success factors against their implementation/consideration at the launch phase


103

Carrying out continuous reviews and effective communication are identified as being the most implemented/considered success factors when managing the R&D process (see Figure 8). “Engaging a separate person to undertake project administration work” and “evaluation of post delivery success” has been selected as the least implemented/considered factors.

1

2

3

4

5 Effective communication

Effective collaboration

Effective planning, controlling, andorganising of activities

Continuous reviews

Effective resource managementEffective management of the people

Having an external person to do reviews

Evaluating post delivery success

Having a separate project administrator


Figure 8: Comparison of the importance of success factors against their implementation/consideration at project management 5. Discussion Overall, the empirical data indicates that the majority of success factors (except for “considering the funding bodies’ requirements”) are not very often (value 4) or always (value 5) implemented/considered during the construction R&D process but are implemented sometimes (value 3). This indicates that CSFs are not adequately implemented/considered during the actual process even though they are identified as important for the success of construction R&D process. The success factors identified as non-critical (refer to Table 4), have generally been ranked low at the implementation/consideration (except for “meeting the researchers requirements” during the development and launch phases). This gives a positive correlation between the importance and implementation/consideration of non-critical success factors. During the actual implementation of the R&D process, the funding bodies’ requirements were taken as the most considered factor (Refer Table 4). This indicates the prominence given in fulfilling the requirements of funding bodies as the satisfaction of funding bodies leads to safeguarding of future funding opportunities for construction R&D projects. Though the empirical investigation of this study highly valued the importance of commitment of principal investigator for the R&D process (Rank 2 at initiation, conceptualization and development phases), during the implementation stage of the R&D project, the commitment of the principal investigator was not ranked highly when compared to its importance. In contrast to the findings of the empirical investigation of this study, Peansupap and Walker (2006) identify the influence of senior management for proper diffusion of innovation. Selecting a competent team during the initiation phase and having a skilled team during the conceptualizing and development phases obtained ranks below 7 (Refer to Table 4) indicating they are not given sufficient implementation/consideration during construction R&D process. Similarly, having a multi-skilled team in order for a construction organization to be innovative was not given much attention in the study carried out by Manley and McFallan (2006). Although “selecting a competent team” has been ranked third according to its importance at the initiation phase, it has been ranked eighth at the implementation. Similarly, factors “committed and cooperative team members” at the conceptualizing and development phases (rank 3 and 9, 1 and 6 respectively), “allocation of responsibilities to team members in line with competencies” at the conceptualizing phase (rank 6 and 12) have taken higher rankings for their importance when compared with their implementation. This indicated that these factors are not given due consideration during the implementation when compared to their importance. Accordingly, some factors showed an inconsistency between the importance and implementation based on their assigned ranks. Such inconsistency of CSFs based on the importance and implementation was identified in the study carried out by Sun and Wing (2005).


104

6. Conclusions The study investigated the CSFs and their implementation during construction R&D process. Despite the importance of R&D to construction industry, there are number of issue that hinders its success. This study suggests the identification of CSFs and evaluation of their implementation during the R&D process as a way forward to enhance success of construction R&D process. The study argued that identification of CSFs and their actual implementation/consideration during R&D process could lead to giving proper attention for the factors that are highly important for the success of construction R&D process. Study identified a number of CSFs of construction R&D process from inception, conceptualizing, development and launch phases and at management. The results revealed that, when compared with the importance, it is seldom that almost all the CSFs are given enough attention during the actual implementation of the construction R&D process. Identification of CSFs from the study provides a good foundation for their effective management of them to provide required resources and attention by concentrating on few critical factors that are required for the success of construction R&D process. The fact revealed from the study that prominence attached to the importance of success factors were not given when it comes to their implementation during the construction R&D process will also help to pay more attention for the success factors during their actual implementation stage. As a way forward, it can be suggested to link CSFs with performance measures so that addressing performance measurement targets can ensure implementation of CSFs to enhance construction R&D activities. Even though there are a number of studies carried out on identifying CSFs, they are based in other disciplines such as manufacturing etc. Thus, this study contributes to the theory by deriving construction R&D specific CSFs and integrating them with the phases of the R&D process from initiation, conceptualising, development to launch and at the management of R&D activities. Evaluation of the actual implementation/consideration of CSFs contributed to management and practice by identifying the CSFs that are not implemented/considered properly during the construction R&D process. The scope of the study is based on collaborative construction R&D activities carried out between universities and industry. The findings derived from the study therefore can be generalized within collaborative construction R&D activities between universities and industry. This can be identified as a limitation of the study. References Aw, K. C. 2005, Integrating quality and reliability assessment into product development process, International journal of quality

and reliability management, Vol. 22 No. 5, pp. 518-530 Barrett, P. 2007, Revaluing Construction: a holistic model, Building Research and Information, Vol. 35 No.3, pp. 268–286 Barrett, S. P., and Barrett, L. C. 2003, Research as a kaleidoscope on practice, Construction management and economics, Vol. 21

No. 7, pp. 755-766 Calvert, J., and Patel, P. 2003, University- industry research collaborations in the UK: bibliometric trends, Science and public

policy, Vol. 30 No. 2, pp. 85-96 Cooper, R.G. 1999, From experience: the invisible success factors in product innovation, Journal of Product Innovation and

Management, Vol. 16 No. 2, pp. 115–133 Cooper, R.G. 2001, Winning at New Products: Accelerating the Process from Idea to Launch, 3rd edition, Perseus publishing,

Cambridge Cooper, R.G., and Kleinschmidt, E.J. 2007, Winning businesses in product development: the critical success factors, Research

Technology Management, Vol. 50 No. 3, pp. 52-66 Department for Business, Enterprise & Regulatory Reform, 2007, Draft Strategy for Sustainable Construction, A consultation

paper, (accessed: Feb 2008), available online at: http://www.berr.gov.uk/files/file40641.pdf Dulaimi, M F., Ling, F.Y.Y., Ofori G., and De Silva N. 2002, Enhancing integration and innovation in construction, Building

research and information, Vol. 30 No. 4, pp.237-247 Egan, J. 1998, Rethinking construction: Report from the construction task force, Department of the environment, transport and

regions, UK Fairclough, J., 2002, Rethinking construction innovation and research: A review of government R and D policies and practices,

Department of Trade and Industry, London Fox, P., and Skitmore, M. 2007, Factors facilitating construction industry development, Building Research and Information, Vol.

35 NO. 2, pp. 178–188 Gann, D. M. 2001, Putting academic ideas into practice: technological progress and the absorptive capacity of construction

organisations, Construction management and economics, Vol. 19 No.3, pp. 321-330 Gann, D.M. 2000, Building innovation, Thomas Telford, London Gilkinson, N., and Barrett, P. 2004, The unanticipated impacts of research on practice, CIB world congress, 2nd – 7th May, Canada Hampson, K., and Brandon, P. 2004, Construction 2020: A vision for Australia’s property and construction industry, CRC

Construction Innovation, Australia Hart, S. J., and Baker, M .J. 1994, The multiple convergent processing model of new product development, International

marketing review, Vol. 11 No. 1, pp. 77-92


105

Hill, T., and Lewicki, P. 2007, STATISTICS Methods and Applications. StatSoft, Tulsa, (accessed: February 2008), available from: http://www.statsoft.com/textbook/stathome.html

Institute of Civil Engineers, 2006, Infrastructure research priority, (accessed: March 2007), available from: http://www.ncrisp.org.uk/Publications/Infrastructure%20Research%20Priorities.pdf

Krosnick, J.A. 2002, The causes of no-opinion responses to attitude measures in surveys: They are rarely what they appear to be. In R.M. Groves, D.A. Dillman, J.L. Eltinge, & R.J. A. Little (Eds), Survey nonresponse, John Wiley & Sons, New York

Kulatunga, U, Amaratunga, D, and Haigh, R 2009, Critical Success Factors of Construction Research and Development, Construction Management and Economics, Vol. 27. No. 9, pp. 891-900

Laing, M. 2001, Yesterday, today and tomorrow - lessons from the past we can take forward into the future, Reading construction forum, Oxford

Lester, D.H., 1998, Critical success factors for new product development, Research Technology Management, Vol.41 No.1, pp.16-21 Lim, L. P. L., Garnsey, E., and Gregory, M. 2006, Product and process innovation in biopharmaceuticals: a new perspective on

development, R&D Management, Vol. 36 No.1, pp. 27-36 Loch, C. H., and Tapper, U. A. S. 2000, R&D performance measurement that are linked to strategy, working paper, INSEAD,

(accessed 5th March 2005), available from: http://ged.insead.edu/fichiersti/inseadwp2000/2000-14.pdf Lorch, R. 2000, Improving the communication of academic research to the construction industry, Construction research and

innovation strategy panel, (accessed 20th February 2006), available online from: http://ncrisp.steel-sci.org/Publications/9916frLo.pdf

Manley, K. and Mcfallan, S., 2006, Exploring the drivers of firm-level innovation in the construction industry, Construction Management and Economics, Vol.24 No. 9, pp.911-920.

Moultrie, J., Clarkson, P. J., and Probert, D, 2006, A tool to evaluate design performance in SMEs, International Journal of Productivity and Performance Management, Vol. 55 No. 3/4, pp. 184-216

Pallant, J. 2001, SPSS survival manual. A step by step guide to data analysis using SPSS for windows (Version 10-11). Open University Press, Buckingham

Peansupap, V. and Walker, D.H. T. 2006, Innovation diffusion at the implementation stage of a construction project: a case study of information communication technology, Construction Management and Economics, Vol. 24 No.3, pp. 321-332

Pillai, A. S., Joshi A., and Rao, K. S. 2002, Performance measurement of R&D projects in a multi-project, International Journal of Project Management, Vol. 20 No. 2, pp. 165-177

Price, R. W. 2004, Road map to Entrepreneurial success: powerful strategies for building a high profit business, AMA-COM, New York.

Roberts, G. 2002, SET for success: The supply of people with science, technology, engineering, and mathematics skills, HM Treasury, UK

Saren, M. 1984, A classification of review models for the intra-firm innovation process, R&D management, Vol. 14 No.1, pp.11-24

Sawhney, M., and Prandelli, E. 2000, Communities of creation: managing distributed innovation in turbulent markets, California Management Review, Vol. 42 No.4, pp.45-69

Seaden, G., and Manseau, A. 2001, Public policy and construction innovation, Building research and information, Vol. 29 No.3, pp. 182-196

Snelson, P.A., and Hart, S. J. 1991, Product Policy: Perspectives on Success, in Baker, M. J. (Ed.), Perspectives on Marketing Management, Vol. 1, pp.193-225.

Sun, H., and Wing, W. 2005, Critical success factors for new product development in the Hong Kong toy industry, Technovation, Vol.25 No.3, pp.293-303

Takeuchi, H., and Nonaka, I. 1986, The New Product Development Game, Harvard Business Review, Vol. 64 No.1, pp. 137-46 Theije, S. M., Sander, P.C., and Brombacher, A. C. 1998, Reliability tests to control design quality: a case study, International

Journal of Quality & Reliability Management, Vol. 15 No. 6, pp. 599-618 Bibliographical notes: Dr. Udayangani Kulatunga: Udayangani is a lecturer in Quantity Surveying at the School of the Built Environment, University of Salford. She has over seven years experience in teaching and research in Sri Lanka and in the UK. She is involved in both at undergraduate and post-graduate teaching. Udayangani completed her PhD in the area of performance measurement in construction research and development. Her research interests are performance measurement, disaster risk reduction, construction waste management, and construction procurement. Her research output is demonstrated by the number of publications done in both journals and international conferences. Prof. Dilanthi Amaratunga: Dilanthi is the Professor of disaster management and the Director of the Research Centre for Disaster Resilience at the University of Salford, UK. Her research interests include performance management, disaster management, facilities management and knowledge transfer from research to teaching, education and training, and women in construction. She is the Co-Editor of International Journal of Disaster Resilience in the Built Environment and has extensive experience in leading international research collaborations. She has presented widely at International conferences, has led international workshops and seminars and working actively with the UN.


106

Dr. Richard Haigh: Richard is a Senior Lecturer at the School of the Built Environment, University of Salford, UK and Programme Director for the School’s MSc Disaster Mitigation and Reconstruction programme. He is Co-Editor of the International Journal of Disaster Resilience in the Built Environment and his research interest includes: adaptive capacity development; community based organisations and livelihood development; corporate social responsibility; stakeholder management; and, the conflict and built environment interface. Received August 2010 Accepted November 2010 Final acceptance in revised form December 2010

MultiCraft


Vol. 2, No. 9, 2010, pp. 107-124



www.ijest-ng.com


A stochastic simulation approach for production scheduling and investment planning in the tile industry

G. Davoli1*, S. A. Gallo1, M. W. Collins2, R. Melloni1.

1Department of Mechanical and Civil Engineering (DIMeC),University of Modena and Reggio Emilia, ITALY

2School of Engineering and Design, Brunel University, West London, UK *Corresponding Author: Giovanni Davoli e-mail: [email protected], Tel +39-059-2056113, Fax.+39-059-2056126

Abstract The present paper aims to develop a simulation tool for tile manufacturing companies. The paper shows how simulation approach can be useful to support management decisions related to production scheduling and investment planning. Particularly the aim is to demonstrate the importance of an information system in tile firms. The Factory Data Model (FDM) parameter is used to describe the activities in ceramic tile industries operating in different European countries. A process- based analysis of tile manufacturers is undertaken and individual company performance is quantified by Key Performance Indicators (KPI). The overall model is composed of different processes, which are coded into Scilab environment and matched together to arrange a stochastic simulator. The simulations results are used to show how management decisions can significantly effect the KPIs. The simulations highlight the effects on KPIs of three specific parameters: the length of scheduling period, the quantity of stock needed and the reliability of the information system supporting orders. The results clearly show that the effect of allowing the presence of unattended orders within the outstanding orders list always has a remarkable negative influence on KPIs. Results also suggest that the presence of sub-groups of homogeneous tiles, based on colour variation, is one of the most important factors affecting a tile manufacturer’s performance. The results of the simulations have two different practical implications. Firstly, they demonstrate the importance of information systems in tile companies, suggest to evaluate investment in information technology and indicate the value of promoting an information culture in the entire work forces. Secondly, they show the potential of simulation tools development to support decision making in a BPR (Business Processes Re-engineering) scenario. Keywords: Simulation, tiles, information system, investments, management, BPR. 1. Introduction The economic scenario today is highly competitive in terms of number of competitors and costs. Globalization as the result of the rapid development of information and communication technologies (fast access to accurate and reliable data), transport system and consideration of common standards (which provide the world-wide comparability of the products) (Westkamper, 1997) allows the fusion of local and nation markets into a global one (Kalpic, 2002). Due to globalization, competition has intensified from a national scale to a global arena (Jin-Hai, 2003). To remain competitive, companies have to reach an high-level of performance by maintaining high quality, low cost, low manufacturing lead times, and high customer satisfaction (Al-Aomar, 2000). Unpredictable and fast changes in the internal and external environment, experienced by enterprises as turbulence (Warnecke, 1993), make very important to be able to analyze and test a manufacturing system before any large investment or any business process reengineering (BPR) activity. Moreover, shorter product life-cycles also constrains the time available for developing new manufacturing system, or for old manufacturing system reconfiguring (Klingstam, 1999). Because of its great versatility, flexibility, and power, simulation is one of the most widely used operations research techniques from the early eighties (Shannon, 1980). Simulation models are proved to be useful to support and drive company management in improving the performances of the production and logistic systems. As reported by O’Kane, many researchers argue that simulation is one of the major tools to assist in the re-engineering process and improve the businesses effectiveness and performance (O’Kane, 2007), in fact, according to Mansar,

Davoli et al. / International Journal of Engineering, Science and Technology, Vol. 2, No. 9, 2010, pp. 107-124

108

simulation could be properly used to test how appropriate it was to apply BPR best practices (Mansar, 2005). At the same time, O’Kane also reported that many authors argue about the high potential of simulation to make a significant contribution to the continuous improvement of the quality management systems themselves (O’Kane, 2007). However several studies show that there is a low usage of simulation by industries (Ryan, 2006), especially simulation has not been widely applied to SMEs (Small Medium Enterprises) (O’Kane, 2007). Usually, to achieve the expected results in term of performances improvement, a detailed model of the production and logistic system is needed. So the simulation modelling become an heavy programming task and carry on the development of such a simulation model is a very expensive activity, in term of resources and time used. Moreover the developed simulation model for a specific manufacture system is not easily adaptable for another one. This fact contributes to increase model development costs and prevent the use of simulation in tile manufacture. 1.1 Tile industries

The tile industries in Europe are concentrated in two countries: there is a Spanish cluster in the area of “Castellon” and an Italian one in the “Emilian” region (Harvas-Oliver, 2007). Originally the tile industries were characterized by large scale production of a limited range of products. Over the last few decades consumer demand for tiles has changed, becoming more sophisticated. Nowadays the Ceramic Tile sector is very competitive. This competition is ultimately reflected in an increase in the variety of products and services, together with a decrease in production costs (Giret, 2009). The fact that tile industries produce a considerable variety of products with shorter life cycles is pointed out by many authors (Harvàs-Oliver, 2007) (Andrès, 2005) (Regueiro, 2000) (Bonavia, 2006) (Vallada, 2005). These characteristics are much more significant for the Italian (Emilian) cluster of companies because they are focused on higher end more sophisticated markets and give more attention to product differentiation (Harvàs-Oliver, 2007). The increase of products number determines scheduling problems because shortening lead times and decreasing batch sizes are not very compatible with the current tile production systems. Efficient and flexible production will be needed in future successful ceramics companies (ECORYS, 2008). An exhaustive description of the production system in the tile industries was given by Andrés in 2005. These industries are dedicated to the production of ceramic products, which are used in the building sector to isolate floors and walls. The raw material used is clay, previously ground in a special mill. The layout for the production system can be schematized as a three stage hybrid flowshop with a certain number of facilities at the first stage (press and glazing line), an intermediate buffer, a different number of facilities at the second stage (kiln), another buffer and a certain number of facilities at the final stage (sorting and packing cells). The general description provided by Andres is confirmed even by the study of Ortiz on a specific tile enterprise (Ortiz, 1999). An exhaustive and detailed description of the best available technology for the tile industry is provided in the last report about the ceramic manufacturing industry by the European Commission (EC, 2007).

The tile industry production system suffers from problems of: finite products characteristics instability, information reliability along the supply-chain and high finite products inventory level. In fact, addressing the phenomenon of finite products characteristics instability, undesired colouring is one of the most important needs in the ceramic sector (Erginel, 2004). Each piece has to be inspected and classified in most companies and individual (product) models are usually stored in sub-groups based on the tone (colour degree) and the calibre (thickness) (Tortajada, 2006). The problems in information flows from/to the customers are related with the distribution channels structure that in many cases use agents and brokers. The data provided by the agents, into the information system, are often unreliable. The reasons of this unreliability are structural. Each agent has a specific market and addresses specific customers. The linkage between the agent and his customers is often stronger than his linkage with the manufacture. In order to fulfil customers needs in short time, agents usually put “fake” orders into the information system trying to anticipate customers requests and shortening lead time. When the agents are not able to change a “fake” order in a real one they simply delete it from the information system, just few days before the delivery date. Usually tile industries information systems are not developed to perform controls in order to measure and/or prevent this kind of behaviour. The importance of information systems in a tile manufacture was pointed out by Chalmeta in his work concerning the integration of IRIS Tile Group (Chalmeta, 2001). The high inventory level for finite products is underline as a persistent problem by Assopiastrelle analysis (Assopiastrelle, 2005). The problem is mainly related to finite products characteristics instability and information unreliability. 2. Aim and methodology The present work presents a simulation approach for tile manufactures. Tile manufacture sector is composed mainly of SMEs and it therefore follows that they have only limited resources to devote to simulation (Bonavia, 2006). The proposed approach requires low resources for the development of a generic tile manufacture model by use of VirtES (Virtual Enterprise Simulator) methodology (Davoli, 2009) based on the open – source platform, Scilab. The VirtES methodology prevents from developing a very detailed model for the whole enterprise and allows to develop and/or integrate detailed models for specific sub – systems and/or processes. In this paper the generic tile enterprise model is presented, than the model is detailed to investigate the impact of information unreliability on industry performances.


109

3. Model assumptions

Generally there is a lack of scientific understanding of tile industrial processes, especially in the Italian cluster (Meyer-Stamer, 2001). The operational research applied on tile manufacture is poor, for example in a recent literature review about scheduling problems, Allahverdi reports only the works of Andres focused on tile manufacture (Allahverdi, 2008). Even the scientific knowledge of processes management are often neglected in tile industries. Only Ortiz reports about the re-engineering, based on CIMOSA architecture, of production planning processes in a tile industry (Ortiz, 1999). So the tile enterprise model is developed using multiple case-study analysis, based on Italian tile manufactures, and it is supported even by the study of Ortiz on a specific tile enterprise (Ortiz, 1999). The level of details in VirtES model is chosen in order to develop a generic tile enterprise model applicable in all case-study.

The model presented is developed mainly to evaluate the impact of information unreliability on a tile manufacture system. The main model assumptions consist of: orders and production capacity are balanced and production process without set-up times is assumed deterministic. Moreover the customers orders are simulated with a random function set, in according with the statistical data, and the production schedule is write up according with strategy adopted in the majority of the considered manufactures. The production process is deterministic but the presence of sub-groups, based on colour variation, is considered in end products.

There is a lack of references, in the recent literature, about the degree of tone variation. However, the range for tone variation can be extrapolated from the sorting machine lines as reported in a technical paper for the industry (SACMI, 2007). The impact of set-up times and scheduling strategy are not considered in the model. To develop a VirtES simulation model at first stage a factory data (FDM) (Yu, 2000) of a tile industry is provided. The FDM model allows the flexibility of the model and its adoption even if only partially completed. The tile enterprise model herein developed, see Figure 1, according with FDM paradigm, is composed by resources, tokens and processes.

Figure 1. The tile enterprise FDM model.


110

4. Virtual enterprise In order to develop a virtual enterprise it is necessary to do an input – output chart for each process, see Figure 2, and to provide a complete processes interaction chart, see Figure 3. 4.1 List of acronyms BPR: Business Processes Re-engineering DAQ: average quantity of products sold in a day DOE: Design Of Experiments FDM: Factory Data Model INI: initializing function KPI: Key Performance Indicator LDS: imposed stored tiles in days of production LDSS: amount of goods needed to achieve the imposed LDS M: spread between the average sale price and the average production cost for 1 m2 of tiles MAG: total amount of stored tile, only 1st quality goods MD: available stock list MF: physical warehouse N: daily orders number NPDP: master production scheduled length P1: stock-outstanding orders matching process P2: production planning process P3: production process P4: quality control process PC: production capacity PDP: master production scheduled PO: outstanding orders list REC: KPI recording function S1: collecting orders function S2: executing orders function S3: canceling order function SME: Small Medium Enterprise SPE: executed orders report TA: rate of unreliable orders generated each day VirtES: Virtual Enterprise Simulator W: production report

The “collecting orders process” creates orders using a random function. Orders can be created of two different types. Orders of both available products and unavailable products at generation time. Every order has been generated with a set of characteristics: product, quality, quantity, day of creation and day of delivery. The sum of the average quantities sold for each product should be equal to the production capacity, so balancing demand and production capacity. The “stock - outstanding orders matching process” matches orders with available goods. The matching is based on a set of characteristics: product, quality, tone, available quantity. The “executing order process” executes orders that were matched with available goods according with the day of delivery. The “production planning process” schedules products to produce The aim of the adopted scheduling strategy is to keep a certain quantity of goods in the warehouse for each product. The amount is determined by the formula: LDS x DAQ, where: LDS is a fixed number of days equal for all products and DAQ is the average quantity of products sold in a day, given within the product characteristics set. So for every product it is possible to have the amount of goods needed to achieve the imposed LDS, this quantity being termed (LDSS). The PDP is made for a certain number of days (NPDP) so an algorithm provide to allocate the production capacity proportionally to LDSS for each product. The “production process” executes the PDP. A deterministic process is implemented. This means that the process producing each product is included within the PDP according to the production characteristics of the products. For each product is given a set of production characteristics is given composed of: percentage of 1st, 2nd and 3rd quality, number of tones for each batch of production, quantity of product for each tone. No set up times are implemented. Minimum batch size is implemented. The “quality control process” checks the goods produced and updates the stock. It is assumed that all goods produced goods are stored in the warehouse.


111

Process S1

Process S2

Process P1

Process P2

Process P3

Process P4

Figure 2. Singles processes input – output charts


112

Figure 3. Processes interaction chart

The processes described are codified to allow implementation into a Scilab environment. A list of virtual products has been done in order to provide all the necessary information for processes. In view of the discussion above about colour variation and sub-groups of tiles, the products in the list are characterized by 3 or 4 tones for each production batch and the distribution of relative tones, see Table 1.

Table 1. Virtual products list

PRODUCTS ID

QUALITY (1ST , 2ND ; 3RD)

N° TONES FOR BATCH

TONES DISTRIBUTION (ONLY FOR 1ST QUALITY)

DAILY AVERAGE QUANTITY SOLD

(m2)

1001 0.9 0.05 0.05 3 0.25 0.50 0.25 0.00 1000

1002 0.8 0.10 0.10 3 0.33 0.33 0.34 0.00 1000

1004 0.85 0.10 0.05 4 0.25 0.25 0.25 0.25 500

1005 0.9 0.05 0.05 4 0.25 0.25 0.25 0.25 500

… … … .. … … … … … …..

100(n-1) 0.9 0.05 0.05 3 0.34 0.33 0.33 0.00 250

100(n) 0.8 0.10 0.10 3 0.34 0.33 0.33 0.00 250 4.2 Main simulation sequence

The defined processes are listed in a sequence to simulate the daily enterprise work. To initialize a simulation a special process, called “INI” runs at the beginning of the first day and provides the initial amount of tiles in the warehouse and a random production plan for the first period. The special process “REC” records the KPIs at the end of each simulated day. The sequence, see Table 2, represents the general scheme followed by the simulator.


113

When order n arrives from market, function S1 generates it and places it in the outstanding orders list (PO). The process P1 checks if the requested tiles are in the warehouse (by checking MD report) and process P2 (production planning) verifies if are satisfied the conditions to place each product in the PDP. The products in the PDP are produced by the process P3 (production lines) that simulates the production of the tiles, the produced tiles go to the process P4 (quality control). The tiles pass the quality control tests and they are stored to the warehouse MF . The function S2 (executing orders function) searches for the satisfied orders and, according with the date of delivery, sends the orders to the market and store a report in SPE.

Table 2. Sequence of main simulation

PROCESS ID PROCESS NAME CONDITION

INI Initializing sequence Only the first day

S1 Collecting orders process Each day

P1 Stock – outstanding orders matching update

Each day

P2 Production planning Only the established days according with NPDP

P3 Production lines Each day

P4 Quality control Each day

S2 Executing orders process Each day

REC KPI recording Each day

New process Modified process

Process S3

Process S1

Figure 4. Modified and new-process for the add-on. 4.3 Add-on to perform simulation focusing on information unreliability In order to simulate the effect of the unreliability of information, a specific add-on is implemented. The add-on is made of a modified S.1 process and a new process called S.3 (see Figure 4) and a new parameter termed “TA”. The S.1 process is modified to create a certain number of unreliable orders. The TA parameter indicates the rate of unreliable orders generated each day. The S.1 process marks this excess of orders as “fake”. The S.3 process inspects all the orders outstanding and deletes the “unreliable” ones which have a delivery date less than 3 days from the current day of simulation. In this way unreliable orders enter the system for a certain period and affect scheduling process before expiry. 5. KPI definition In order to judge the effectiveness of different factors on the enterprise performances key performance indicators (KPIs) are needed. All the KPIs are expressed in square meters of tile and are referred only to 1st quality products. The three KPIs chosen are referred to the amount of sold goods (SPE), the amount of stocked goods (MAG) and the amount of not fulfilled orders, back-orders (BO), as shown in Figure 5. The raw data provided from the simulation model are specific for the used features imposed in the model. The KPIs are normalized on production capacity (PC).

SPEi is the total amount of sold goods at day-i and it is the cumulative raw data provided by the model. incSPEi is the amount of


114

sold good in day-i and incSPE is the daily average increment of sold goods in a defined period, from day-i to day-j, normalized on production capacity. MAG is the total amount of stocked goods at day-i and it is the raw data provided by the model. incMAGi is the variation in stocked good in day-i and incMAG is the daily average variation of stocked goods in a certain period, from day-i to day-j, normalized on production capacity. MAG0 is the amount of socked goods at day-i, normalized on production capacity, i.e. the normalized amount of stocked tile at the first day of the period chosen to calculate incMAG. BOi is the total amount of back-orders at day-i and it is the cumulative raw data provided by the model. incBOi is the variation of back-orders in day-i and incBO is the daily average increment of back-orders in a certain period, from day-i to day-j, normalized on production capacity. Normalized KPIs have been used to develop the profit function model and the cumulative values have been used to show an example in the findings section. Raw data, day data and KPIs are shown in Table 3.

Figure 5. Enterprise environment interactions and KPIs

Table 3. Raw data, Day data and KPIs table

TYPE NAME FORMULA

Raw data SPE -

Raw data MAG -

Raw data BO -

Day data incSPEi

PCSPESPE

incSPE iii

1−−=

Day data incMAGi

PCMAGMAG

incMAG iii

1−−=

Day data incBOi

PCBOBO

incBO iii

1−−=

KPI incSPE

( )ij

incSPEincSPE

t

j

it

−=∑=


115

Table 3. (cont’d). Raw data, Day data and KPIs table

TYPE NAME FORMULA

KPI incMAG

( )ij

incMAGincMAG

t

j

it

−=∑=

KPI incBO

( )ij

incBOincBO

t

j

it

−=∑=

KPI MAG0

PCMAGMAG i=0

6. Purpose of Experiment The purpose of the experiment is to quantify the impact of unreliable orders included in the information system. Two main impacts are considered: enterprise earnings and capacity to fulfil customers orders. The degree of unreliability is represented by TA. A comparison between rate of TA and other parameters is needed to evaluate the relative importance of the TA rate itself. In our current study all simulation parameters are fixed to representative values for a typical, or ‘average’ company, in accordance with the experience of, and information supplied by, Assopiastrelle. Only TA and LDS are studied here, and within a predetermined range, for three different value of NPDP. LDS is the desired amount of stored tiles in days of production. This parameter is the one that operation managers address for first to improve the service level for final customers. The experimented LDS direct correlation with service level and stock costs imposed a difficult trade-of. It is impossible for managers to improve service level and to reduce costs at the same time, acting on LDS. The aim is to determinate the relative importance of TA and LDS for different value of NPDP. NPDP is the length of the master production schedule, it depends on many technical and commercial factors and, usually, it is decided by top managements. A variation of NPD can not be done at operational management level but, of course, it could be suggested. In fact a slight variation in LDS and NPDP can be achieved in practice without either financial investment or process redesign. The investigated range for TA is from 0 to 20% of “fake” orders acting in the information system, it is estimated from some analysis in specific enterprise. The investigated range for LDS is from 45 to 75 days of production equivalent represents the common range of the parameter approximately around the mean of 65.25 days for stocked tiles reported by Bonavia (2006). The considered NPDP lengths are taken in accordance with information supplied by Assopiastrelle. The full setting of parameters is provided in Table 4. 6.1 Computer DOE

To make a warm-up and an error analysis two groups of replication are used. Three runs, named group A, are performed using the main simulation sequence, for which TA = 0%. A second group of three runs, named group B, is performed using the add-on sequence with a TA of 20%. Within group B one order out of five was assumed to be unreliable, and the two group comparison is used to confirm the validity of the model itself. Using the DOE application “Design-Expert 6 ©”, the experiment is performed to investigate the effect of changes in LDS at different value of NPDP. The ranges selected for LDS and NPDP were 45 to 75 days and 10 to 30 days respectively. The DOE application indicates how the simulations should be set and Table 5 shows the entire sets of parameters for the series of runs. Every run is marked by a 3-record label: for instance 20.60.000 signifies a run for which LDS = 20 days NPDP = 60 days and TA = 0%.


116

Table 4. Setting of processes parameters

ID PROCESS NAME PARAMETERS SET

P.S1 Collecting orders process

o Daily orders number (N) o Random function for Products generation o Random function for Quality generation o Random function for Quantity generation o Random function for Day of delivery

generation o TA rate, unreliability of information system

o N = (PC)/(Average order amount)

o Simple random function o Only 1 quality o Simple random function from

50 to 900 m2 o Simple random function from 5

to 45 days o From 00% to 20%

P.P1 Stock – outstanding orders matching update

o Matching rules o Matching for product, quality and a single tone is needed for each order

P.S2 Execution of orders

o Order characteristics to be executed o Already matched and current day equal or superior to delivery day

P.P2 Production planning

Implemented scheduling strategy parameters are:

o Production capacity

o LDS

o NPDP

o 20.000 m2 every day

o from 45 to 75 days

o from 10 to 30 days

P.P3 Production lines o Minimum batch size o 5.000 m2

Table 5. Computer DOE

Group Simulation label

NPDP (days)

LDS (days)

TA (%)

A 1x20.60.000 20 60 000 A 2x20.60.000 20 60 000 A 3x20.60.000 20 60 000 B 1x20.60.020 20 60 020 B 2x20.60.020 20 60 020 B 3x20.60.020 20 60 020 10.45.000 10 45 000 30.45.000 30 45 000 10.75.000 10 75 000 30.75.000 30 75 000 10.45.010 10 45 010 30.45.010 30 45 010 10.75.010 10 75 010 30.75.010 30 75 010 10.45.020 10 45 020 30.45.020 30 45 020 10.75.020 10 75 020 30.75.020 30 75 020


117

7. Findings Graphical outputs related to raw data and day data are presented for run 1x20.60.00 of group A in Figures 6 and 7. The raw data are the output of the simulation experiment and the values are specific of the used parameters. The above mentioned data show that the model present a warm-up period necessary to reach the steady state. In the day data the simulation outputs are elaborated in order to provide the daily increment normalized to the production capacity, according with Table 3. As shown in Figure 7, the presence of a warm-up is still evident. Moreover the graphic outputs show a stochastic fluctuating motion of values among a central mean. The above mentioned result was expected because the single day observation period is not representative of the enterprise performances. The chosen KPIs are presented in Figure 8, where the fluctuating motion is smoothed and the presence of a warm-up period is still evident.

Figure 6. Raw data

Figure 7. Day data

Figure 8. KPI

8. Warm-up analysis Data collection and model validation have been done according to simulation modeling theory and practices as described by Chung (Chung, 2004). The simulation length has been defined according to the result of standard deviation (STD) analysis referring to all KPIs, except for MAG0, as shown in Figure 9 for group A. Three replications have been used to perform the error analysis, over a number of four replications no significant differences were observed. A length of 250 working days has been taken for warm up phase in order to have error stability. The maximum related error, observed in KPIs, is below the standard deviation level of 0.01 (1%). A length of 200 working days has been taken for simulation run time, according with formulas in Table 3 for

MAG - Raw Data

1 500 000

1 550 000

1 600 000

1 650 000

1 700 000

1 750 000

1 800 000

1 850 000

1 900 000

1 950 000

2 000 000

0 50 100 150 200 250 300 350 400 450days

m^2

BO - Raw Data

0

100 000

200 000

300 000

400 000

500 000

600 000

700 000

800 000

900 000

1 000 000

0 50 100 150 200 250 300 350 400 450days

m^2

SPE - Raw data

0

1 000 000

2 000 000

3 000 000

4 000 000

5 000 000

6 000 000

7 000 000

8 000 000

9 000 000

0 50 100 150 200 250 300 350 400 450days

m^2

incSPEi - Day data

-1.00

-0.50

0.00

0.50

1.00

1.50

0 50 100

150

200

250

300

350

400

450

days

incMAGi - Day data

-1.00

-0.50

0.00

0.50

1.00

1.50

0 50 100

150

200

250

300

350

400

450

days

incBOi - Day data

-1.00

-0.50

0.00

0.50

1.00

1.50

0 50 100

150

200

250

300

350

400

450

days

incSPE (1-i)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0 50 100

150

200

250

300

350

400

450

days

incMAG (1-i)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0 50 100

150

200

250

300

350

400

450

days

incBO (1-i)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0 50 100

150

200

250

300

350

400

450

days


118

KPIs calculation i is set to 250 and j to 450. As shown in Figure 10 for run 1x20.60.00 of group A, during the observation period the KPIs values are stable within the related errors. MAG0 value depends on warm-up length, so the error analysis is done only for day-250. MAG0 standard deviation for group A is about 0,7 and for group B it is about 1,2. All the simulation results, in term of KPIs, are shown in Table 6.

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

0 50 100 150 200 250 300 350 400 450

days

st.d

.

incSPE incMAG incBO

Figure 9. Error analysis upon testing area utilization rate.

Figure 10. Observed KPIs after warm-up period.

Table 6. All simulation results

Group Simulation label NPDP (days)

LDS (days)

TA (%)

KPI1 incSPE

KPI2 incMAG

KPI3 incBO

KPI3 MAG0

A 1x20.60.000 20 60 00 0.89 0.04 0.02 85,27A 2x20.60.000 20 60 00 0.89 0.05 0.02 86,42A 3x20.60.000 20 60 00 0.88 0.05 0.03 85,03B 1x20.60.020 20 60 20 0.83 0.10 0.06 95,16B 2x20.60.020 20 60 20 0.82 0.11 0.06 95,46B 3x20.60.020 20 60 20 0.84 0.09 0.06 93,20 10.45.000 10 45 00 0.82 0.11 0.08 79,69 10.75.000 10 75 00 0.84 0.08 0.07 103,03 30.45.000 30 45 00 0.91 0.04 0.00 74,14 30.75.000 30 75 00 0.93 0.01 0.00 92,30 10.45.000 10 45 10 0.80 0.13 0.09 87,58 10.75.000 10 75 10 0.82 0.11 0.09 110,34 30.45.000 30 45 10 0.88 0.06 0.02 78,83

incSPE (250-i)

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

250

300

350

400

450

days

incMAG (250-i)

-0.200

0.000

0.200

0.400

0.600

0.800

1.000

250

300

350

400

450

days

incBO (250-i)

-0.200

0.000

0.200

0.400

0.600

0.800

1.000

250

300

350

400

450

days


119

Table 6. (cont’d). All simulation results

Group Simulation label NPDP (days)

LDS (days)

TA (%)

KPI1 incSPE

KPI2 incMAG

KPI3 incBO

KPI3 MAG0

30.75.000 30 75 10 0.89 0.05 0.03 95,32 10.45.020 10 45 20 0.78 0.14 0.10 96,42 10.75.020 10 75 20 0.80 0.13 0.10 116,30 30.45.020 30 45 20 0.85 0.09 0.04 80,52 30.75.020 30 75 20 0.85 0.08 0.04 100,74

9. Interpretation of results A linear model function (1) is used to describe the correlation of KPIs with the studied parameters: LDS and TA at three different level of NPDP. The response analysis is performed with Design-Expert 6 © and the coefficients for different KPIs are shown in Table 7. The ANOVA test for KPIs shows that the model studied parameters are significant (P<0.05) at any level of NPDP, except for LDS parameter for incBO. The factorial ANOVA is completed at the 95% confidence level. The F-test is used to evaluate the significance of the experimental factor effects. ANOVA test results are shown in Tables from 8 to 11.

εξξξξ +×+×+×+= TALDSNPDPY 3210 (1)

Table 7. Calculated coefficients for model function

Y ζ 0 ζ 1 ζ 2 ζ 3 St.d. Symbol

incSPE 0.786 0.00353 0.000417 -0.00300 0.01 β

incMAG 0.145 -0.00283 -0.000583 0.00271 0.01 α

incBO 0.0895 -0.00307 0.000056 0.00182 0.01 δ

MAG0 59.6 -0.539 0.641 0,459 1.20 γ

Table 8. ANOVA results for incSPE

Source DF Seq SS Adj SS Adj MS F P TA 1 0,010864 0,010864 0,010864 157,97 0,000 LDS 1 0,000675 0,000675 0,000675 9,81 0,008 NPDP 2 0,017344 0,017344 0,008672 126,10 0,000 Error 13 0,000894 0,000894 0,000069 Total 17 0,029778

Table 9. ANOVA results for incMAG



120

Table 10. ANOVA results for incBO


Table 11. ANOVA results for MAG0


The ANOVA analysis shows that the influence of TA on KPIs is greater than the that one of the LDS at any level of NPDP, except for MAG0. In the case of incSPE the most important factors are TA and NPDP. It can be argued that the importance to scheduling of a long period points to a larger batch size in tile production involving and this results in a small number of sub-groups based on a classification in terms of tone. It is more important to have less sub-groups than to have a more reactive production system. The same consideration holds about the influence of NPDP upon incMAG and MAG0. Regarding incSPE, ANOVA analysis indicates that LDS is the less important parameter. This outcome results from the influence of the choices made in the very implementation of the model itself. This is due to the balance between production and the market request for tiles being a defined constituent of the model. A flat production capacity, which is a practical characteristic for a tile company, has in the model an implication of a matching flat market request. This may underestimate the importance of LDS upon incSPE. The same consideration can be done for LDS factor, that is indicated as non significant for incBO. 10. Cost/benefit analysis In this section an example is developed where the results achieved with VirtES are implemented to analyze a specific manufacture. To enable a full cost/benefit analysis to be carried out, a simple function is proposed, termed the “Earning function” (2). In the proposed function incBO is not considered because it is not possible to propose any cost to this KPI. incBO can be considered apart as a measure of service level for customers. Of course the customers satisfaction affects industry earnings but it is not possible to propose a unique equation at this stage. Maximizing the proposed function means that the performance of the company, as defined in the model, is optimised in terms of enterprise profit.

PCMAGTincMAGCTincSPEMMAGincMAGincSPEfE m ×⎭⎬⎫

⎩⎨⎧

⎥⎦⎤

⎢⎣⎡ +××−××== 00 2

),,( (2)

Where:

• M is the spread between the average sale price and the average production cost for 1 m2 of tiles; • Cm is the average warehousing cost for 1 m2 of tiles; • T is the considered time period in days; • PC is the production capacity in square meters for day;

As presented above it is possible to have a linear function that correlated the KPIs with the parameters studied:

- TALDSNPDPincSPE ×+×+×+= 3210 ββββ (3)

- TALDSNPDPincMAG ×+×+×+= 3210 αααα (4)

- TALDSNPDPMAG ×+×+×+= 32100 γγγγ (5) After the substitution the “Earning function” can be written as shown in equation (6)


121

PCTCMTTATCMTLDSTCMTNPDPDTCMTE mmmm⎭⎬⎫

⎩⎨⎧

⎥⎦

⎤⎢⎣

⎡⎟⎠⎞

⎜⎝⎛ +−+⎥

⎦

⎤⎢⎣

⎡⎟⎠⎞

⎜⎝⎛ +−+⎥

⎦

⎤⎢⎣

⎡⎟⎠⎞

⎜⎝⎛ +−+⎥

⎦

⎤⎢⎣

⎡⎟⎠⎞

⎜⎝⎛ +−= 333222111000 2222

γαβγαβγαβγαβ (6)

By the way the equation allows some considerations to be made about the management of a tile firm. In equation (2) he first

term including incSPE is the most important one, due to the weight of the coefficients. A more competitive market scenario reduces the spread M (Assopiastrelle, 2000) and contributes to an increase in warehousing cost due to increased storage of products and a more rapid obsolescence of the products themselves. This tendency highlights the importance of taking the other factors into account that in a previously strong growing market were neglected by management. Some consideration ca be made from equation (6). A longer NPDP always has a positive impact on the function because of the production of a smaller number of larger homogeneous sub-groups of tiles. The LDS term suggests that the facility of a larger warehouse correlates directly with the ratio between M spread and Cm cost. The TA term indicates that an unreliable information system always negatively affects a firm’s performance and the importance of this factor is comparable with those of the other parameters. A numerical example of the “Earning Function” is given below with this set of parameters:

• M = 0,222 €/m2, (Assopiastrelle, 2005); • Cm = 0.01*Cp, where Cp is the total production cost with Cp = 8,163 €/m2 (Assopiastrelle, 2005). In absence of any

literature or known studies about warehouse costs in tile industries, it is assumed that the warehouse variable cost is represented by 1% of the warehoused goods production cost. In this way the warehouse cost includes only the capital cost, this approximation underestimate the importance of warehouse cost;

• T = 200 days, the number of working days for 1 year; • PC = 20.000 m2, the production capacity for an average tile manufacture.

See Figure 11 to compare the influence of TA and LDS at three different NPDP values, pay attention that for graphical, reason

the plotted parameter is (100-TA) and not TA. In Figure 12 is given the numerical example for incBO that confirms the importance of TA terms to minimize incBO improving the service level for customers.

Figure 11. Example of response surfaces for the “Earning function”


122

Figure 12. Example of response surfaces for incBO.

11. Conclusions In this work we have described the development of a simulation model of a tile company which can be successfully used to investigate the influence of many parameters on its performances. The FDM model is implemented to create a virtual enterprise. The specific advantage of the FDM model is that it is possible to utilize it with only a partial modelling of the firm. This allows the creation of a model which focuses on a particular aspect without having to spend resources and time in creating a full, but demanding, enterprise model. The paper presents the application of the proposed approach to a generic tile firm. In this case the model represents the whole manufacturing sector of the tile industry. The FDM-related model is based on the common features of tile firms obtained mainly from a literature review and collaboration with Assopiastrelle. The model is split into a series of functions, each of which is implemented in Scilab. The functions are arranged into a basic sequence and a set of KPI is identified to evaluate the firm’s performance. The basic sequence of functions may be upgraded to investigate any particular aspect of company management, in this specific case the importance of the reliability of the information system. This add-on demonstrates how the final Scilab code maintains the characteristics of the FDM model while being easily extendable and upgradeable. When the set of parameters and the model specific to the application are fixed, the simulations, performed with the Scilab code, allows the empirical equation to be determined that connects the parameters studied with the KPI. This equation is useful in allowing the management to make a cost/benefit analysis. A simple “Earning function” is proposed to connect the parameters together. Finding the best set for the parameters for an individual case is possible by maximizing the “Earning function”. A cost analysis is needed to explicitly relate the cost to the parameter setting and the minimization of investment needed. In this paper the model focuses on the importance of the process, which collects and handles the information about the orders. This focus was identified because it was evident from the experience of Assopiastrelle that the information aspects of business constitute one of the areas of tile firms activities which needs improvement. The simulations performed here involve the construction of the equations which explicitly connect the dependence of KPI with 3 selected parameters. These are: NPDP (scheduling period in days), LDS (warehouse consistency in days of production capacity) and TA (rate of unreliable orders which penetrate the information system). The results indicate that the influence of TA rate is comparable with that of the LDS parameter at any NPDP studied values. For both, maximize the “Earning function” and minimize the back-orders quantity. Moreover the results suggest that one of the most important factors in tile industry performance is the presence of sub-groups of tiles due to variations in tile colouring. The proposed approach is well suited to instances of tile enterprises because the Scilab model we have developed represents a starting point from which it is possible to create a model for a specific firm. The development of such a model can be carried out with modest financial and other resources. This is because the approach is deliberately planned to start from any specific aspect of activity, and to enlarge the model as needed. The implementation of this approach forms a useful tool for the enterprise in that it can support management decision-making and investment planning.


123

References Al-Aoma, R., 2000. Product-mix analysis with discrete event simulation, Proceedings of the 2000 Winter Simulation Conference,

Orlando, FL, December 10-13, pp. 10385-92. Allahverdi, A., Ng, C.T., Cheng , T.C.E., Kovalyov, M.Y., 2008. A survey of scheduling problems with setup times or costs,

European Journal of Operational Research, Vol. 187, pp. 985 – 1032; Andrés, C., Albarracin, J.M., Torino, G., Vicens, E., Garcia-Sabater, J.P., 2005. Group tschnology in a hybrid flowshop

environment: A case study, European Journal of Operational Research, Vol. 167, pp.272-181. Assopiastrelle 2000, Costi per tipologia e ricavi medi nell’industria delle piastrelle di ceramica, anni 1998-2000, Giorgio Olivieri

& Associati. Assopiastrelle 2005. 26a Indagine Statistica Nazionale, industria italiana delle piastrelle di ceramica. Bonavia, T.,. Marin, J.A, 2006. An empirical study of lean production in the ceramic tile industry in Spain, International Journal

of Operations & Production Management, Vol.26, No.5, pp. 505-531. Boukouvalas, C., De Natale, F., De Toni, G., Kittler, J., Marik, R., Mirmehdi, M., Petrou, M., Le Roy, P., Salgari, R., Vernazza,

G., 1998. ASSIST: automatic system for surface inspection and sorting of tiles, Journal of Materials Processing Tachnology, Vol.82, pp. 179-188.

Bursi, T., Marchi, G., Nardin, G., 2006. Difendere lo sviluppo: condizioni, attori e politiche, Progetto di ricerca: il sistema ceramico di fronte alla globalizzazione: strategie di impresa e strategie di sistema, Università degli studi di Modena e Reggio Emilia, Dipartimento di Economia Aziendale

Chalmeta, R., Campos, C., Grangel, R. 2001. References architectures for enterprise integration, The Journal of Systems and Software, Vol 57, pp. 175 -191.

Chung A. C., 2004, Simulation Modeling Handbook – A Pratical Approach, Industrial and Manufacturing Engineering Series, Series Editor, CRC Press.

Crary, S.B., 2002. Design of computer experiments for metamodel generation, Analog Integreted Circuits and Signal Processing, Vol.32, pp. 7-16;

Davoli, G., Gallo S.A., Melloni, R., 2009. Analysing the ceramic sector using processes and simulation models, Ceramic World Review, N.80 pp. 116-119.

Davoli, G., 2009. Sviluppo di modelli simulativi orientati ai processi per l’analisi di sistemi produttivo logistici, Ph.D. Thesis in Modelling and Mechanical Design Methods.

ECORIS, 2008. Client: Directorate-General Enterprise & Industry. “FWC Sector Competitiveness Studies - Competitiveness of the Ceramics Sector Within the Framework Contract of Sectoral Competitiveness Studies – ENTR/06/054”, Final report, 13 October. Notice: This publication was prepared for the European Commission;

Erginel, N., Dogan, B., Ay, N., 2004. The statistical analysis of coloring problems faced in ceramic floor tile industry, Euro Ceramics VIII, PTS 1-3 Key Engineering Materials 264-268: 1693-1696, Part 1-3.

European Commission, 2007. Reference document on best available techniques in the ceramic manufacturing industry, August . Giret, A., Botti, V., 2009. Engineering holonic manufacturing systems, Computer in Industry, Article in press

doi:10.1016/j.compind.2009.02.007; Jin-Hai, L., Anderson, A., Harrison, R., 2003. The evolution of agile manufacturing, Business Process Management Journal, Vol.

9, pp. 170–189. Kalpic, B., Bernus, P., 2002. Business process modelling in industry – the powerful tool in enterprise management, Computer in

Industry, Vol. 47, pp. 299-318. Klingstam, P., Gullander, P., 1999. Overview of simulation tools for computer-aided production engineering, Computer in

Industry, Vol. 38, pp. 173-186. Hervàs-Oliver, J.L., Albors-Garrigos, J. 2007. Do clusters capabilities matter? An empirical application of the resource-based view

in clusters, Entrepreneurship & Regional Development, Vol. 19, March, pp.113-136; Mansar, S.L., Reijers, H.A., 2005. Best practices in business process redesign: validation of a redesign framework, Computer in

Industry, Vol. 56, pp. 457-471. Meyer-Stamer, J., Maggi C., Seibel S., 2001. Improving upon Nature. Creating Competitive Advantage in Ceramic Tile Clusters in

Italy, Spain, and Brazil”, INEF Report, Institute for Development and Peace Gerhard-Mercator-University of Duisburg, Report 54 / 2001;

O’Kane, J., Papadoukakis, A., Hunte D., 2007. Simulation usage in SMEs, Journal of Small Business and Enterprise Development, Vol. 14, No. 3, pp. 514-52.

Ortiz, A., Lario, F., Ros, L., Hawal, M., 1999. Building a production planning process with an approach based on CIMOSA and workflow management systems, Computer in Industry, Vol. 40, pp. 207-219.

Regueiro, M., Sanchez, E., Sanz,V., Criado, E., 2000, Ceràmica Industrial en Espana, Boletin de la Societad Espanola de Ceràmica y Vidrio, Vol.39, No.1 , pp. 5-30.

Ryan, J., Heavey, C., 2006. Process modelling for simulation, Computer in Industry, Vol. 57, pp. 437-450. Ripley, Brian D., 1987. Stochastic Simulation, ed. John Wiley & Sons, New York, N.Y.


124

SACMI Imola S.C. 2007. Technical papers: “Single-arm “Master” sorting”, http://www.sacmi.it/Products.aspx?IdAzienda=1&IdDivisione=2&IdSottoDivisione=1&IdLinea=334; Shannon, R., Long, S., Buckles, B., 1980. Operations research methodologies in industrial engineering, AIIE. Tortajada, I., Peris-Fajarnes, G., Aguilar, M., Latorre, P., 2006. Anàlisis del proceso de classification ceràmico, Boletin de la

Societad Espanola de Ceràmica y Vidrio, Vol.45, No.1 , pp. 22-27. Vallada, E., Maroto, C., Ruiz, R., Segura, B., 2005. Anàlisis de la programacion de la produccion en el sector ceràmico espanol,

Boletin de la Societad Espanola de Ceràmica y Vidrio, Vol.44, No.1 , pp. 39-45. Warnecke, H.J., 1993. The fractal company, Springer, Berlin. Westkamper, E., 1997. Versatile, integrated production with virtual elements, Proceedings of the 29th CIRP International Seminar

on Manufacturing System, Osaka, Japan, pp. 50-56. Yu, B., Harding, J.A., Popplewell, K., 2000. A reusable enterprise model, International Journal of Operations & Production

Management, Vol.20, No.1, pp. 50-69. Biographical notes Dr. Eng. G. Davoli received MS and Ph.D. from University of Modena and Reggio Emilia (Italy) in 2005 and 2009, respectively. He is a Lecturer at Department of Mechanical and Civil Engineering (DIMeC) at the University of Modena and Reggio Emilia (Italy) since April 2009. His research interests include: BPR and lean production practices, discrete event simulation, supply-chain, stocks management and logistic problems. He is a Fellow of IFIP Working Group 5.7 Associates. Dr. Eng. S.A. Gallo received MS and Ph.D. from University of Naples – Federico II (Italy) in 1993 and 1998, respectively. He is a Contract Professor at Department of Mechanical and Civil Engineering (DIMeC) at the University of Modena and Reggio Emilia (Italy) since April 2005. He had applied as lecturer at the University of Naples - Federico II 1998 to January 2005. His research interests include: projects management practices, discrete event simulation, scheduling and logistic problems. Prof. M. W. Collins received PhD and DSc at City University of London (UK) and MA at Oxford University (UK). He is a Visiting Professor at School of Engineering and Design, Brunel University, West London, UK. Prof. Eng. R. Melloni received MS and Ph.D. from University of Bologna (Italy) (Italy) in 1984 and 1991, respectively. He is a Full Professor at Department of Mechanical and Civil Engineering (DIMeC) at the University of Modena and Reggio Emilia (Italy) since February 2005. He had applied as associate professor at the University of Modena and Reggio Emilia (Italy) from Novembre 2001 to January 2005. He had applied as lecturer at the University of Parma (Italy) from April 1991 to October 2001. His research interests include: safety system management, projects management, BPR and lean production practices, discrete event simulation, scheduling, supply-chain and logistic problems. Received June 2010 Accepted November 2010 Final acceptance in revised form December 2010

International Journal of Engineering, Science and Technology (IJEST) © 2010 MultiCraft Limited. All rights reserved No part of this journal may be reproduced by any means, nor transmitted, nor translated into any language without permission of the publisher Published in Nigeria by MultiCraft Limited 14, Olajide Street, Iyana Ipaja, Lagos.

!!ijest 2(9) special issue npd whole

Documents