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NATIONAL INSTITUTE OF INDUSTRIAL ENGINEERINGMumbai-400 087 INDIA

Vol. 36, No. 1January-March, 2012

ISSN 0970-3365

IN THIS ISSUE

�� The Management Students Placed in Various Sectors

Dr. Apoorva PalkarVijaya Puranik

IPO Grading and Short Term Performance of IPOs in India

Dr. Harish Kumar Singla

How SPC Tools are helpful in Automotive Industry: A Case Study

Dr. D.R. Prajapati

Cloud Computing: An Emerging Innovative Business Model of IT Services for Sustainable Competitive Advantage

Dr. Madhavaiah

Make to Order Manufacturing in Indian Context: A Case Based Study

Rajiv K. SrivastavaSubramanian Chidambaran

UDYOG PRAGATI - The Journal for Practising ManagersVol. 36, No. 1, January-March, 2012

Udyog Pragati is published as a Quarterly Journal by the National Institute of Industrial Engineering.��

PUBLICATIONS COMMITTEEContact Address

Professor Incharge Information, Publications & Publicity CellNational Institute of Industrial Engineering (NITIE)Vihar Lake, NITIE P.O., Mumbai - 400 087, India.Tel. - +91-22-28573371 / 28035207Fax - +91-22-28573251E-Mail - [email protected] / [email protected] - http://www.nitie.edu

ABOUT NITIENITIE was established as a National Institute in 1963 by the Government of India with the assistance of United Nations Development Programme through International Labour Organisation.

NITIE offers 2 years Post-Graduate programmes in Industrial Engineering, Industrial Management, Industrial Safety and Environmental Management, Information Technology Management and a Fellowship Programme of Doctoral level recognized as equivalent to Ph.D of an Indian University. NITIE has been conducting several short-term Management Development Programmes of one week duration in various areas of Industrial Engineering and Management. NITIE can conduct eight courses concurrently. The training programmes of NITIE emphasize on learning with a purpose, and are accompanied by an abiding concern for man. Besides training, NITIE is also engaged in applied research and offers consultancy in the various facets of Industrial Engineering, Operations Research, Information Systems and Computers, Environmental Management, Marketing, Organisation Behaviour and Human Resource Management.

NITIE faculty members, drawn from various basic disciplines, have diverse experience in business, industry and government, and thus are able to bring to bear academic concepts to the practical problems. By introducing new concepts, techniques and programmes to meet the changing needs arising out of rapid technological development and socio-economic transformation, NITIE endeavours to equip the managers, administrators and specialists in government, public utilities, �� performance.

NITIE has established a Centre of Excellence in Ergonomics and Human Factors Engineering (CEEHFE) as part of Government of India’s Technology Mission-2020 through TIFAC (Technology Information Forecasting and Assessment Council) – mission REACH (Relevance and Excellence in Achieving New Heights) in Educational System. NITIE has also established an Advanced Centre of Excellence in Operations and Manufacturing Management. The centre is equipped with ERP/MRP-II, CAD/CAM, EDM, QUESTsoftwares.

NITIE publishes quarterly a professional journal, UDYOG PRAGATI. This deals with new developments in industrial engineering, industrial �� to a copy of the journal. Participants of Management Development Programmes are eligible to become members of Alumni. NITIE also publishes NITIE NEWS containing information about Institute’s activities which is circulated to industries, educational institutions and alumni.

NITIE campus is located in one of the most picturesque surroundings of �� !��"��#$�$%�� 150 participants at a time in self-contained single rooms.

NITIE is administered through a Board of Governors representing industry, government, labour and professional bodies. Shri Gautam Thapar as Chairman and Dr. Amitabha De as Director.

Vision“To be a leader in the knowledge led productivity movement”

Mission“To nourish a learning environment conducive to foster innovations in productivity and business development”

Chairman Dr. Amitabha De Director, NITIE

Editor Dr.(Ms.) Mani K. Madala

Printer & Publisher Dr. U. K. Debnath

Member Secretary Mr. R.L. Samota Registrar, NITIE

Members

Dr. (Ms.) Mani Madala

Dr. D. K. Srivastava

Dr. S. B. Hiremath

Prof. (Mrs.) R. D. Chikhalkar

Prof. (Mrs.) Seema Unnikrishnan

Dr. Milind Akrate

Dr. Suman Mukhopadhyay

1Vol. 36, No. 1, January-March, 2012

�� in Various Sectors

* Director, Sinhgad Institute of Management and Computer Application Narhe -Ambegaon, Pune. [email protected]

** Associate Prof. HR Sinhgad Institute of Management and Computer Application Narhe -Ambegaon, Pune. [email protected]

Whether individual attributes and differences will affect their selection and placement in different sectors of the industry is often an area of interest for the management schools and the industry. We are analyzing the brain dominance, achievement motivation and tolerance to change scores of �� !�� "�� #��"��$��preference and Achievement Motivation than those placed in ��$��%��

Key words: placement, achievement, change, right brain, management students.

Dr. Apoorva Palkar *Vijaya Puranik **

IntroductionVivekananda points out that the weakness of the '��; �� pursue. A sculptor has a clear idea about what he wants to shape out of the marble block; similarly, a painter knows what he is going to paint. But a teacher, he says, has no clear idea about the goal of his teaching. The aim of education is to manifest in our lives the perfection, which is the very nature of our inner self. This perfection ��=��'�� everything and every-where-existence, consciousness and bliss (satchidananda). After understanding the essential nature of this perfection, we should identify it with our ‘inner self’.

A closely related concept of ‘Self Actualization was introduced by Maslow in his theory on ‘Hierarchy of Needs’. Maslow was a Humanistic Psychologist. Humanistic psychologists believe that every person has a strong desire to realize his or her full potential, to reach a level of “self Actualization”. In Maslow’s view, self actualized people can have many peak experiences (high points in life) throughout the day while others have those experiences less frequently. In effect self actualization is the person’s motivation to transform perception of self into reality.

Self actualization should be the main goal of education, because the main reason for learning is to further one’s knowledge of the self and one’s environment. (Emmerson Philippe, 2009)Dr. Richard Boyum, with the use of a test on self actualization, tests ‘how to know yourself better’ (Boyum, 2007)

Having known the ‘self’ it is then essential to know how an individual will better use the self for self actualization of experience or practice. The process


used by the individual for moving from self realization to self actualization is called’ learning’. Learning is �� '��that occurs as a result of experience ( Robbins, Judge, Sanghi, 2008). Experiential Learning and Experiential education are two ways of achieving this learning. Learning from experience by yourself might be called “nature’s way of learning”. It is “education that occurs as a direct participation in the events of life” (Houle, 1980). Experiential education is learning through programs and activities structured by others. Principles of experiential learning are used to design experiential education programs. Emphasis is placed on the nature of participants’ subjective experiences.

To learn through experience, means to learn from the interaction between the self and the situation. �� > �� ''�� management education. Situations promoting learning, can be designed and are being designed by most business schools to facilitate the learning of aspiring management students. As Tyler (1949) pointed out, over half a century ago, learning depends on the activities of the student: Students learn according to what they do, not according to what their teacher does; they either pay attention to or they do not (Smith, M.K. 1996, 2000). Rothkopf(1976, 94) reinforced Tyler’s contention by emphasizing that: students have complete veto power over the success of instruction.

However the awareness of the self is not always facilitated by the business schools, thus the interaction, as explained above, may not be understood and facilitated, necessarily to achieve the goals of education. One of the fundamental truths in education is that the knowledge, skills, aptitudes, attitudes and values with which students leave a particular teachers classroom, �� ?�� >��>aptitudes, attitudes and values that students possess when they enter the classroom.(Anderson, 2004)

Students of Management apply their knowledge in managing organisations. In management education research therefore, “Effectiveness” is one of the most critical issues, especially in management education for adult. This is because; there are two radically different

�� different contexts to evaluate the effectiveness viz. educational organization and company. In educational setting, the effectiveness of education is often evaluated by the learners’ understanding on educational contents, such as examinations and academic research reports. On the contrary, companies evaluate the effectiveness of education by practice in companies. How learners solve managerial problems in current business by applying acquired knowledge and skills through �� organizations and companies and resulted in a critical gap between the two. In order to close the gap, educational organizations developed many alternative teaching methods such as case study and action learning.

When adult joins education for acquiring new knowledge and skills, it is essential to refer to the past experience (Knowles, 1998; Lindeman, 1926). Through �� > � �� '��experience (Lindeman, 1926). Cranton (1992) thus, suggested shifting initiatives in learning from faculties (i.e. other-directed) to learners according to learners’ maturity. Summarizing above, learners in management education are basically self-directed and object-oriented. It is thus a prerequisite for learners to interact with educational organization and companies to acquire and accumulate tacit knowledge that forms managerial competencies. Argyris (1992) suggests that learning suited to organizational work is double loop learning, where individuals take actions based on revised norms to produce appropriate consequences. Therefore it is more relevant for complex, non-programmable issues similar to those in organisations.

Pfeffer and Sutton (2000) described the situation as “knowing-doing gap” between learning and research used in institutes and practical application required in companies. Bennis and O’tool (2005) proposed �� '�� X��and creating knowledge through research as a model to be used in educational institutes. However both the approaches do not seem to help managers. Some


educational programs adopted experiential learning method (e.g. Action learning) to solve the knowing-doing gap (Mintzberg, 2004; Mintzberg and Gosling, 2006). But, one of the fundamental truths in education is that the knowledge, skills, aptitudes, attitudes and values with which students leave a particular teachers ��> �� ?�� knowledge, skills, aptitudes, attitudes and values that students possess when they enter the classroom.(Anderson, 2004).

�� '�� '�� out how individual factors will affect the suitability of the management student for the industry. The factors being tested are ‘Brain dominance’, ‘Achievement Motivation’ and ‘Tolerance to Change’.

Brain Dominance has been studied for a long time. Sperry(1996) found that the left half of the brain tends to function by processing information in an analytical, rational, logical, sequential way. The right half of the brain tends to function by recognizing relationships, integrating and synthesizing information, and arriving at intuitive insights. Ned Herrman’s (2007) Whole Brain Model combines Roger Sperry’s left/right brain theory and Paul MacLean’s triune model (rational brain, intermediate brain and primitive brain) to produce a quadrant model of the brain. Left Cerebral: Theorists, Left Limbic: Organizers, Right Limbic: Humanitarians, Right Cerebral:Innovators. Brain dominance data also indicate that people with similar occupations tend to �� '�� Z�� is a strong and direct correlation between a person’s '��'�� '��'��>�� '��to consider the uniqueness of the learning group when designing educational programs for the group.(Power, Kummerow, Lundsten, Lorman, 1999)

�[�� \]^_]` �� achievement motivation. David McClelland pioneered workplace motivational thinking, developing achievement-based motivational theory and models, and promoted improvements in employee assessment methods, advocating competency-based assessments and tests, arguing them to be better than traditional

IQ and personality-based tests. The n-ach person is ‘achievement motivated’ and therefore seeks achievement, attainment of realistic but challenging goals, and advancement in the job. There is a strong need for feedback as to achievement and progress, and a need for a sense of accomplishment. McClelland argues that n-ach people with strong ‘achievement motivation’ make the best leaders, although there can be a tendency to demand too much of their staff in the belief that they are all similarly and highly achievement-focused and results driven, which of course most people are not. What affects Achievement Motivation and how does it affect the work of an individual? Several studies have found that parents play a key role in shaping students’ aspirations and achievement (Beyer, 1995; Eccles & Harold, 1993; Hossler, Schmit, & Vesper, 1999; Hossler & Stage, 1992; Paulson, 1996). Results indicate a �� ?�� taken as variable of interest on academic achievement in mathematics based on the degree of the students’ motivation (Adedeji, 2007). Deci (1995) argues that autonomy, competence and relatedness are essential needs of all learners and as the degree to which these �� > ��> �� motivation will also rise. Intrinsic motivation has the ability to foster lifelong learning skills (Messali, 2010). Goodenow (1993) established that a sense of belonging and support was strongly associated with motivation and academic achievement. In a study which �� an individual’s career planning, the result of analysis ��motivation and individual career planning is 0.617 and it presents perfect positive correlation (Lee, Liu, 2010). Employers need motivated individuals with an ability to work with little or no supervision. (Hansen, Hansen). �� ' �� among the players of individual and team sports on the variable achievement motivation. (Bal, Singh, 2010). Teams structures are being increasingly used �� >�� relevant. Calvin A. Kent (1985) expresses the view that entrepreneurs are a breed apart because they possess two characteristics that are unique- risk taking and achievement motivation.


[�� '�� '��assigned to teams have accompanied the recent increase in the use of work teams in organisations. A content analysis of 1,060 open-ended comments of employees in two fortune 50 organisations were conducted by Kirkman, Jones, Shapiro (2000) The �� '��| �� issues of trust and low tolerance to change. These may ��'�� X��$��argued that employee tolerance to change will affect their perceptions of post acquisition management’s trustworthiness. It is hypothesized that this and a few other factors are affecting employee perceptions of management’s ability, benevolence and integrity which form their perception of its trustworthiness (Mayer et al,1995)

The purpose of education in business schools can be � �� course culminates viz. the placement of students in various prestigious companies.

The Study:

The purpose of this research is to study the students to identify which attributes of the self are relevant in �� > ��;�;�� placements.

Objectives: The objective of this study is to assess �� '��>'�� X��the 9 different industrial sectors with respect to Left brain preference, Right brain preference, Achievement Motivation and Tolerance to Change.

�� Population: The population consists of the students of Management of 2 batches: 2007-2009 and 2008-2010 (Management course duration is two years). Age range of the respondents is 23 to 29 years. All are graduates from different disciplines like Arts, Science, Commerce, Engineering, Pharmacy.

�� 210

Distribution of the students who were placed on jobs, sector wise are as follows:

Table 1:Sector wise sample distribution

B M R I P N F T CNumber 67 30 24 34 12 12 7 5 19

1. Banking and Insurance Sector (B)

2. Manufacturing + Engineering + Oil and Gas + Agriculture (M)

3. Retail + Real Estate + Hospitality + Computer Suppliers(R)

4. IT and ITES (I)

5. Pharmaceuticals(P)

6. News and Media( N)

7. Fast Moving Consumer Goods (F)

8. Telecom Sector (T)

9. Consultancy (C)

Attributes:

The students have been tested for 4 different attributes:

1. Left Brain preference

2. Right Brain preference

3. Tolerance to change

4. Achievement Motivation

The tools used are:

1. Brain Dominance Test for Left brain and Right Brain.

2. Tolerance to Change Test

3. Sentence completion Test for Achievement Motivation.

The tools used for the research are all standardized instruments.

Description of Tools:

1. A brain style is your set of brain-based natural gifts. This test is used for getting the data of two attributes which are Left brain preference and Right brain preference. Brain Style inventory is


developed on the ‘Split Brain’ studies of Roger Sperry proposed in the year 1981 for which he won the Nobel Prize. It consists of 18 pairs of items. The test scores indicate the preference of use of the respondent of the right brain and the left brain

2. Tolerance to change: This test is designed by S. Budner.(McShane, Von Glinow, Sharma, 2006).It is designed to help the respondent understand how people differ in their tolerance to change.

3. Sentence completion test: The test is developed by B.N. Mukherjee.(Mukherjee, 1965 ) It is based on the forced choice method, to measure achievement motivation. It has 50 items, each item has a stem which is incomplete. Three alternative responses are provided to complete the stem. The individual is asked to select two of the alternatives, one which corresponds most and the other which corresponds least with his present likings and attitudes.

The responses of the students were scored according to the Manuals of the tests.

� �� The tests were conducted when the students were pursuing the management course. The administrators were trained in administering psychometric tests. Tests were administered in batches of 40 students each. The total testing was conducted in 4 hours.

The data has been analysed using descriptive and inferential statistics. Kruskal Data: The data is the scores of the sample on the three tools.

Data Analysis: Wallis, ANOVA test, and Duncan multiple range test have been used for inferential statistics.

Analysis:

There are in all 9 industrial sectors where students have been placed and which are being analysed in the study. They are called groups. However for analysis purpose the sectors are split into two clusters: Groups with a large sample size (Cluster I- four groups) and those

��'��=�\[��$$;��'�`��are as follows:

��}~Z��[��

Cluster I n Cluster II n

Banking and Insurance Sector (B)

67 Pharmaceuticals (P) 12

Manufacturing + Engineering +Oil and Gas+ Agriculture (M)

30 News and Media (N) 12

IT and ITES (I) 34 Fast Moving Consumer Goods (F)

7

Retail +Real Estate+ Hospitality +Computer Suppliers(R)

24 Telecom Sector (T) 5

Consultancy (C) 19

�� !�� "� #$�$%$� ��'table 2):

1. Left Brain Preference & Right Brain Preference: The table 3 below, gives the descriptive statistics of Cluster I on the preference of the respondents in using the left brain and the right brain.

Table 3: Descriptive statistics of Left Brain and Right Brain of Cluster I

Left Brain Right Brain

Groups B M R I B M R I

N 67 30 24 34 67 30 24 34

Mean 9.82 10.16 9.83 10.73 8.56 9.53 9.12 10.47

SD 2.96 2.45 2.14 3.13 3.31 2.94 3.13 2.77

To assess the differences between Groups on the two attributes , ANOVA has been used.

Null Hypothesis statement I: The four groups do not differ from each other on scores of Left Brain.

H0: μ1=μ2=μ3=μ4


�� Hypothesis statement I.

Table 4: Inferential statistics for Left Brain Scores

Sum of Squares Df

Mean Sq F

Between 20.771 3 6.92

Within 1186.229 151 7.855 0.88 Not sig

Total 1207 154

Since F value (0.88) is less than the table value, (2.66 at p>0.05, df 3,151) we say that the data supports the null hypothesis. �� "��"��'

Null Hypothesis statement II: The four groups do not differ from each other on scores of Right Brain.

H0: μ1=μ2=μ3=μ4

The following table gives the inferential statistics for Right Brain for Cluster I.

Table 5: Inferential statistics for Right Brain Scores

Sum of Sq Df

Mean Sq F

Between 84.669 3 28.223

Within 1453.01 151 9.622 2.933Sig at 0.05

Total 1537.68 154

Since F value (2.933) is greater than the table value (2.66 at p.0.05, df 3,151) we say that the data fails to support the null hypothesis. Thus we conclude that the �� "��%��#��'

2.Achievement Motivation and Tolerance to change: The following table gives the descriptive statistics of the Cluster I on the two attributes of Achievement motivation and Tolerance to change.

Table5: Descriptive statistics for Achievement Motivation and Tolerance to Change

Achievement Motivation Tolerance to Change

Groups B M R I B M R I

N 67 30 24 34 67 30 24 34

Mean 21.61 24.33 24.20 25.82 66.25 54.83 64.63 64.85

SD 6.66 7.32 7.72 8.13 9.49 7.57 8.74 9.29

To test the differences between the groups ANOVA is being used.

Null Hypothesis Statement III : The four groups do not differ from each other on scores of Achievement Motivation.

H0: μ1=μ2=μ3=μ4

The following table gives the inferential statistics of Achievement motivation attribute.

Table 6: ANOVA for Achievement Motivation

Sum of Square Df

Mean Square F

Between 636.252 3 212.084

Within 9369.142 151 62.04 3.41Sig at 0.05

Total 10005.39 154

Since the F value (3.41) is greater than the table value (2.66 at p.0.05, df 3,151) we conclude that the data fails to support the null hypothesis. Therefore we may say �� (��(��'

Null Hypothesis Statement IV : The four groups do not differ from each other on scores of Tolerance to change.

H0: μ1=μ2=μ3=μ4

The following table gives the inferential statistics of Tolerance to change using ANOVA.


Table 7: ANOVA of Tolerance to change

Sum of Sq Df

Mean Sq F

Between 2853.95 3 951.31

Within 65426.76 151 424.84 2.239 Not sig

Total 68280.71 154

Since the calculated value of F value is less than the table value (2.66 at p<0.05, df 3,151) , the null hypothesis is accepted and we conclude that �� "��'

*' ��(��+�� "�P,N,F,T,C):

1. Left Brain Preference & Right Brain Preference: The following table gives the descriptive statistics of the preference of the respondents on the use of the left brain versus the use of the right brain in Cluster II.

Table 8: Descriptive statistics for Left Brain and Right Brain

Left Brain Right Brain

Groups P N F T C P N F T C

N 12 12 7 5 19 12 12 7 5 19

Mean 9.25 9.83 11.71 8 10.26 9.5 9.41 10.28 10 9.21

SD 3.13 2.29 2.29 2.45 2.42 1.83 3.08 2.75 2.91 3.56

2. Achievement Motivation and Tolerance to change: The following table gives the descriptive statistics of Cluster II on achievement motivation and tolerance to change.

Table 9: Descriptive statistics of Achievement Motivation and Tolerance to Change

Achievement Motivation Tolerance to Change

Groups P N F T C P N F T C

N 12 12 7 5 19 12 12 7 5 19

Mean 21.75 23.17 24.57 18.8 24 68.75 66.75 70 62 65.74

SD 6.72 6.38 5.32 4.44 6.31 11.36 16.49 6.93 4.30 8.31

To assess the differences between Groups on the four attributes , inferential statistics ( Kruskal Wallis test) has been used.

The H values for the four attributes are shown in the following table.

Table 10: Kruskal Wallis test for Cluster II

Group Left Brain

Right Brain

AchievementMotivation

Tolerance to

Change

H value 3.65 1.08 3.61 3.69

Since calculated value of H is not greater than the table �� ;�� > ��null hypothesis is accepted and we conclude that the �� any of the four attributes

Continuing with further analysis of Cluster I:

�� |��Right Brain Preference and Achievement motivation, the researcher is further interested in making comparisons between all possible combinations of groups taken two at a time. After prolonged investigation of the numerous statistical procedures available, it was the researchers’ opinion that Duncan’s Range test of unequal ‘n’ is the most appropriate.

Duncan Multiple range for unequal ‘n’ has been used ��

1. Right Brain- Putting the means in order:

Table 11: Means of groups on Right Brain preference

Group B R M I

Mean 8.57 9.125 9.53 10.47

The following matrix compares the groups in pairs:


Table 12: Matrix of comparison of groups on Right Brain Scores

B M R I

B Not sig. Not sig Sig (0.05)

M Not sig Not sig

R Not sig

I �� ' �� $ ��' �� from each other and it is observed from the descriptive statistics ( refer table 8 ) that Mean I> Mean B. So students placed in the IT and ITES companies have a ��'��placed in the Banking sector.

2. Achievement Motivation- Putting the means in order:

Table 13: Means of Achievement Motivation put in order of value.

Group B R M I

Mean 21.61 24.21 24.33 25.88

The following matrix compares the groups in pairs:

Table 14: Matrix of comparison of groups on Achievement Motivation

B M R I

B Not sig. Not sig Sig (0.05)

M Not sig Not sig

R Not sig

I Only B group and I group are again found to be �� from the descriptive statistics ( refer table 9 ) that Mean I> Mean B. So students placed in the IT and ITES ��(��(��(�� #��/��'

Discussion:IT industry in India is one of the fastest growing industry. Indian IT industry has built up valuable brand equity for itself in the global markets. IT industry in India comprises of software industry and information technology enabled services (ITES), which also includes business process outsourcing (BPO) industry. India is considered as a pioneer in software development and a favorite destination for IT-enabled services. In the last few years Indian IT industry has seen tremendous growth. According to the annual report 2009-10, prepared by the Department of Information Technology (DIT), the IT-BPO industry is expected to garner a revenue aggregate of US$ 73.1 billion in 2009-10 as compared to US$ 69.4 billion in 2008-09, growing at a rate of over 5 per cent. The report predicts that the Indian IT-BPO revenues may reach US$ 225 billion in 2020.

According to DIT, the Indian software and services exports is expected to reach US$ 49.7 billion in 2009-10 as compared to US$ 47.1 billion in 2008-09, registering an increase of 5.5 per cent in dollar terms. Further, the IT services exports is estimated to grow from US$ 25.8 billion in 2008-09 to US$ 27.3 billion in 2009-10, showing a growth of 5.8 per cent. Unveiling the information booklet of ‘e-India 2010’, L. Suresh, President, IT Department, Government of Andhra Pradesh said, the IT industry in India is now growing at 13 to 15 per cent.(ANI, 2010)

The experts are however of the opinion that there are certain things that need to be done in order to make sure that India can maintain its status as one of the leading information technology destinations of the world. The ��'�� for innovation that could be carried for a long time. The innovation needs to be done in three areas that are connected to the information technology industry of India such as business models, ecosystems and �� #� ��'�� of the current research indicate that the focus in the selection procedure in IT sector is on the creativity and innovation as well as the achievement motivation criteria.


A look at the Banking sector in India reveals a totally contrast picture. There is a failure to respond to changing market realities which has stunted the development of the sector(regulatory authority, 2009), there is a scope for better performance(T.V, Gopalkrishnan, 2010). In a ‘White Paper’ published on 9th��}��^>��sector regulatory entities, have emphasize the need to act both decisively and quickly to build an enabling, rather than regulating sector. Costs of inaction or ��

Management success will be determined on the human capital upgradation to reach the three fronts: fundamentally upgrading high-performing scenario; organizational capability to stay in tune with the changing market; adopting value. Processes to create �� '�� missing in the banking sector of India. As a result, growth and productivity levels in banks are still low. Thus, while the sector emerges as a support to a fast-growing economy, this important driver of the economy and wealth scenario sees limited consolidation in the year 2010. It has still not come of age and most banks ��;��''�� '�'�� achievement motivation and preference of right brain use in the recruits of the banking sector.

Conclusion: The study of the student groups placed in different sectors indicates that only two groups of students, those who are placed in the Banking Sector and those placed in the IT and ITES sector differ from ��Motivation and Use of Right Brain.

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]�� #��/�� *[\[ ^ _��Transcript, INDIA BANKING 2010 TOWARDS A HIGH PERFORMING SECTOR 9TH JULY, 09, http://www.slideshare.net/Dreamgains/white-paper-india-banking-2010


IPO Grading and Short Term Performance of IPOs in India

* Assistant Professor-National Institute of Financial Management An autonomous Institute of Ministry of Finance, Government of India Sector 48, Pali Road, Faridabad 121 001 Haryana, India Mobile-08860833531 [email protected], [email protected]

The study investigates the effect of IPO Grading on the short ��&'�� !�� !�� &'�� (�� &'��(�� &'��!��)�� *�� effect of a particular grade at short term performance of IPO is analyzed, there is no evidence of any effect of IPO grade on ��&'��)�� !�� !��!�� market conditions that prevailed during the study period.

Key Words: IPO, Grading

Dr. Harish Kumar Singla *

The IPO Market in India has been developing since the liberalization of the Indian economy. It has become one of the foremost methods of raising funds for various developmental projects of different companies. SEBI has made it mandatory for companies to offer a part of their stocks to retail investor. Retail participation in IPO market is also increasing day by day. Retail investors consider IPO as safe investment to make quick returns on the day of listing. Companies in order to attract retail investors (Considered to be uninformed investor) do under pricing of their IPO, so that their IPOs get subscribed.

As part of a series of initiatives to protect investors’ interest, the market regulator Securities and Exchange Board of India (SEBI) has introduced grading of public issues. IPO grading is the grade assigned by a Credit Rating Agency registered with SEBI, to the initial public offering (IPO) of equity shares or any other security which may be converted into or exchanged with equity shares at a later date. The grade represents a relative assessment of the fundamentals of that issue in relation to the other listed equity securities in India. �� ;'��point scale with a higher score indicating stronger fundamentals and vice versa as below.

IPO grade 1: Poor fundamentals

IPO grade 2: Below-average fundamentals

IPO grade 3: Average fundamentals

IPO grade 4: Above-average fundamentals

IPO grade 5: Strong fundamentals

Some credit rating agencies (CRAs), including ICRA, CRISIL, Fitch Ratings India and CARE, are registered


with SEBI. According to SEBI, IPO grading is mandatory, 1st May 2007 onwards.

The main objective of IPO grading is to enable investors to have an independent opinion from credible entities about the equity issue of an unlisted company.

Literature ReviewThe literature on IPOs generally concludes that IPO ��>��>��'�� '�� long run. According to Ritter and Welch (2002), the ��$��]�� average, from 1980 to 2001. Ljungqvist (2007) shows that, in the US, the average for IPO under pricing has been approximately 19% since the 1960s. While the studies on international IPO initial returns have been consistent in terms of return, the nature and underlying contributing factors of IPO short-term performance are still unclear.

There have been number of researches on IPO (Initial Public Offer) under pricing. There are several explanations to account for short-term IPO under pricing, recent studies support the information asymmetry explanation.

Information Asymmetry says that all Investors are not equally informed as information research is costly and time consuming. Some investors become informed �� |�� uninformed. Informed investors subscribe IPOs only when they know that these issues are underpriced and there is money to be made, while uninformed investors can subscribe to any IPO. Since the management’s goal ��?��=��?'�� '�� >��?�price for IPO, which gives them desired funds at the same time ensure that the IPO is subscribed. A lower price induces more uninformed investors to submit bids in the offering. An increasing number of investors, who potentially submit bids in the offering, also increase the probability that the offering will succeed.

The IPO literature to date is unclear on the impact of IPO grading on short-term IPO performance. As an extension to the IPO study, An and Chan (2008)

examine the impact of IPO grading on IPOs’ short-term '�� $�� ratings, on average, exhibit 22% less under pricing than �� '�� with a credit rating prior to the IPO issuance can �� '�� '�� > $�� credit ratings have less short-term under pricing, more immediate and more complete market reactions, and ��;��'��

Reduced IPO short-term under pricing is the result of information risk reduction because investors demand higher risk premiums when perceiving higher risk. Consequently, reduced information asymmetry leads to lower required returns and higher security prices \�� ]^�_`��$��

Since it’s an emerging area, the research on IPOs, is very limited in Indian Market. In particular, no comprehensive work is available, which deals with the IPO under pricing in India and IPO grading and its impact of IPO under pricing.

The objective of this study is to examine the impact of credit ratings on IPO short term performance.

Data Methodology and ResultsAs discussed above in the objective, the study aims to see the impact of IPO grading on its short term performance.

The sample in the study is in two sets i.e. Pre IPO grading and Post IPO grading

Post IPO grading contains all the companies (90) that came out with an Initial public offering after May 2007 till December 2010, as IPO grading became mandatory from 1st May 2007. The sample pre grading consists of IPOs that came out with an initial public offering between 1/1/2005 to 30/12/2006. 123 such IPO’s were �� #�% �� 87 were selected for the study as rests were either FPO’s (following Public offerings) or the data was not available. Both the time period chosen are very challenging from the research point of view, as these


years witnessed, the market going achieving its all time high of more than 21000 points (BSE) and going to a low of 7500 points(BSE). The details like offer price, size of IPO, listing date, IPO grade, Price range is collected from NSE website. The closing stock price on listing day is collected from CMIE PROWESS. The prices were converted into return taking the listing day, offer price as base. The above can be expressed in the equation given below.

= [(P1-Po)/Po] X 100 where Po= the offer Price, P1 = the close price ------------------------------Eq1

Hypothesis to be tested

Ho- The performance of graded IPOs is same as performance of non-graded IPO.

Ha- The performance of graded IPOs is different from performance of non-graded IPO.

Table-I (T-Test: Two-Sample Assuming Equal Variances)

Pre Grading

Post Grading

Mean 10.29% 13.75%

Variance 6.47% 9.45%

Observations 87 90

Hypothesized Mean Difference 0

df 175

t Stat -0.81327**

P(T<=t) two-tail 0.41717

t Critical two-tail 1.97361

As reported in table-I, T stat is less that T critical at 5% level, we accept the null hypothesis that there is no difference between the short term performance of IPOs pre and post grading. But it is still not clear whether; �� '��$��address this use we test the following hypothesis.

Ho- IPO grading has no effect on short term performance of IPO

Ha- IPO grading has an effect on short term performance of IPO

The results are presented in table-II.

�� ¡$��¢��Z%

Where SR stands for Stock return on listing day

And IPOGRADE stands for Dummy variable (1 is IPO is graded else 0)

�£¡��'�� '�

Table-II (Effect of IPO grading on performance of IPO)

Variable �� Std. Error

t-Statistic P

Intercept 0.102904 0.03029 3.3969 0.00084IPO Grade 0.034549 0.04248 0.8132 0.4171R-squared 0.00376

It is evident from table-II, that IPO grade has no effect on the short term performance of IPO.

Table-III (Descriptive Statistics)

Pre Grading

Post Grading

Mean 10.29% 13.75%Standard Error 2.73% 3.24%Standard Deviation 25.43% 30.74%Sample Variance 6.47% 9.45%Kurtosis 1.82 2.37Skewness -26.85% 135.76%Minimum -88.89% -47.60%Maximum 72.56% 129.25%Companies recorded –ve Returns 22 31[��'�� Returns 65 59Count 87 90Average Return Generated by Nifty 14% -6%


Table-III, gives a summary data of IPOs pre and post Grading and it is observed that IPOs post grading are recording higher returns, i.e. reporting more under pricing in short term when compared to IPOs pre grading. The average return generated are 10.29% pre grading and 13.75% post grading, which is higher for post grading IPOs. Further, the variation in returns is much higher in post graded IPOs compared to Pre graded IPOs. This counters the argument that investors react more calmly and in complete manner to graded IPOs. However, the fact can not be denied that the ��

different market conditions. For this purpose, the returns ��'�� #��returns to be much higher than Nifty returns in pre and post grading market. But no conclusive evidence can be drawn.

This bring us back to the point that if graded IPOs do perform better and different from non graded IPOs, to what extent each grade has an effect on such under pricing. For this purpose, a simple OLS regression model is prepared, where the stock return on listing day is considered to be a dependent variable and IPO grade is considered as an independent variable and the following hypothesis is tested.

Ho=IPO individual grades has no effect on the short term performance of stock returns

Ha= IPO individual grades has a effect on short term performance of stock return

The results are presented in table-IV.

�� ¡$��¢

Where SR stands for Stock return on listing day

And IPOG stands for IPO grade, (Grade 1 is poor and grade 5 is strong)

�£¡��'�� '�

As the results display in table IV, F statistic is more ��;'��>�� > �� '�� '�� $��grade has no effect on the stock returns. To study the impact of individual grade on performance of stock return, further each grade was separately analyzed. A regression model based on dummy was created for the purpose.

�� ¡1$��¢] ¡2$��¢} ¡3$��¢¤ ¡4 IPOG4

IPOG1, IPOG2, IPOG3 and IPOG4 are dummy variables, and IPOG1 will take the value 1 if grade is 1 else 0, IPOG2 will take a value of 1 if grade is 2 else 0 and so on. No dummy is created for IPO grade 5 as ��'�� results of regression are presented in table-V.

Table-IV (Regression results of stock return and IPO grade)

Variable �� Std. Error t-Statistic P

Intercept 0.255189 0.106123 2.404646 0.0183

IPO Grade -0.04123 0.035396 -1.164817 0.2472

R-squared 0.015184

Adjusted R-squared 0.003993 F-statistic 1.356798

S.E. of regression 0.306791 Prob(F-statistic) 0.247241

Durbin-Watson stat 1.830918

Note-To further test the results of table-IV, two control variables “size of IPO and Market return” were introduced ��'�� "��'


Results in table V, show that F statistic is more than �;'��>�� > �� '�� accepted. IPO grade has no effect on the stock returns. The value of R2 is very low.

ConclusionThe results are inconsistent with the information asymmetry reduction hypothesis. We have found that $��'�� different in terms of short term performance, but the results also indicate that under pricing has increased after IPO grading. Though, when it was investigated further to see the impact of grading on short term performance of IPO, it shows no impact. Therefore, it can be concluded that graded IPOs display higher under pricing, but this under pricing is not contributed �� of external factors on the short term performance of IPO (R2 = 0.015). This leaves a scope for future research in the area to study the external factors that �� '�� $�� $� ��$��>�� of IPO is not affecting the pricing of IPO.

It leave a huge question mark on the objective and utility of introduction of IPO grading as graded IPOs have witnessed greater variability in performance.

References�� An H., Chan K.C. (2008). Credit ratings and IPO

pricings. The Journal of Corporate Finance, vol. 14, pp584–595.

�� Beatty R., Ritter J. (1986). Investment banking, reputation and underpricing of initial public offerings. The Journal of Financial Economics, vol. 15, pp 213–232.

�� Carter R.B., Dark F.H. and Singh A.K. (1998). Underwriter reputation, initial returns, and the long-run performance of IPO stocks. The Journal of Finance, vol. 53, pp 285–311.

�� Cai N., Lee H.W. (2005). The long-run post-offer performance of subsequent IPOs ��%��, The University of Michigan-Dearborn.

�� Gomers, P.A., Lerner J. (2003). The really long-run performance of initial public offerings: the pre-Nasdaq experience. The Journal of Finance, vol. 58, pp1355–1392.

�� Loughran T., Ritter J.R. (1995). The new issues puzzle. The Journal of Finance, vol. 50, pp23–51.

�� Ljungqvist A., (2007). IPO Underpricing: a survey. Handbook of Corporate Finance. Empirical Corporate Finance, North-Holland, pp 378–422

Table-V (Regression results of stock return and IPO individual grade)

Variable �� Std. Error t-Statistic P

Grade-5 0.397347 0.302411 1.313931 0.1924

Grade-1 0.010111 0.326641 0.030955 0.9754

Grade-2 -0.24863 0.308172 -0.8068 0.422

Grade-3 -0.32132 0.306826 -1.04724 0.298

Grade-4 -0.26356 0.308915 -0.85317 0.396

R-squared 0.075723 F-statistic 1.740937

Adjusted R-squared 0.032227 Prob (F-statistic) 0.148499

S.E. of regression 0.302411

Durbin-Watson stat 1.899659


�� Pukthuanthong-Le K., Varaiya N. (2007). IPO pricing, block sales, and long-term performance. +��"��, vol. 42, pp319–348.

�� Ritter J.R., Welch I. (2002). A review of IPO activity, pricing, and allocations. The Journal of Finance, vol. 57, pp1795–1828.

�� http://www.sebi.gov.in/faq/ipograding.html

�� Rock K. (1986). Why new issues are underpriced. Journal of Financial Economics, vol. 15, No. 112, January/February, pp. 187-212.

�� Robert Eisenbeis, Richard W. McEnally. (1995). Initial Public Offerings: Findings and Theories. ��/�4��*�� &�!��.

�� Schenone C. (2004). The effect of banking ��'��|�$�� '��The Journal of Finance, vol. 59, pp2903–2958.

Form IV

1. Place of Publication : Mumbai2. Periodicity of its Publication : Quarterly3. Printer's Name : Dr. U.K. Debnath4. Whether Citizen of India

\$��>��£[��`: Indian

Address : National Institute of Industrial Engineering Vihar Lake, Mumbai 400 087.

5. Publisher's Name : Dr. U.K. DebnathWhether Citizen of India\$��>��£[��`

: Indian


6. Editor's Name : Dr. (Ms) Mani K. MadalaWhether Citizen of India\$��>��£[��`

: Indian


7. Name and address of Individual who own the newspapers and partners of shareholder more than one per cent of the total capital

: National Institute of Industrial Engineering Vihar Lake, Mumbai 400 087.

Dr. U.K. Debnath, hereby declare that the paticulars givcn are true to the best of my knowledge and belief.

Dated : 1st March, 2012 Sd/- Signature of Publisher


How SPC Tools are helpful in Automotive Industry: A Case Study

* Assistant Professor in the Department of Mechanical Engineering, PEC University of Technology (formerly Punjab Engineering College), Chandigarh-160012 (India). E-mail: [email protected]

In this paper, an attempt has been made to implement the some statistical process control (SPC) techniques in the industry that is offering its customers the widest and latest range of sealing solutions for various applications in the automotive industry. The power of SPC lies in the ability to examine a process and the sources of variation in that process, using tools that give weightage to objective analysis over subjective opinions and that allow the strength of each source to be determined numerically. Only two main techniques i.e. cause and effect diagram and control charts are implemented in this industry, out of seven SPC techniques. The present work deals with the study of defects in shocker seals of an automotive industry. It is found that after implementing the SPC tools to remove the root causes, the percentage rejection is reduced from 8.5% to less than 1% and process capability is also improved.

Key Words: Cause and effect diagram, control charts, process capability index, casting defects and percentage rejection.

Dr. D.R. Prajapati *

IntroductionThe variation is the law of nature and no two natural items in any category are exactly the same. The variation may be quite large and easily noticeable such as the height of humans or the variation may be ��''� '��¦��variations are very small, it may appear that items are identical; however precision instruments will show differences. If two items appear to have the same measurements, it is due to limitations of our measuring instruments. As measuring instruments have become more precise, variation has continued to exist; only the amount of variation has changed. The ability to measure this variation in the product is necessary before it can be controlled. In all production processes, we need to monitor the extent to which our products �� '�� '�� \��[`is a technique to monitor the processes to identify the variation and signalling to take corrective action. Many industries require their suppliers to provide evidence �� '�� [ '�� capability necessary for survival in today’s competitive market.

§�� [ �� control charts and cause & effect diagrams, focused on continuous improvement. Variations in the process that may affect the quality of the end product or service can be detected and corrected, thus reducing waste as well as the likelihood ��'��'�� ¦��its emphasis on early detection and prevention of '��> ��[ �� quality methods. In mass-manufacturing, the quality �� through post-manufacturing inspection of the product; accepting or rejecting each article (or samples from


a production lot) based on how well it met its design �'��

$� ��> �� [�� \��[` ��statistical tools to observe the performance of the '�� '�� '�� deviations that may later result in rejected product. By observing at the right time what happened in the process that led to a change, the quality engineer or any member of the team responsible for the production line can troubleshoot the root cause of the variation that has crept in to the process and correct the problem. ��[ �� '��> �� #� ��should be taken. An example is a person who would like to maintain a constant body weight and takes weight measurements weekly. A person who does not �� [��'�� his or her weight increased, or eat more every time his or her weight decreased. This type of action could be harmful and possibly generate even more variation in �� [ �� variation and better indicate when the person is in fact gaining or losing weight.

�� '��'�� '�� [ �� '�� are available to help organizations to better understand and improve their processes. The essential tools for the ��'��~[��>[��;�� ;%�� >��[��>��[��>��Z��>Histogram or probability plot and Control Charts.

Check sheets are simply charts for gathering data. ¦�� >they assist in gathering accurate and pertinent data, and allow the data to be easily read and used. Cause-and-Effect or Fishbone diagram are also called Ishikawa diagrams because Kaoru Ishikawa developed them �� '�� chart organizes and displays the relationships between different causes for the effect that is being examined. This chart helps to organize the brainstorming process. The major categories of causes are put on major branches connecting to the backbone and various sub-causes are attached to the branches. Flowcharting

breaks the process down into its many sub-processes. Analyzing each of these separately minimizes the number of factors that contribute to the variation in the process. The Pareto chart can be used to display categories of problems graphically so they can be properly prioritized. The Pareto chart is named for a 19th century Italian economist who postulated that a small minority (20%) of the people owned a great '��'��\��`�� '��'��'�� '�� to uncover possible cause-and-effect relationships. It is constructed by plotting two variables against one ��'��?��'��'��that one variable causes another, but it does show how a pair of variables is related and the strength of that relationship.

The probability plot is a graph of the cumulative relative frequencies of the data, plotted on a normal probability scale. The purpose of this plot is to show whether the data approximates a normal distribution. A histogram is a snapshot of the variation of a product or the results of a process. It often forms the bell shaped curve which is characteristic of a normal process. Control charts are an essential tool of continuous quality control. Control charts monitor processes to show how the process is performing and how the process and capabilities are affected by changes to the process. This information is then used to make quality improvements. Control charts are also used to determine the capability of the process. They can help to identify special or assignable cause for factors that impede peak performance.

Capability index value and comparing the calculated capability index to the desired index value, making a decision concerning process changes, and recommending any suggestions to reach the desired goal. The popularity of capability measures continues �� Terminology associated with this subject must be relatively easy and provide a common language for ��'��>��''��well as with customers.

��[�� ~\�`��


for variables and (ii) control charts for attribute. The control charts based on variable data that can be measured on a continuous scale i.e. weight, volume, temperature etc. are known as control charts for variables. The control charts based on discrete data i.e. counted as ‘present’ or ‘not’ are called control charts for ��¦�� >a subgroup is the group of units that are inspected to obtain the number of defects or the number of defective ��Figure 1.

Theory of Charts

In chart, means of small samples (3-5) are taken at regular intervals, plotted on a chart, and compared against two limits. The limits are known as upper control limit (UCL) and lower control limit (LCL). �� ~

LCL = - A2 R and UCL = + A 2R

¦��>

is the target mean and factor A2 depends on sample


size. The process is assumed to be out of control when the sample average falls beyond these limits.

Theory of Range (R) charts

In these charts, the sample ranges are plotted in order to control the variability of a variable. The centreline of the R chart is known as average range. The range of a sample is simply the difference between the largest and smallest observation.

If R1, R2, ..., Rk, be the range of k samples, then the average range (R bar) is given by:-

The upper and lower control limits on the R chart are:

Upper control limit:

Lower control limit:

¦��>Factors, D3 and D4 depend only on sample size (n)

2.0 Literature ReviewA lot of research on statistical process control is done ��'��''�� ?'��=��[��in the area of production, manufacturing and design.

�� \]^¤]` �� '� �� control and stated that a control chart is not a test of �� $� �� control, he described to select the criterion (the three sigma limits). A control chart predicts that, in the absence of assignable causes, the process will operate as a random system and produce the present level of quality in the future. If that level of quality is not satisfactory, a fundamental change in the process is required.

Ishikawa (1985) added the cause-and-effect chart as an aid to brainstorming, but all the rest of the methods were the same as those recommended by Juran (1999). The popularity of these problem-solving tools and the ease of their use caused many to adopt them. Kane (1986) carried out a study on capability indices and examined

the uses of capability indices along with their estimation '�� ¦��'��'��''�� >��an effective means for improving process quality.

Costa (1999) also studied the performance of joint and R charts with variable sample size and variable sampling intervals. He compared the performance of �� X�� X�� X�� noticed that the joint charts proposed by Costa (1999) ��'��|�X��

Jaju et al. (2002) conducted a process capability study for bought out components for tractor manufacturing industry. The methodology used consists of component ��> �'�� > �� analysis using cause and effect diagram, control charts and process capability study.

Lillrank and Kujala (2006) examined on the �''�� [ �� ;��'�� '�� open systems, non-routine processes and project-based business activities. They also proposed guidelines for � X�� '��project-based businesses are proposed. Puga-Leal and Pereira (2007) demonstrated that traditional capability indices do not cope properly with service performance characterized by a zone of tolerance and that the new capability index, proposed by the authors, is more than � �� '��

�� \}��^` �� Z�� \�Z�` �� [�� \��[` '�� '�� performances in manufacturing companies. The focus �� duration of machine breakdowns as well as the major causes of breakdowns affecting productivity. They �� [ �� '�� X��loss times from various machine breakdowns using �Z�� can cause a huge cost and the best approach to address any breakdown is the preventive measure.

¦� �� \}�]�` '��'�� ;�� in correlated processes. The proposed model used a selective network ensemble approach named Discrete �� '��=�� \Z��%#` ��


the improved generalization performance, which outperforms those of single neural network. The model was capable of on-line monitoring mean and variance shifts, and classifying the types of shifts without considering the occurrence of both mean and variance �� it provided additional useful information about the '��>�� of assignable causes.

Prajapati and Mahapatra (2010) suggested the new design approach of control chart for detecting the process shift, when process goes out of control. The concept of their chart is based upon the theory of average �� '�� \��`�� \��"�`of proposed chart are computed and compared with �� [©�©�> ��|� ��;�� [©�©�� |�[©�©��'��the proposed chat is fairly comparable to the various [©�©� �� '��mean.

Abdolshah et al. (2011) stated that process capability indices (PCIs) are appropriate tools to measure the inherent capability of a process, but most of them do not consider the losses of a process, while in today’s competitive business environment, it is becoming more and more important for companies to evaluate and minimise their losses. They presented a review of ��;�� [$� �� ['�> ['��> �[$ª> ['> "�� "«�� ;based PCIs such as reject based, asymmetric, bounded, loss based and target based. Finally, they made some recommendations for developing a new loss-based process capability index with more excellent �'��

3.0 Introduction of Industry and Products

�� '��rubber parts industry, situated in northern India. It is recognized as the largest manufacturing company in the �� '��$� ��>�� '�� $� �� ;]_^¬^~}��}>�;^�� $��;^��}��

Being the unchallenged market leader in the sealing technology, this group of companies together offers its customers the widest and latest range of sealing solutions for various applications in the automotive and �� $��'�� class products:

� ��

� ��

� ��®��

� ��

� ��

� !��

3.1 Brief Description of the Case Study

��'�� which needed more attention because of their higher rejection. These shocker seals have percentage rejection of around 8.5%, as shown in Table 1.

Table 1 Percentage rejection of shocker seals before implementing SPC

S. No.

Month & Year

No. of shocker

seals produced

Rejected shocker

seals

Percentage rejection

1. April, 2009 16,200 1378 8.51

2. May, 2009 16,200 1366 8.43

3. June, 2009 16,200 1343 7.96

4. July, 2009 16,200 1297 8.00

5. Aug., 2009 16,200 1399 8.64

6. ��'��>}��^ 16,200 1382 8.53

7. ��>}��^ 16,200 1406 8.70

8 #��>}��^ 16,200 1365 8.43

9. Dec., 2009 16,200 1377 8.50

10. Jan., 2010 16,200 1395 8.61

11. Feb., 2010 16,200 1383 8.54

12 March, 2010 16,200 1378 8.51

TOTAL 1,94,400 16,475 101.70

Average monthly rejection = 101.70/12 = 8.475%


3.2 Case study

In this case study, rejection level of shocker seals before ��'�� [ �� |� ��¯ ��[techniques are required to implement on these products to reduce the percentage rejection.

The main root causes, during moulding process of shocker seals are mentioned below.

(i) Moulding

�� '�� shocker seals. It is found that various moulding defects are responsible for the rejection. Following defects are observed in this process.

� Air trap

� Tear

� Knitting

� Foreign matter

� Curing

� Excess material

� Less material

� Dirty cavity

Probable causes for each defect are listed below:

Air trap

� $��!��

� Improper Environmental temperature

Tear

� High temperature (19000C-21000C)

� Improper manual loading

Material (excess/less)

� Improper setting of grub screw volume

Cold bit

� Improper cleaning of nozzle hole

� Dirty Top portion of mould

Trimming

� ��'��

� �'��"��'��

� ��'��'��

Figure 2 Cause and effect diagram for shocker seals


Following recommendations are given to eliminate various root causes.

3.2.1 Recommendations to remove the moulding defects:

� In-process inspection is must for each manufacturing operation.

� Adequate vacuum must be created.

� Proper Environmental temperature should be maintained

� High temperature (19000C-21000C)

� Manual loading should be replaced by mechanised loading.

� Grubbed screw volume should be maintained at the required level.

� Clean the nozzle hole properly.

� Clean top portion of mould properly.

� Trimming should be done very carefully.

3.3 Implementation of and range (R) charts to diameters of shocker seals

The sample size (n) of 4 is considered and 400 observations of outer diameter of shocker seals 4 are taken in random manner. These observations are taken after removing the root causes, as suggested in section 3.2.1. The observations before the case study are not included in this paper due to length of the paper. The rejection level of 12 months are shown in Table 1. The concept of sub-grouping is followed when observations are taken. These observations are shown in Table 1A (Appendix A).

3.3.1 Methodology

The initial process is assumed to be in-control, and it remains under control until an assignable cause occurs. After which the process mean shifts and can be measured in terms standard deviation units.

The chart area is divided into 2 regions namely (i) Rejection region (ii) Acceptance region, as shown in Figure 3.

Figure 3 Pattern of points falling in different regions of chart


¦��'�� point falls in ‘ rejection region’, it can not be guaranteed that the process has a mean equal to target mean \°0) and standard deviation equal to target standard ��\±0). This situation leads to acceptance of the ��'��\�]`��'��'��|>��#��'��\�0) can be accepted. The following procedure is adopted to implement the control charts in the industry.

Step 1 Take the samples of sample size of 4 from industry at random basis.

Step 2 Compute the sample mean and range of each sample.

Step 3 Calculate the upper and lower control limits on both and R charts.

Step 4 %�� \±|` �� process.

Step 5 Plot the sample points both and R charts. If any points fall beyond the upper control limit (UCL) or Lower control limit (LCL), the process may be assumed to be out of control and corrective action is required.

Step 6 [�� '�� '�� \_±|` �� '��\©�";"�"`>��>©�"�� "�"��''�� '��product respectively.

Step 7 $� \©�";"�"` �� '��'�� \_±|`> �� '�� '�� '�� '�� \©�";"�"`��'��'��\_±|`>��'�� '�� '�� defective products, which needs corrective actions.

Step 8 Calculate the process capability index (Cpk) of the process.

3.3.2 Calculation of Average and range values

Target outer diameter of shocker seals = 42 mm ± 0.10 mm (tolerance)

��> �''�� '�� calculated as:

©''��'��"��\©�"`�¬}�]�� "��'��"��\©�"`�¬]�^��

Mean ( ) Chart

Mean or Average of one sample can be calculated as:

= (X1 + X2+ X3+ X4) ÷ 4

¦��>��'��=��¬\��`

��>��¬��'��calculated as

¦��>��'��¬��\��case)

�]_��¤�¤®¬��¬}�� be calculated as:

�}^�}]®¬��³¤

Upper control limit =

= 42.008 +0.738×0.095= 42.08 mm

Lower control limit =

= 42.008 - 0.738×0.095= 41.93 mm

A2 = 0.738, D4 = 2.28, D3= 0 (values of these factor, corresponding to sample size, are available in all the books of Quality control)

Range (R) Chart

�}^�}]®¬��³¤

where, k is the number of subgroups = 400


Upper control limit on R chart

= 2.28* 0.073 = 0.166

Lower control limit on R chart

= 0*0.073 = 0

3.3.3 Calculation of Process capability (Cp)

��'�� \±|`� ® }��> d2 = 2.059 (for sample size of 4)

±|��³¤®}��^��¤�

��>��'��\['`�_±| = 6×0.035= 0.212

To be process under control,

\©�"´"�"`¶_±|

\¬}�]�´¬]�^�`¶�_·��¤_��}]}

��}�¶��}]}

��> '�� X��level has been reduced after removing the root causes of rejection.

The Process Capability ratio can also be calculated as:

��[�'��\©�";"�"`®_±| �\¬}�]�´¬]�^�`®_·��¤�¸ = 0.952

3.3.4 Calculation of Process capability Index (Cpk)

Process capability Index (Cpk) can be calculated as:

Cpk = Minimum [( ;"["`®¤±|>\©["´ `®¤±|¸

��¹\¬}��´¬]�^�`®¤º��¤�> \\¬}�]�´¬}��``®¤º��¤�¸

Cpk ��¹]��}>��¸��

��>��'��$� �?\[pk) of the process is 0.88

It indicates that the process average is currently nearer ��©�"��'�� "�"�

Although 400 observations of the shocker seals are taken, only plots of 100 observations for and are R

charts are shown in Figures 4 and 5 respectively. The reason to show the graphs of only 100 observations is the clarity of the plots. These observations are taken after removing the root causes of rejection.

Figure 4 Graphical representation of chart

Figure 5 Graphical representation of R chart

It is clear from the Figures 3 and 4 that all the observations are falling within the control limits on both and R charts.

4.0 Conclusions�� '�� '�� > �� '��can considerably decrease the loss to the industry in terms of both money and time. Although, improvement in rejection level of all the other products of the industry is noticed, shocker seals were the main concern because the rejection level of this product was around 8.5%. The basic requirements of the manufacturing processes are studied and then the statistical process �� '�� '�� [��'� ��'��


of the manufacturing process to decrease the number of defective products, thus saving a lot of re-work cost and valuable time. After implementing the required ��®�� > �� found that process capability is improved and rejection �� ]��¬��of outer diameter of shocker seals, no any observation is falling outside of control limits on both and R charts as shown in Table 1 (Appendix A).

Refferences � Abdolshah, M., ��, R. M., Hong, T.

�� Ismail> �� \}�]]`> �"��;�� '�� '�� ~ � ��>International Journal of Productivity and Quality Management,!��³>#��]>''�];}].

� [��> �� \]^^^`> � charts with !�� > Journal of Quality Technology>!��¤]>#��¬>''�¬��;¬]_�

� Ishikawa, Kaoru (1985), What is Total Quality Control?, Z� �� "� \��`> #�� ~Prentice Hall, $��# 0139524339.

� ��X�>��>��\}��}`>��[�'�� [��'�� $� ��> $� ��%�� >!�� ¤]>#��¬> ''� ]¤;19.

� �©��#> �� \]^^^`> �� >5th Edition, McGraw-Hill.

� Kane, V.E.(1986), Process Capability Indices, Journal of Quality Technology, Vol. 18, pp. 41-52.

� "$""��#§> �� §©��"�> �� \}��_`>“�� '�� of quality problems in project-based ��=��> International Journal of Productivity and Quality Management, Vol. ]>#�;]®}>''��_;_��

� PRAJAPATI, D. R. and MAHAPATRA, P. B. \}�]�`> �� [��'��Z��of [��> Journal of Udyog Pragati (NITIE),!��¤¬>#��]>''�]_;}¬�

� Puga-Leal, R. and Pereira, Z. L. (2007) ��'��>International Journal of Quality & Reliability Management, !��}¬$��>''��´�]}�

� ��%¦��> ¦� �� \]^¤]`> �%��'�� >Van Nostrand, New York.

� �� > ��=��> #� �� =��> ��\}��^`> �$��"%�%#��$�# ��$��$[�" ��[%�� [�#��"(SPC`�> Journal of Mechanical Engineering,IEI (Bangladesh), Vol. 40 (1).(www.banglajol.info/index.php/JME/rt/captureCite/3466/2908.

� ¦�> �� > ��;�� \}�]�`> �� #��#��;�� ;�� !�� >International Conference on E-Business and E-Government, pp. 2615-2618.

APPENDIX ‘A’Table 1A Observations of outer diameter of shocker seals after implementing

the SPC tools

S.N0 X1 X2 X3 X4 R

1 42.02 42.03 42.05 41.98 42.02 0.072 42.01 42.01 41.97 41.98 41.9925 0.043 42.04 42.05 42.01 42.03 42.0325 0.044 41.97 41.98 41.97 42.04 41.99 0.075 41.96 41.97 42.05 42.01 41.9975 0.096 41.98 42.04 42.04 41.97 42.0075 0.077 42.03 42.01 41.97 41.98 41.9975 0.068 42.02 41.96 41.98 42.05 42.0025 0.099 41.98 42.01 42.04 42.06 42.0225 0.0810 41.96 41.98 41.97 41.98 41.9725 0.0211 41.97 42.04 42.08 41.97 42.015 0.1112 41.94 42.05 42.02 41.98 41.9975 0.1113 41.98 42.04 42.1 41.97 42.0225 0.1314 42.06 42.02 41.9 41.94 41.98 0.1615 42.04 41.95 41.98 42.06 42.0075 0.1116 42.04 41.98 42.02 41.96 42 0.08


S.N0 X1 X2 X3 X4 R

17 41.98 41.97 42.03 42.06 42.01 0.0918 41.94 41.98 42.02 42.07 42.0025 0.1319 41.96 41.92 41.98 42.02 41.97 0.120 42.02 42.03 42.06 42.09 42.05 0.0721 42.03 42.07 41.98 41.98 42.015 0.0922 42.07 42.05 42.08 41.93 42.0325 0.1523 42.1 42.04 41.96 41.92 42.005 0.1824 41.98 41.96 42.04 42.06 42.01 0.125 41.96 41.98 42.02 42.06 42.005 0.126 42.05 42.05 41.98 42.02 42.025 0.0727 41.97 42.04 42.03 42.01 42.0125 0.0728 42.01 41.97 42.04 42.04 42.015 0.0729 41.97 41.98 42.01 41.97 41.9825 0.0430 42.05 42.04 41.97 41.96 42.005 0.0931 42.04 41.97 41.98 41.98 41.9925 0.0732 41.97 42.08 42.05 42.03 42.0325 0.1133 41.98 42.02 42.06 42.02 42.02 0.0834 42.04 42.1 41.98 41.98 42.025 0.1235 41.97 41.9 41.97 41.96 41.95 0.0736 42.08 41.98 41.98 41.97 42.0025 0.1137 42.02 42.02 41.97 41.94 41.9875 0.0838 42.1 42.03 41.94 41.98 42.0125 0.1639 41.9 42.02 42.06 42.06 42.01 0.1640 41.98 41.98 41.96 42.02 41.985 0.0641 42.02 42.06 42.06 42.03 42.0425 0.0442 42.03 41.98 42.07 42.02 42.025 0.0943 42.02 42.08 42.04 41.98 42.03 0.144 41.98 41.96 41.98 42.06 41.995 0.145 42.06 42.04 41.94 41.98 42.005 0.1246 42.03 42.02 41.96 42.08 42.0225 0.1247 42.07 42.09 42.02 41.96 42.035 0.1348 42.05 41.98 42.03 42.04 42.025 0.0749 42.04 41.93 42.07 42.02 42.015 0.1450 41.96 41.92 42.1 41.98 41.99 0.1851 41.98 42.06 41.98 42.03 42.0125 0.0852 42.05 42.06 41.96 42.04 42.0275 0.153 42.04 42.02 42.05 42.01 42.03 0.0454 41.97 42.01 41.97 41.97 41.98 0.0455 41.98 42.04 42.01 41.98 42.0025 0.0656 42.04 41.97 41.97 42.05 42.0075 0.0857 41.97 41.96 42.05 42.06 42.01 0.1

S.N0 X1 X2 X3 X4 R

58 42.08 41.98 42.04 41.98 42.02 0.159 42.02 42.03 41.97 41.97 41.9975 0.0660 42.1 42.02 41.98 41.98 42.02 0.1261 41.9 41.98 42.04 41.97 41.9725 0.1462 41.98 41.96 41.97 41.92 41.9575 0.0663 42.02 41.97 42.04 42.03 42.015 0.0764 42.03 41.94 41.97 42.07 42.0025 0.1365 42.02 41.98 42.08 42.05 42.0325 0.166 41.98 42.06 42.02 42.04 42.025 0.0867 41.92 42.02 42.1 41.96 42 0.1868 42.03 42.03 41.9 41.98 41.985 0.1369 42.07 42.02 41.98 42.05 42.03 0.0970 42.05 41.98 42.02 42.04 42.0225 0.0771 42.04 42.06 42.03 41.97 42.025 0.0972 41.96 41.98 42.02 41.98 41.985 0.0673 41.98 42.08 41.98 42.04 42.02 0.174 42.05 41.96 42.06 41.97 42.01 0.175 42.04 41.98 42.03 42.08 42.0325 0.176 41.97 41.97 42.07 42.02 42.0075 0.177 41.98 41.98 42.05 42.1 42.0275 0.1278 42.04 41.92 42.04 41.9 41.975 0.1479 41.97 42.03 41.96 41.98 41.985 0.0780 42.08 42.07 41.98 42.02 42.0375 0.181 42.02 42.05 42.05 42.03 4a2.0375 0.0382 42.1 42.04 42.04 42.02 42.05 0.0883 41.9 41.96 41.97 41.98 41.9525 0.0884 41.98 41.98 41.98 42.06 42 0.0885 42.02 42.05 42.04 41.98 42.0225 0.0786 42.03 42.04 41.97 42.08 42.03 0.1187 42.02 41.97 42.08 41.96 42.0075 0.1288 41.98 41.98 41.97 42.04 41.9925 0.0789 42.06 42.04 41.96 42.02 42.02 0.190 41.98 41.97 41.98 42.09 42.005 0.1291 42.08 42.08 42.03 41.98 42.0425 0.192 41.96 42.02 42.02 41.93 41.9825 0.0993 42.04 42.1 41.98 41.92 42.01 0.1894 42.02 41.9 41.96 42.06 41.985 0.1695 42.09 41.98 41.97 42.06 42.025 0.1296 41.98 42.02 41.94 42.02 41.99 0.0897 41.93 42.03 42.08 42.01 42.0125 0.1598 41.92 42.02 41.96 42.04 41.985 0.12


S.N0 X1 X2 X3 X4 R

99 42.06 41.98 42.04 41.97 42.0125 0.09100 42.06 42.06 42.02 41.96 42.025 0.1101 41.98 42.04 42.04 42.04 42.025 0.06102 42.03 41.97 41.97 41.97 41.985 0.06103 42.02 42.08 42.08 42.08 42.065 0.06104 41.98 42.02 42.02 42.02 42.01 0.04105 41.96 42.1 42.1 42.1 42.065 0.14106 41.97 41.9 41.9 41.9 41.9175 0.07107 41.94 41.98 41.98 41.98 41.97 0.04108 41.98 42.02 42.02 42.02 42.01 0.04109 42.06 42.03 42.03 42.03 42.0375 0.03110 42.02 42.02 42.02 42.02 42.02 0111 42.03 41.98 41.98 41.98 41.9925 0.05112 42.02 42.06 42.06 42.06 42.05 0.04113 41.98 42.03 41.98 42.03 42.005 0.05114 42.06 42.07 42.08 42.07 42.07 0.02115 41.98 42.05 41.96 42.05 42.01 0.09116 42.08 42.04 42.04 42.04 42.05 0.04117 41.96 41.96 42.02 41.96 41.975 0.06118 42.04 41.98 42.09 41.98 42.0225 0.11119 42.02 42.05 41.98 42.05 42.025 0.07120 41.98 42.04 41.93 42.04 41.9975 0.11121 42.03 41.97 41.92 41.97 41.9725 0.11122 42.04 41.98 42.06 41.98 42.015 0.08123 42.01 42.04 42.06 42.04 42.0375 0.05124 41.97 41.97 41.98 41.97 41.9725 0.01125 41.98 42.08 42.02 42.08 42.04 0.1126 42.05 41.97 42.03 42.02 42.0175 0.08127 42.06 41.96 41.97 42.1 42.0225 0.14128 41.98 41.98 41.96 41.9 41.955 0.08129 41.97 42.03 41.98 41.98 41.99 0.06130 41.98 42.02 42.03 42.02 42.0125 0.05131 41.97 41.94 42.02 42.03 41.99 0.09132 41.92 42.06 41.98 42.02 41.995 0.14133 42.03 41.96 41.96 41.98 41.9825 0.07134 41.98 42.06 41.97 41.92 41.9825 0.14135 41.96 42.07 41.94 42.03 42 0.13136 41.97 42.02 41.98 42.07 42.01 0.1137 41.94 42.09 42.06 42.05 42.035 0.15138 41.98 41.98 42.04 42.04 42.01 0.06139 42.06 41.93 42.04 41.96 41.9975 0.13

S.N0 X1 X2 X3 X4 R

140 42.02 41.92 41.98 41.98 41.975 0.1141 42.03 42.06 41.94 42.05 42.02 0.12142 42.02 42.06 41.96 42.04 42.02 0.1143 41.98 42.02 42.02 41.97 41.9975 0.05144 42.06 42.01 42.03 41.98 42.02 0.08145 41.98 42.04 42.07 42.04 42.0325 0.09146 42.08 41.97 42.1 41.97 42.03 0.13147 41.96 41.96 41.98 42.08 41.995 0.12148 42.04 41.98 41.96 42.02 42 0.08149 42.02 42.03 42.05 42.1 42.05 0.08150 41.98 42.02 41.97 41.9 41.9675 0.12151 42.04 41.93 42.02 41.94 41.9825 0.11152 41.97 41.92 42.1 41.96 41.9875 0.18153 42.08 42.06 41.9 42.02 42.015 0.18154 41.97 42.06 41.98 42.03 42.01 0.09155 41.96 42.02 42.02 42.07 42.0175 0.11156 41.98 42.01 42.03 42.1 42.03 0.12157 42.03 42.04 42.02 41.98 42.0175 0.06158 42.02 41.97 41.98 41.96 41.9825 0.06159 41.98 41.96 42.06 42.05 42.0125 0.1160 41.96 41.98 41.98 41.97 41.9725 0.02161 41.97 42.03 42.08 42.01 42.0225 0.11162 41.94 42.02 41.96 41.97 41.9725 0.08163 42.08 41.98 42.04 42.05 42.0375 0.1164 41.96 41.96 42.02 42.04 41.995 0.08165 42.04 41.97 42.09 41.97 42.0175 0.12166 42.02 41.94 41.98 41.98 41.98 0.08167 42.04 41.98 41.93 42.04 41.9975 0.11168 41.97 42.06 41.92 41.97 41.98 0.14169 42.08 42.02 42.06 42.08 42.06 0.06170 42.02 42.03 42.06 42.02 42.0325 0.04171 42.1 42.02 42.02 42.1 42.06 0.08172 41.9 41.98 42.01 41.9 41.9475 0.11173 41.98 42.06 42.04 41.98 42.015 0.08174 42.02 41.98 41.97 42.02 41.9975 0.05175 42.03 42.08 41.96 42.03 42.025 0.12176 42.02 41.96 41.98 42.02 41.995 0.06177 41.98 42.04 42.03 41.98 42.0075 0.06178 41.98 42.02 42.02 42.06 42.02 0.08179 42.04 41.98 41.98 42.03 42.0075 0.06180 41.97 42.03 41.96 42.07 42.0075 0.11


S.N0 X1 X2 X3 X4 R

181 42.08 42.04 41.97 42.05 42.035 0.11182 42.02 42.01 41.94 42.04 42.0025 0.1183 42.1 41.97 41.98 41.96 42.0025 0.14184 41.9 41.98 42.06 41.98 41.98 0.16185 41.98 42.05 42.02 42.05 42.025 0.07186 42.02 42.06 42.03 42.04 42.0375 0.04187 42.03 41.98 42.02 41.97 42 0.06188 42.02 41.97 41.98 41.98 41.9875 0.05189 41.98 41.98 42.06 42.04 42.015 0.08190 42.06 41.97 41.98 41.97 41.995 0.09191 41.98 41.92 42.08 42.08 42.015 0.16192 42.08 42.03 41.96 42.02 42.0225 0.12193 41.96 42.07 41.98 42.1 42.0275 0.14194 42.04 42.05 41.97 41.9 41.99 0.15195 42.02 42.04 41.98 41.98 42.005 0.06196 42.09 41.96 41.92 42.02 41.9975 0.17197 41.98 41.98 42.08 42.03 42.0175 0.1198 41.93 42.05 42.02 42.02 42.005 0.12199 41.92 42.04 42.1 41.98 42.01 0.18200 42.06 41.97 41.9 41.92 41.9625 0.16201 41.98 42.04 41.96 42.02 42 0.08202 41.92 41.97 42.06 41.98 41.9825 0.14203 42.03 41.96 42.07 42.06 42.03 0.11204 42.07 41.98 42.02 41.98 42.0125 0.09205 42.05 42.03 42.09 42.08 42.0625 0.06206 42.04 42.02 41.98 41.96 42 0.08207 41.96 41.98 41.93 42.04 41.9775 0.11208 41.98 41.96 41.92 42.02 41.97 0.1209 42.05 41.97 42.06 41.98 42.015 0.09210 42.04 41.94 42.06 42.03 42.0175 0.12211 41.97 41.98 42.02 42.04 42.0025 0.07212 41.98 42.06 42.01 42.01 42.015 0.08213 42.04 42.04 42.04 41.97 42.0225 0.07214 41.97 42.04 41.97 41.98 41.99 0.07215 42.08 41.98 41.96 42.05 42.0175 0.12216 42.02 41.94 41.98 42.06 42 0.12217 42.1 41.96 42.03 41.98 42.0175 0.14218 41.9 42.02 42.02 41.97 41.9775 0.12219 41.98 42.03 41.98 41.98 41.9925 0.05220 42.02 42.07 41.96 41.97 42.005 0.11221 42.03 42.1 41.97 41.94 42.01 0.16

S.N0 X1 X2 X3 X4 R

222 42.02 41.98 41.94 42.06 42 0.12223 41.98 41.96 41.98 41.96 41.97 0.02224 42.06 42.05 42.06 42.06 42.0575 0.01225 41.98 41.97 42.02 42.07 42.01 0.1226 42.08 42.01 42.03 42.04 42.04 0.07227 41.96 41.97 42.02 41.98 41.9825 0.06228 42.04 42.05 41.98 41.94 42.0025 0.11229 42.02 42.04 42.06 41.96 42.02 0.1230 42.09 41.97 41.98 42.02 42.015 0.12231 41.98 41.98 42.08 42.03 42.0175 0.1232 41.93 42.04 41.96 42.07 42 0.14233 41.92 41.97 42.04 42.1 42.0075 0.18234 42.06 42.08 42.02 41.98 42.035 0.1235 42.06 42.02 41.98 41.96 42.005 0.1236 42.02 42.1 42.03 42.05 42.05 0.08237 42.01 41.9 42.04 41.97 41.98 0.14238 42.04 41.98 42.01 42.01 42.01 0.06239 41.97 42.02 41.97 41.97 41.9825 0.05240 41.96 42.03 41.98 42.05 42.005 0.09241 41.98 42.02 42.05 42.04 42.0225 0.07242 42.03 41.98 42.06 41.97 42.01 0.09243 42.02 42.06 41.98 41.98 42.01 0.08244 41.98 42.03 41.97 42.04 42.005 0.07245 41.96 42.07 41.98 41.97 41.995 0.11246 41.97 42.05 41.97 42.04 42.0075 0.08247 41.94 42.04 41.92 41.97 41.9675 0.12248 41.98 41.96 42.03 42.08 42.0125 0.12249 42.06 41.98 42.07 42.02 42.0325 0.09250 42.02 42.05 42.05 42.1 42.055 0.08251 42.03 42.04 42.04 41.9 42.0025 0.14252 42.02 41.97 41.96 41.98 41.9825 0.06253 41.98 41.98 41.98 42.02 41.99 0.04254 42.06 42.04 42.05 42.03 42.045 0.03255 41.98 41.97 42.04 42.02 42.0025 0.07256 42.08 42.08 41.97 41.98 42.0275 0.11257 41.96 42.02 41.98 42.06 42.005 0.1258 41.98 42.1 42.04 42.03 42.0375 0.12259 41.97 41.9 41.97 42.07 41.9775 0.17260 41.98 41.98 42.08 42.05 42.0225 0.1261 41.92 42.02 42.02 42.04 42 0.12262 42.03 42.03 42.1 41.96 42.03 0.14


S.N0 X1 X2 X3 X4 R

263 42.07 42.02 41.9 41.98 41.9925 0.17264 42.05 41.98 41.98 42.05 42.015 0.07265 42.04 41.92 42.02 42.04 42.005 0.12266 41.96 42.03 42.03 41.97 41.9975 0.07267 41.98 42.07 42.02 41.98 42.0125 0.09268 42.05 42.05 41.98 42.04 42.03 0.07269 42.04 42.04 42.06 41.97 42.0275 0.09270 41.97 41.96 41.98 42.08 41.9975 0.12271 41.98 41.98 42.08 41.97 42.0025 0.11272 42.04 42.05 41.96 41.96 42.0025 0.09273 41.97 42.04 42.04 41.98 42.0075 0.07274 42.08 41.97 42.02 42.03 42.025 0.11275 42.02 41.98 42.09 42.02 42.0275 0.11276 42.1 42.04 41.98 41.98 42.025 0.12277 41.9 41.97 41.93 41.96 41.94 0.07278 41.98 42.08 41.92 41.97 41.9875 0.16279 42.02 42.02 42.06 41.94 42.01 0.12280 42.03 42.1 42.06 42.08 42.0675 0.07281 42.02 41.9 42.02 41.96 41.975 0.12282 41.98 41.98 42.01 42.04 42.0025 0.06283 42.06 42.02 42.04 42.02 42.035 0.04284 42.04 42.03 41.97 42.04 42.02 0.07285 41.97 42.02 41.96 41.97 41.98 0.06286 42.08 41.98 42.04 42.08 42.045 0.1287 42.02 42.06 41.97 42.02 42.0175 0.09288 42.1 41.98 42.08 42.1 42.065 0.12289 41.9 42.08 42.02 41.9 41.975 0.18290 41.98 41.96 42.1 41.98 42.005 0.14291 42.02 42.04 41.9 42.02 41.995 0.14292 42.03 42.02 41.98 42.03 42.015 0.05293 42.02 42.09 42.02 42.02 42.0375 0.07294 41.98 41.98 42.03 41.98 41.9925 0.05295 42.06 41.93 42.02 42.06 42.0175 0.13296 42.03 41.92 41.98 41.98 41.9775 0.11297 42.07 42.06 42.06 42.08 42.0675 0.02298 42.05 42.06 42.03 41.96 42.025 0.1299 42.04 41.98 42.07 42.04 42.0325 0.09300 41.96 42.03 42.05 42.02 42.015 0.09301 41.98 42.02 42.04 42.09 42.0325 0.11302 42.05 41.98 41.96 41.98 41.9925 0.09303 42.04 41.96 41.98 41.93 41.9775 0.11

S.N0 X1 X2 X3 X4 R

304 41.97 41.97 42.05 41.92 41.9775 0.13305 41.98 41.94 42.04 42.06 42.005 0.12306 42.04 41.98 41.97 42.06 42.0125 0.09307 41.97 42.06 41.98 41.98 41.9975 0.09308 42.08 42.02 42.04 42.02 42.04 0.06309 41.97 42.03 41.97 42.03 42 0.06310 41.96 42.02 42.08 41.97 42.0075 0.12311 41.98 41.98 42.02 41.96 41.985 0.06312 42.03 42.06 42.1 41.98 42.0425 0.12313 42.02 41.98 41.9 42.03 41.9825 0.13314 41.94 42.08 41.98 42.02 42.005 0.14315 42.06 41.96 42.02 41.98 42.005 0.1316 41.96 42.04 42.03 41.96 41.9975 0.08317 42.06 42.02 42.02 41.97 42.0175 0.09318 42.07 41.98 41.98 41.94 41.9925 0.13319 42.02 42.03 41.92 41.98 41.9875 0.11320 42.09 42.04 42.03 42.06 42.055 0.06321 41.98 42.01 42.07 42.04 42.025 0.09322 41.93 41.97 42.05 42.04 41.9975 0.12323 41.92 41.98 42.04 41.98 41.98 0.12324 42.06 42.05 41.96 41.94 42.0025 0.12325 42.06 42.06 41.98 41.96 42.015 0.1326 42.02 41.98 42.05 42.02 42.0175 0.07327 42.01 41.97 42.04 42.03 42.0125 0.07328 42.04 41.98 41.97 42.07 42.015 0.1329 41.97 41.97 41.98 42.1 42.005 0.13330 41.96 41.92 42.04 41.98 41.975 0.12331 41.98 42.03 41.97 41.96 41.985 0.07332 42.03 41.98 42.08 42.05 42.035 0.1333 42.02 41.96 42.02 41.97 41.9925 0.06334 41.93 41.97 42.1 42.02 42.005 0.17335 41.92 41.94 41.9 42.1 41.965 0.2336 42.06 41.98 41.94 41.9 41.97 0.16337 42.06 42.06 41.96 41.98 42.015 0.1338 42.02 42.02 42.02 42.02 42.02 0339 42.01 42.03 42.03 42.03 42.025 0.02340 42.04 42.02 42.07 42.02 42.0375 0.05341 41.97 41.98 42.1 41.98 42.0075 0.13342 41.96 42.06 41.98 42.06 42.015 0.1343 41.98 41.98 41.96 41.98 41.975 0.02344 42.03 42.08 42.05 42.08 42.06 0.05


S.N0 X1 X2 X3 X4 R

345 42.02 41.96 41.97 41.96 41.9775 0.06346 41.98 42.04 42.01 42.04 42.0175 0.06347 41.96 42.02 41.97 42.02 41.9925 0.06348 41.97 41.98 42.05 42.09 42.0225 0.12349 41.94 42.04 42.04 41.98 42 0.1350 41.98 41.97 41.97 41.93 41.9625 0.05351 42.06 42.08 41.98 41.92 42.01 0.16352 42.02 41.97 42.04 42.06 42.0225 0.09353 42.03 41.96 41.97 42.06 42.005 0.1354 42.02 41.98 42.08 42.02 42.025 0.1355 41.98 42.03 42.02 42.01 42.01 0.05356 42.06 42.02 42.1 42.04 42.055 0.08357 41.98 41.98 41.9 41.97 41.9575 0.08358 42.08 41.96 41.98 41.96 41.995 0.12359 41.96 41.97 42.02 41.98 41.9825 0.06360 42.04 41.94 42.03 42.03 42.01 0.1361 42.02 42.08 42.02 42.02 42.035 0.06362 41.98 41.96 41.98 41.98 41.975 0.02363 42.03 42.04 42.06 41.96 42.0225 0.1364 42.04 42.02 42.03 41.97 42.015 0.07365 42.01 42.04 42.07 41.94 42.015 0.13366 41.97 41.97 42.05 41.98 41.9925 0.08367 41.98 42.08 42.04 42.06 42.04 0.1368 42.05 42.02 41.96 42.02 42.0125 0.09369 42.06 42.1 41.98 42.03 42.0425 0.12370 41.98 41.9 42.05 42.02 41.9875 0.15371 41.97 41.98 42.04 41.98 41.9925 0.07372 41.98 42.02 41.97 42.06 42.0075 0.09

S.N0 X1 X2 X3 X4 R

373 41.97 42.03 41.98 41.98 41.99 0.06374 41.92 42.02 42.04 42.08 42.015 0.16375 42.03 41.98 41.97 41.96 41.985 0.07376 42.07 41.98 42.08 41.98 42.0275 0.1377 42.05 42.04 42.02 41.97 42.02 0.08378 42.02 41.97 42.1 42.02 42.0275 0.13379 42.09 42.08 41.9 41.98 42.0125 0.19380 41.98 42.02 41.98 42.06 42.01 0.08381 41.93 42.1 42.02 41.98 42.0075 0.17382 41.92 41.9 42.03 42.08 41.9825 0.18383 42.06 41.98 42.02 41.96 42.005 0.1384 42.06 42.02 42.1 41.98 42.04 0.12385 42.02 42.03 41.9 41.97 41.98 0.13386 42.01 42.02 41.98 41.98 41.9975 0.04387 42.04 41.98 42.02 41.92 41.99 0.12388 41.97 42.06 42.03 42.03 42.0225 0.09389 41.96 41.98 42.02 42.07 42.0075 0.11390 41.98 42.05 41.98 42.05 42.015 0.07391 42.03 42.04 42.06 42.04 42.0425 0.03392 42.02 41.97 42.04 41.96 41.9975 0.08393 41.98 41.98 41.97 41.98 41.9775 0.01394 41.96 42.04 42.08 42.05 42.0325 0.12395 41.97 41.97 42.02 42.04 42 0.07396 41.94 42.08 42.1 41.97 42.0225 0.16397 41.98 42.02 41.9 41.98 41.97 0.12398 42.06 42.1 41.98 42.04 42.045 0.12399 42.02 41.9 42.02 41.97 41.9775 0.12400 42.03 41.98 42.03 42.08 42.03 0.1

Total 16803.3 29.12


Cloud Computing: An Emerging Innovative Business Model of IT Services for Sustainable Competitive Advantage

* Assistant Professor Dept. of Management Studies, Pondicherry University. Karaihal 609605. E-mail: [email protected]

Information technology has become care of address for innovations and the industry has always been faster compared to other industries. Because of these innovations, the industry has witnessed a series of transformations over the last 50 years. Technology transformations started with mainframe computers, then moved on to minicomputers, PCs and the web. The next wave of transformation in IT industry has been the cloud computing. The cloud computing has reached an ��K��and changing business models. Organisations that recognise and are able to effectively manage risks around cloud will create a sustainable competitive advantage.

The purpose of the present paper is to review the existing �� !�� subsequent sections of the paper fundamental aspects of cloud computing and their relevance to business application for achieving sustainable competitive advantage are discussed. The market size and growth potential of cloud computing throughout the world, in general, and in India, in particular, are reviewed. Finally, major players in the market and their contribution are presented elaborately followed by constructive conclusion.

Key Words: Cloud computing, business model, sustainable competitive advantage, deployment models, service delivery models.

The rise of the cloud is more than just another platform shift that gets geeks excited. It will undoubtedly transform the IT industry, but it will also profoundly change the way people work and companies operate.

- The Economist, ‘Let it Rise’, October 2008

Dr. Madhavaiah *

Introduction�� ;�� is the achievement and maintenance of a sustainable competitive advantage. Indeed, understanding which �� competitive advantage is considered to be the fundamental issue in business strategy (Varadarajan and Jayachandran, 1999). A competitive advantage can result either from implementing a value-creating strategy not simultaneously being employed by current or prospective competitors or through superior execution of the same strategy as competitors (Bharadwaj, Varadarajan, and Fahy, 1993). The competitive � �� '�� \��>]^^]`�Because of its importance to the long-term success of ��>�� '��>some innovations or business models which will enable them to achieve sustainable competitive advantage. Cloud computing is such an innovative business model of information technology (IT) services which is being adopted by very few businesses around the world and have achieved competitive advantage.

[�� '��trend, and many experts expect that cloud computing will reshape IT processes and the IT marketplace. With the cloud computing technology, users use a variety of devices, including PCs, laptops, smartphones, and PDAs to access programs, storage, and application-development platforms over the Internet, via services offered by cloud computing providers.

Although not new as a concept, cloud computing is new in its generalized application to all IT services and is the next step in the relentless journey of corporate information technology. Given its profound impact,


cloud computing has become a source of sustainable competitive advantage to the companies globally and its conceptual development, global and Indian market scenario and future growth potential have become the main focuses of the present paper.

The purpose of the present paper is to review the �?�� '�� '��'��'�� $� ��subsequent sections of the paper fundamental aspects of cloud computing and their relevance to business application for achieving sustainable competitive advantage are discussed. The market size and growth potential of cloud computing around the world, in general, and in India, in particular, are reviewed. Finally, major players in the market and their contribution are presented elaborately followed by constructive conclusion.

*' _�� Cloud computing is a relatively recent term even though it was built upon some existing concepts. Number of computing researchers and practitioners �� '��>�� of the term. In 1997, Ramnath K. Chellappa established �� '��> �� as: “a computing paradigm where the boundaries of computing will be determined by economic rationale rather than technical limits”. Still there is a debate �� '�� '�� '�� '�� different academic and industry sources are given in Table 1. �� '��from different perspectives but none of them provides an adequate view of this new platform except the ��et al.,\}�]]`�$� ��cloud computing, Marston et al., (2011) have tried to ��'�� '��business perspective as well as its unique features from a technological perspective.

�� > �� '�� major characteristics:

� Provision of scalable resources on demand: Users can order and use as much resources as is necessary on demand. These resources are dynamically scalable. As demand from users grows and shrinks, the necessary computer, storage and network capacity can be adjusted on an hourly basis.

� Virtualization: Cloud computing stores resources in virtual machines through the Internet platform. This increases the accessibility and allows users request and use those resources at a timely manner. It also enables the interconnectivity between users and service providers.

� Maintenance and management free: Resources are stored in the cloud virtual servers and will maintain and manage themselves. The vendor will take care all of these responsibilities and users will be free from these time and cost consuming tasks.

*'\ �� _��

��]� ��Table 1>��'��'�� computing which is more comprehensive and relevant in both business and technical perspectives and is given below:

“Cloud computing is an information technology deployed business model, provided as a service over the Internet, where both hardware and software computing services are delivered on-demand to customers in a self-service fashion, independent of device and location in which high levels of quality service, dynamically scalable, rapidly provisioned, shared, virtualized and released with minimal service provider interaction.”

��]~Z��[�� [��'��

S. No. Reference Z��

1 Gartner A style of computing where massively scalable IT-related capabilities are provided as a service across the Internet to multiple external customer.



2 Forrester A pool of abstracted, highly scalable, and managed infrastructure capable of hosting end-customer applications and billed by consumption.

3 Wikipedia A style of computing in which dynamically scalable and often virtualized resources are provided as a service over the Internet

4 UC Berkeley

�� computing resources available on demand, the elimination of up-front commitments by cloud users, and the ability to pay for use of computing resources on a short-term basis as needed.

5 IBM A cloud computing platform dynamically provisions, ��> ��>and deprovisions servers as needed. Cloud applications use large data centers and powerful servers that host web applications and web services.

6 Chellappa (1997)

A computing paradigm where the boundaries of computing will be determined by economic rationale rather than technical limits.

7 Hammond (2008)

The ability to connect to software and data on the Internet (the cloud) instead of on your hard drive or local network.

8 Hartig (2008)

Virtualization of resources that maintains and manages itself.


9 Horrigan (2008)

Cloud computing is an emerging computing paradigm where data and applications reside in the cyberspace, it allowing users to access their data and information through any web-connected device be ��?� ��

10 Vouk (2008)

Cloud computing embraces cyber-infrastructure, and builds upon virtualization, distributed computing, grid computing, utility computing, networking, and web and software services.

11 Buyya et al., (2009)

A type of parallel and distributed system consisting of a collection of inter-connected and virtualized computers that are dynamically provisioned and presented as �� '��resources based on service-level agreements established through negotiation between the service provider and consumers.

12 Educause (2009)

The delivery of scalable IT resources over the Internet. Those resources can include applications and services, as well as the infrastructure on which they operate.

13 Scale (2009)

The sharing and use of applications and resources of a network environment to get work done without concern about ownership and management of the network’s resources and applications.



14 Vaquero et al. (2009)

A large pool of easily usable and accessible virtualized resources (such as hardware, development platforms and/or services) which can be �� adjust to a variable load (scale), allowing for an optimum resource utilization.

15 Armbrust, et al., (2010),

Cloud computing refers to both the applications delivered as services over the Internet and the hardware and systems software in the data centers that provide those services.

16 Marks and Lozano (2010)

Cloud computing is on-demand access to virtualized IT resources that are housed outside of your own data center, shared by others, simple to use, paid for via subscription, and accessed over the Web.

17 Mell and Grance (2009), NIST

Cloud computing is a model for enabling convenient, on-demand network access to a �� '�� computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. This cloud model promotes availability and is composed of �� >three delivery models, and four deployment models.


18 Marston et al., (2011)

Cloud computing is an information technology service model where computing services (both hardware and software) are delivered on-demand to customers over a network in a self-service fashion, independent of device and location. The resources required to provide the requisite quality-of-service levels are shared, dynamically scalable, rapidly provisioned, virtualized and released with minimal service provider interaction. Users pay for the service as an operating expense without ��capital expenditure, with the cloud services employing a metering system that divides the computing resource in appropriate blocks.

3. Evolution of Cloud ComputingCloud computing can be seen as an innovation in different ways. From a technological perspective it is an advancement of computing, for which history can be traced back to the construction of the calculating machine in the early 17th century. But, the evolution has �� > ��> �� ? '�� Figure 1, shows six phases of the evolution of cloud computing, from dummy terminals/mainframes, to PCs, networking computing, to grid and cloud computing.

Phase 1: many users shared powerful mainframes using dummy terminals.

Phase 2: stand-alone PCs became powerful enough to meet the majority of users’ needs.

Phase 3: PCs, laptops, and servers were connected


together through local networks to share resources and increase performance.

Phase 4: local networks were connected to other local networks forming a global network such as the Internet to utilize remote applications and resources.

Phase 5: grid computing provided shared computing power and storage through a distributed computing system.

Phase 6: cloud computing further provides shared resources on the Internet in a scalable and simple way.

Comparing these six phases, it looks like that cloud computing is a return to the original mainframe computing paradigm. However, these two paradigms have several important differences. Mainframe

��'�� '�� '��> �� '�� '�� '�� capacity. In addition, in mainframe computing dummy terminals acted as user interface devices, while in cloud computing powerful PCs can provide local computing power and cashing support.

4. Layers and Types of Cloud Computing

Cloud computing services are divided into three classes, according to the abstraction level of the capability provided and the service model of providers. They are:

1. Infrastructure as a Service (IaaS)2. Platform as a Service (PaaS)3. Software as a Service (SaaS).


Figure 2 depicts the layered organization of the cloud stack from physical infrastructure to applications.

Service Class

IaaS PaaS SaaS

Main Access and Management Tool

Virtual InfrastructureManager

Cloud DevelopmentEnvironment

Web Browser

Service Content Cloud InfrastructureCompute Servers, Data Storage, Firewall, Load Balancer

Cloud PlatformProgramming languages, Frameworks,Mashups editors, Structured data

Cloud Applications��>��suites, CRM,Video processing

Figure 2: Layers of Cloud Computing

These abstraction levels can also be viewed as a layered architecture where services of a higher layer can be composed from services of the underlying layer (Youseff et al., 2008). The reference model of Buyya et al., (2009) explains the role of each layer in an integrated architecture. A core middleware manages physical resources and the VMs deployed on top of them; in addition, it provides the required features (e.g., accounting and billing) to offer multi-tenant pay-as-you-go services. Cloud development environments are built on top of infrastructure services to offer application development and deployment capabilities; in this level, various programming models, libraries, APIs, and mashup editors enable the creation of a �� > ¦��> �� ''��Once deployed in the cloud, these applications can be consumed by end users.

(1) Infrastructure as a Service (IaaS)

Providing virtualized resources like computation, storage, communication, etc., on demand is known as Infrastructure as a Service (IaaS) (Sotomayor, 2009). A cloud infrastructure enables on-demand provisioning of servers running several choices of operating systems and a customized software stack. Infrastructure services are considered to be the bottom layer of cloud computing systems.

Examples: Amazon Web Services

(2) Platform as a Service

In addition to infrastructure-oriented clouds that provide raw computing and storage services, another approach is to offer a higher level of abstraction to make a cloud easily programmable. This is called Platform as a Service (PaaS). A cloud platform offers an environment on which developers create and deploy applications and do not necessarily need to know how many processors or how much memory that applications will be using. In addition, multiple programming models and specialized services like data access, authentication, payments, etc., are offered as building blocks to new applications (Appistry Inc., 2009).

Example: Google AppEngine

(3) Software as a Service

Applications reside on the top of the cloud stack. Services provided by this layer can be accessed by end users through Web portals. Therefore, consumers are increasingly shifting from locally installed computer programs to on-line software services that offer the same functionally. Traditional desktop applications such as word processing and spreadsheet can now be accessed as a service in the Web. This model of delivering applications, known as Software as a Service (SaaS), alleviates the burden of software maintenance �� '�� '�� for providers (Youseff et al., 2008; Hayes, 2008).

Example: Salesforce.com,


5. Deployment Models of Cloud Computing

Although cloud computing has emerged mainly from the appearance of public computing utilities, other deployment models, with variations in physical location and distribution, have been adopted. In this sense, regardless of its service class, a cloud can be �� '��> '��> �� >based on model of deployment.

5.1 Public cloud

A public cloud refers to a cloud service delivery model in which a service provider makes massively scalable IT resources, such as CPU and storage capacities, or software applications, available to the general public over the Internet. Public cloud services are typically offered on a usage-based model. Public cloud is the �� '�� IT industry’s vocabulary. The concept of public clouds has clearly demonstrated the long-term potential of the cloud computing model.

There are many public cloud service providers in the market today, which are offering services ranging from infrastructure-as-as-service, to development-platform-as-a-service, to special purpose application-as-a-services.

While the public cloud offers a clean, infrastructure-less model for end users to consume IT services, and intrigues the research community with its disruptive nature, migrating the majority of today’s IT services, such as the various business applications in an enterprise environment (e.g. insurance applications, health care administration, bank customer account management, the list goes on and on), to a public cloud model is not feasible. Data security, corporate governance, regulatory compliance, and performance and reliability concerns prohibit such IT applications to be moved out of the “controlled domains” (i.e. ��'��`>��'�� infrastructure, government regulation, and public acceptation continue to improve.

5.2 Private cloud

Private cloud, in contrast, represents a deployment model where enterprises (typically large corporations with multi-location presence) offer cloud services over the corporate network (can be a virtual private network) �� ;'�� environment. Recent advances in virtualization and data center consolidation have allowed corporate network and datacenter administrators to effectively become service providers that meet the needs of their customers within these corporations. Private clouds ��'��pooling” concept associated with cloud computing and their very own size, yet in the mean time addressing the concerns on data security, corporate governance, government regulation, performance, and reliability issues associated with public clouds today.

5.3 Hybrid Cloud

While public and private clouds represent the two ends of the cloud computing spectrum in terms of ownership �� ´�� acceptance in accordance to the services offered and �� ´ � �� '��model of cloud computing, the hybrid cloud model that blends the characteristics of public and private clouds, is emerging.

A hybrid cloud is a deployment model for cloud services where an organization provides cloud services and manages some supporting resources in-house and has others provided externally. Architecturally, a hybrid cloud can be considered a private cloud extending its boundary into a third party cloud environment (e.g. a public cloud) to obtain additional (or non-mission critical) resources in a secure and on-demand manner.

5.4 Community cloud

The cloud infrastructure is shared by several ��=�� ''�� '�� that has shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be managed by the organizations or a third party and may exist on premise or off premise.


6. Essential Characteristics of Cloud Computing

Cloud computing platforms possess characteristics of both Clusters and Grids, with its own special attributes and capabilities such strong support for virtualization, dynamically composable services with Web Service interfaces, and strong support for creating 3rd party, value added services by building on Cloud compute, storage, and application services. Thus, Clouds are promising to provide services to users without reference to the infrastructure on which these are hosted.

On-demand self-service: A consumer can unilaterally receive computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with each service provider.

Broad network access: Capabilities are available over the network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: The service provider’s computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to consumer demand. There is a sense of location independence in that the customer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter). Examples of resources include storage, processing, memory, network bandwidth, and virtual machines.

Rapid elasticity: Capabilities can be rapidly and elastically provided, in some cases automatically, to quickly scale out, and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time.

Measured Service: Cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts).

Resource usage can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service.

�' #�� W�� Computing

�� '��'��of the size and scale of the operations of a business enterprise. They are as follows:

��

1. Cloud computing dramatically lowers the cost �� from compute-intensive business analytics that were hitherto available only to the largest of corporations.

2. Cloud computing also represents a huge opportunity to many third-world countries that have been so far left behind in the IT revolution.

3. It can provide an almost immediate access to hardware resources, with no upfront capital investments for users, leading to a faster time to market in many businesses.

4. Cloud computing can lower IT barriers to innovation, as can be witnessed from the many promising startups, from the ubiquitous online applications such as Facebook and Youtube to the more focused applications like TripIt (for managing travel) or Mint (for managing personal ��`�

5. Cloud computing makes it easier for enterprises �� ´ �� ´ �� client demand.

6. Since the computing resources are managed through software, they can be deployed very fast as new requirements arise. In fact, the goal of cloud computing is to scale resources up or down dynamically through software APIs depending on client load with minimal service provider interaction.


7. Cloud computing also makes possible new classes of applications and delivers services that were not possible before. Examples:

� Mobile interactive applications that are location-, environment- and context-aware and that respond in real time to information provided by human users, nonhuman sensors (e.g. humidity and stress sensors within a shipping container) or even from independent information services (e.g. worldwide weather data);

� Business analytics that can use the vast amount of computer resources to understand customers, buying habits, supply chains and so on from voluminous amounts of data; and

Limitations

There are other cases when cloud computing is not the best solution for company computing needs. This section looks at why certain applications are not the best to be deployed on the cloud.

� In a public cloud, the client does not have the control over security of company’s own data. Trust is the major disadvantage of cloud computing because data is sensitive, because once data leaves the hands of the company and lands in the lap of a service provider, company will lose a layer of control and there may be a threat to the data from hackers and internal breaches. And other providers may, by way of their agreement with the company, be allowed to access and catalog the information and use it in ways that the company never intended.

� If there is a problem in data center, all virtual machines are affected. There might or might not be a backup of the data if an enterprise relies only on the cloud for its data management needs.

� �� '��>��has some hidden or additional costs as well. Clients are charged extra for data transfer or other services. Initial offerings are priced higher, till economies of scale work out for the service provider. It may be

�� \��'��` ��amounts of data from the provider.

� The concept assumes that the client has reliable network connection. If there are problems of network connectivity, accessing the cloud also becomes a problem.

� Peripheral devices like printers or scanners might not work with cloud. Many of them require software to be installed locally. Networked peripherals have lesser problems. Integrating internal applications with those on cloud can be complex and in some cases not viable.

� Public cloud offerings are very generic and offer multi-tenancy service which all organizations might not be comfortable with. Implementing an in-house cloud is more complex to implement and are burdensome on internal resources if the organization is not large enough.

Cloud service providers are continuously evolving solutions to overcome the above mentioned hurdles. �� '�� to the cloud and are adopting it unconditionally and are achieving sustainable competitive advantage, while some enterprises are moving non-critical applications. Some others want to wait and watch how the �� disadvantages may differ, depending on the provider.

8. Global Cloud Computing MarketCloud computing is a fast growing market through out the world. It is expected to grow at a double-digit rate in the next 5 years. According to the MarketsAndMarkets report, the global cloud computing market is expected to grow from $ 37.8 billion in 2010 to $ 121.1 billion in 2015 at a CAGR of 26.2% from 2010 to 2015. Gartner Research expects cloud computing to be a $150 billion business by 2014, and according to AMI partners, small and medium businesses are expected to spend over $100 billion on cloud computing by 2014.

Software as a Service (SaaS), a component of cloud computing, is the largest contributor in the cloud computing services market, accounting for 73% of


the market’s revenues in 2010. Experts like Steve Balmer believe that emerging countries such as India have the greatest potential for market growth, including opportunities to support outsourcing of Cloud services. According ��$Z[��'��>��}�]}>��;�� composition and delivery; by 2014, about 65% of new products from established ISVs will be delivered as SaaS services. SaaS-derived revenue will account for nearly 26% of net new growth in the software market in 2014.

9. Major Players in Global Cloud Computing MarketCloud computing has changed the face of IT industry around the world, almost beyond recognition, because of the emergence of number of players every year. Cloud computing Journal has published the list of 150 cloud computing companies operating globally in their 2008 issue. The major players in the global cloud computing market and their contribution are shown in Table 2, Table 3 and Figure 3.

Table 2: Major Players in Cloud Computing Market and their Contribution

S. No. IT Company Contribution for the Growth of Cloud Computing Market

1 AT&T � AT&T provides two cloud services: Synaptic Hosting and Synaptic Storage.� Through Synaptic Hosting, client companies will be able to store Windows server,

Linux client server applications and web applications on AT&T’s cloud.� Synaptic Storage enables clients to store their data on AT&T’s cloud.

2 Google � Google provides cloud computing services with the name App Engine� Google’s App Engine offers client organizations access to Google’s cloud-based

platform that provide tools to build and host web applications.� $��'��¢��''�>��'��

including e-mail, calendaring, word processing and a simple Web site creation tool.� Its recent acquisition of Postini, which offers a set of e-mail and Web security services,

makes it a credible player in the area of electronic corporate communications.

3 IBM � IBM provides cloud computing services with the name Blue Cloud� Blue Cloud offers companies access to tools that allow them to manage large scale

applications and database via IBM’s Cloud.� The company offers consulting services to help companies integrate their

infrastructure into the cloud.� Recent partnership with Google to work with several universities in order to promote

new software development methods which will help students and researchers address the challenges of the cloud applications of the future.

4 Mocrosoft � Microsoft provides cloud computing services with the name Windows Azure, the “cloud operating system” PaaS (early 2010).

� Additionally company has created the Azure Services Platform to run on the Windows Azure operating systems giving client organizations access to several online Microsoft services like Live, .Net, SQL, SharePoint, and Microsoft’s Dynamic CRM.

Source: Marston, S., Li, Zhi, Bandyopadhyay, S, Zhang, J., Ghalsasi, Anand (2011), “Cloud Computing - The Business Perspective”, Decision Support Systems, Vol. 51, No. 1, pp. 176-189.


Table 3: Key Cloud Computing Technology Providers, Innovators and Enablers

S. No. Company Contribution for the Growth of Cloud Computing Market

KEY TECHNOLOGY PROVIDERS

1 Apache � Apache’s Hadoop is an open-source software framework that has inspired the development of database and programming tools for cloud computing.

2 Cisco � Though Cisco has lately in the cloud computing space, it is actively working on a set of standards that will allow portability across providers.

� �� '�� '�� autonomous system to another, which includes the consistent execution of the workload on the new system.

3 EMC � %�[ �� '�� '�� '�� ´ �� virtualization software

� Recently it acquired VMWare).� Recently started offering specialized storage solutions for cloud applications.� Recently introduced their vCloud initiative, which allows client organizations to

run their in-house applications on a cloud and be interoperable with other cloud services from other providers that run within the vCloud ecosystem.


THE INNOVATORS

1 Amazon � Amazon is offering two services namely: Amazon Web Services, a suite of several services which include the Elastic Compute Cloud (EC2), for computing capacity, and the Simple Storage Service (S3), for on-demand storage capacity.

� In addition to these core offerings, Amazon offers the SimpleDB (a database Web service), the CloudFront (a Web service for content delivery) and the Simple Queue Service (a hosted service for storing messages as they travel between nodes).

� Like IBM and Google, Amazon is working with universities, by giving access to their large databases and their engineers teaching classes on web-scale development.

2 Enomaly � Enomaly is offering cloud service called Elastic Computing Platform (ECP) which integrates enterprise data centers with commercial cloud computing offerings.

� It enables IT professionals manage and govern both internal and external resources from a single console, while making it easy to move virtual machines from one data center to another.

3 Salesforce.com � ��;�� ''�� Recently the company has introduced Force.com, an integrated set of tools

and application services that independent software vendors and corporate IT departments can use to build any business application and run it on the same infrastructure that delivers the Salesforce CRM applications.

� More than 100,000 business applications already run on the Force.com platform.� It includes the company’s Apex programming language.

THE ENABLERS

1 CapGemini � [�'��X��'��'��'��'��Google Apps Premier Edition (GAPE) for enterprises.

� It uses Google’s SaaS initiative to target opportunities among large enterprises.

2 RightScale � RightScale offers a SaaS platform called RightScale Platform, that helps customers manage the IT processes they have outsourced to cloud providers.

� It deploys new virtual servers and applications, performs load balancing in response to changing needs, automates storage backups, and offers monitoring and error reporting.

Source~�� >��>"�>»��>�� '� ��>�>»��>��>¢��>�� \}�]]`>�[�� [��'��;The Business Perspective”, Decision Support Systems, Vol. 51, No. 1, pp. 176-189.

10. Cloud Computing Market in IndiaThe Indian cloud computing market which stands at $110 million in 2010 is expected to grow almost ten times ��¼]��}�]�>�� >»��Consulting. The study titled “Cloud Computing in India: Opportunities and Way Forward” highlighted that in the Indian cloud computing market, Software-as-a-Service has witnessed the most rapid uptake until now. As components of the overall cloud market, Software-as-a-service (SaaS) in India is likely to reach a mark of $650


million by 2015, while Platform-as-a-service (PaaS) and Infrastructure-as-a-service (IaaS) markets cumulatively would touch $434 million each by then. The domestic market for SaaS is estimated to be about US$ 66 million and is currently dominated by Collaborative Applications, CRM, ERP & Email workloads.

There are several multinational and Indian companies entering the cloud space and trying to drive business relevance of its solutions for the Indian customers. The reasons for the growth of Cloud computing market in India are:

� IT spending in India is one of the highest and fastest in the world.

� India being the world’s fastest growing mobile market with over 20 million subscribers added every month and the money that companies have invested for 3G services showcases the belief that the large telecom providers have on-data services in the Indian market.

� Over 500 million people form the middle class in India, and the products and services consumed by them are relevant to other emerging markets.

� Indian SMBs particularly, lack budgets, want business improvement, lack management bandwidth required to manage internal IT and are looking for rapid growth in the next few years for whom cloud computing is an opportunity.

� The highest spending on IT as a percentage of revenues is to be found in IT-BPO and Retail sectors.

� Telecom and Banking, Financial Services and Insurance (BFSI) are highly mature with their IT adoption and show higher potential towards cloud adoption going forward.

� In an effort to meet global standards, verticals such as Education, Healthcare, and Government show massive potential for cloud adoption in the immediate future.

Major players in the Indian cloud computing market are shown in the Table 4.

Table 4: Indian Companies in Cloud Computing Market

S. No. Company Service Location Remarks

1 AppPoint ½ AppsOnAzure - PaaS

Bangalore Cloud based application infrastructure using Microsoft Azure as the platform.

2 Clogeny ½ Cloud Enabler Pune Cloud related services such as: Migration, Deployment, Planning, Consulting

3 CtrlS ½ CtrlS Cloud - IaaS Hyderabad On-Demand Private Cloud.½ 99.995% uptime½ Tier 4 data centre

4 EazeWork ½ EazeHR - SaaS½ EazePayroll - SaaS½ EazeSales - SaaS

Noida Cloud SaaS for SMEs/SMBs.

5 NetMagic Solutions

½ Cloud 2.0½ CloudNet½ CloudServe½ PrivateCloud

Mumbai A front runner in the Indian IaaS space.


6 OrangeScape ½ OrangeScape Studio - PaaS

Chennai USP - Visual PaaS.½ OrangeScape Launches into US Market

with Persistent Systems Partnership

7 Ozonetel Systems ½ §��§��´��½ CTS - SaaS

Hyderabad $� $� �� ;��advantage in cloud telephony services (CTS)

8 PK4 Software ½ Impel CRM - SaaS Bangalore ©��´�non-western CRM for India

9 Ramco ½ Ramco OnDemand - SaaS

Chennai An early mover in SaaS. An ERP on the cloud.

10 Remindo ½ Remindo - SaaS Mumbai ---

11 Synage ½ DeskAway - SaaS Mumbai Cloud based project management.

12 Tata Communications

½ InstaCompute - IaaS½ $��;��

Mumbai Data Centers located at Hyderabad, Singapore$��'�� GoogleApps

13 TCS ½ iON - ITaaS Mumbai Covers the entire spectrum of business processes for SMBs. Domains: Manufacturing, Welness, Retail, Education

14 Wolf Frameworks ½ Wolf PaaS Bangalore Cloud PaaS with 99.97% SLA.

Source: India Based Cloud Computing Companies to Watch in 2011/12 accessed from http://www.techno-pulse.com/2011/05/india-based-cloud-computing-companies.html on 4-07-2011.

ConclusionConsidering the historic development of providing IT resources, cloud computing has been established as �� ?�� providing IT services thereby achieving sustainable competitive advantage. It can be seen as the consequent evolution of the traditional on-premise computing spanning outsourcing stages from total to the selective, and from the multi-vendor outsourcing to an asset-free delivery. While from a technical perspective, cloud computing seems to pose manageable challenges, it rather incorporates a number of challenges on a business level, both from an operational as well as from a strategic point of view.

Building a sustainable competitive advantage requires breakthrough teams. Interestingly, the Cloud Computing Business Model offers fresh insights for building new service and relationship competencies. Ultimately, to build distinctive competencies, a

competitive strategy must be integrated with strategic opportunities available as Cloud Computing Services. This is an immediate need for Indian companies of all sectors.

Cloud is opening up new windows of opportunities for Indian companies both from global as well as domestic opportunity stand point. Having realized the immense potential of cloud, it is essential for companies to come together and enable collaborative innovation to address both India and global market needs in order to attain sustainable competitive advantage. The key focus should now be on developing the ecosystem. It should include developing talent for cloud development, ��'�� '�� >�� '�� friendly. In the near future, this ecosystem together with Indian capabilities of exporting business models will provide the right mix to leverage opportunities created by cloud.


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Make to Order Manufacturing in Indian Context: A Case Based Study

* Professor of Operations Management Indian Institute of Management, Lucknow Sitapur Road, Lucknow 226013 E-mail: [email protected], [email protected]

** PGDM, Indian Institute of Management, Lucknow E-mail: [email protected]

Make to order systems (MTO) have traditionally been used in environments which face intermittent demand and high degree of customization, and hence require significant delivery lead times. However in the contemporary context, such organizations are facing substantial market pressures and uncertainty, necessitating MTO industries to respond to changing demands at lower lead times. Application-oriented academic literature in this area has been relatively sparse in the Indian context. This paper aims to bring forth the recent approaches adopted by Indian industries to cope with the ��Z�'�� expected to aid practising managers in identifying some major challenges when faced with markets and industry situations such as seen in the cases studied in this paper.

Key Words: Make To Order High Variety Low Volume Decoupling Point Lean Operations

Rajiv K. Srivastava *Subramanian Chidambaran **

IntroductionIn the contemporary business environment, customers expect products and services of high quality, with the characteristics they desire, as quickly as possible and at minimum cost. Every manufacturer aspires to know the customer requirements well in advance and deliver him ‘just the right product’, implying that manufacturing can start only after the exact individual requirements are known. In case the product is a high variety low volume (HVLV) item, then the customer may not mind waiting for its delivery, and a ‘Make to Order’ (MTO) system would be used to produce it. However in case of low variety high volume scenarios, the focus is on responding to the customer quickly and inexpensively, ��'�� |>rather than ‘exactly what he needs’. These are typically met by ‘Make to Stock’ (MTS) systems wherein customer requirements are forecast using suitable techniques, and the manufacturer makes the product ��'��

With market environments becoming increasingly competitive, these classical manufacturing approaches are beginning to overlap in many industries, incorporating various evolving practices in standardizing and customizing their products. For example an industry that traditionally operated on MTO basis may shift towards standardization to reduce lead times and hence gain an edge; conversely, traditional MTS industries may turn towards increasing variety and customization for competitive advantage. Indeed, the distinction between these different systems is gradually becoming thinner and in many cases vanishing, leading to the development of hybrid MTO-MTS systems.


Identifying the Gaps

A reasonable amount of academic literature is available on various aspects of the evolutions in the MTO industry globally. Right from decision criteria for order selection till marketing strategies, academicians have analyzed the different aspects of MTO systems. Some of these areas are: Order acceptance strategy (Nandi & Rogers, 2006), Order acceptance and scheduling (Jalora, 2006), HPP in MTO (Caravilla & Sousa, 1995), Inventory system performance (He & Jewkes, 2000), Order expediting strategies (Arslan et al., 2001), Marketing strategies (Sharma & LaPlaca, 2005), World Class Manufacturing (WCM) in MTO (Hendry, 1997) etc. There have also been researches on combined MTO-MTS systems especially on How to decide MTO vs. MTS (Dirk, 2001), Planning and scheduling (Soman ��>}��³`>%�� \ ��>}��¬`>Production-inventory systems (Risa & De Croix, 1998) and general approaches (Kaminsky & Kaya, 2006).

�� focussed either on the quantitative aspects such as developing models, decision rules, and solving optimization programs; or on developing theoretical concepts and content mostly based on previous academic literature. There are very few case studies �� > �� [�� |� '�'�� \]^^¬`on the ‘Customization-Responsiveness Squeeze’ in the US machine tool industry is among the few �� '��management adopting a case study based approach. Further even the available case studies are typically in a non-Indian context. This has perhaps made academic literature less accessible and less understandable to �� '�� ';�� the link between the academia and industry, which is relatively stronger in western countries compared to India.

Evidently, there is a gap in terms of a category of papers/ research intended primarily for the practising manager operating in MTO environment, especially in Indian context. This causes lack of contemporariness, as well ��''��'��

still taken by managers purely based on their ‘gut feeling’, whereas if properly translated, the already available academic literature like those mentioned above can help in bringing more clarity and objectivity to the managers’ decision making processes.

To summarise, the limitations in academic Operations-based literature related to MTO systems are:

]� $�� ?'��trends in the MTO based industries so that the best practices could be adopted by other organisations; addressing this gap would also help to make academic literature more appealing to practising managers.

2. Inadequate research on the way Indian industries function and adopt MTO systems so that a comparative study with organisations outside India could be explored; this would give the Indian manager a better perspective on where his organisation stands in global competition.

¤� "�� research, and the practical application of these in Indian industry.

This paper is an attempt to bridge these gaps by studying these aspects in relation to industries adopting MTO systems, especially in the Indian context. The case study format has been deliberately chosen to be the methodology of the main part of the paper, since the challenges described in the cases would be faced by managers so they would be able to connect easily to them. Even academic literature corroborates the fact that case studies are considered an appropriate methodology when a holistic, in-depth investigation is �� \��>]^^]`�� \]^^¤` �� \]^^�`> �� 'in this section could be categorised as instrumental explanatory cases, where the case is used to understand more than what is obvious to the observer for doing certain causal investigations (Tellis, 1997). Accordingly, the heart of the paper lies in three contemporary case studies drawn from Indian industries, to give the reader an idea of how MTO systems are actually implemented, and demonstrate how the above knowledge gaps could be addressed in future.


Understanding MTO SystemsIn this section we give the background and conceptual perspectives on MTO systems, drawing from relevant literature, to help develop a focussed understanding of MTO systems and their usage.

MTO Systems Overview

As mentioned earlier, manufacturing approaches comprise two broad types - Make-to-stock (MTS), and Make-to-order (MTO). The basic distinction between the two types is the timing of production vis-à-vis the receipt of customer orders. In MTS, all the production is already complete before the order arrives, and customer requirements are supplied from stock. In full MTO, all the production – in-house or vendors - takes place after the customer order has been received.

MTO manufacturing could cover a wide range of activities/ scenarios. In the pure Engineer-to-Order ��'�� \%��`> �� the product to meet the requirements described by customers in terms of the functions and characteristics they need. These companies often make one-off products, and an order may be for a combination of both design and manufacture. At the other extreme is the component manufacturing company, the classical job shop, where the customer supplies the design and ��and skills available. Each order may be very different from any other, though there may be some repeat production and provisioning of spares. In other cases the company may offer a range of specialized products, but orders arrive intermittently for small quantities. Production is initiated only after order receipt, and may also contain some exclusively customized components. In some cases, such companies are actually selling the skills to perform certain types of operations, rather than an actual product.

MTO systems have been traditionally popular in manufacturing industries that either seek to provide greater variety to their customers, or make products that are unique to their customers (Jalora, 2006]. In certain cases, even the components required to produce ��'�� '�� also required only in low volumes. Some industries that

typically adopt MTO manufacturing systems in varying degrees are (1) Machine tool industry; (2) Earth moving equipment/ heavy construction equipment; (3) Heavy �� ¯\¬`�� Tractor industry; and (5) Electrical equipment industry.

General Features of MTO Industries

The typical Operations features of MTO industries are:

A. Low Finished Goods Inventory: MTO systems �� inventory levels than MTS systems; however an �� sustain this.

B.�[�!�� #��/� MTO systems are known to have a very skilled/empowered workforce trained to perform specialized tasks pertaining to that industry, due to non-standard nature of work.

C.�High Lead time: Since MTO manufacturing can begin only after the customer order is received, and is time consuming due to customization as well as large requirement of skilled activities, delivery lead times would tend to be higher than MTS systems (Kingsman et al, 1996).

D.�Functional Shop Layout: The layout of an MTO system is traditionally more functional than line/ cellular layouts, which matches the non-standardized products they handle. Relatively less attention has been given in literature towards the ��'��'��'��

E.� Mixed Vertical Integration: MTO industries vary in degree of vertical integration. Higher integration is generally adopted by certain companies to keep control on the uniqueness and variety of the products. However in some cases such as the aircraft industry where the cost of in-��>��'��prefer to outsource many of their activities.

F.� Planning & Execution Tools: Approaches such as MRP are generally not fully adequate for MTO systems, being more oriented towards initial planning, and need to be supplemented by order tracking tools at operating levels.


G.�\�� $��%��/� Despite its importance, benchmarking of products/systems ��;¾;��'�� system because of wide dissimilarities between the environments being compared (Hendry, 1998).

Evolutions in MTO Systems

Contemporary MTO industries have reached their present modus operandi in two major ways:

� Traditionally MTO systems due to nature of products/ services and customer segments served;

� Industries using MTS systems traditionally but ��

Increasingly, such systems are evolving to hybrids of MTS and MTO, differentiated by the position of an important Operations concept called the ‘Decoupling Point’ (DP), which indicates how deeply the customer �� '�� separates the order-driven activities from forecast-driven activities, with only activities downstream of the DP as MTO. Activities upstream of the decoupling point can be run as MTS, which can be optimised using lean/other principles (Donk, 2001). Some possible positions of the DP can be seen in Figure 1, adapted and extended from Hilderbrand (2008); each position represents a different trade-off choice between increased lead times and lost opportunities vs. higher inventory and obsolescence risks.

Figure 1: Decoupling Point Positions along the Production Continuum

While in classical MTS (1) the stock is held at/near the Point-of-Sale, in MTS-STO (2 - Supply to Order) �� >�� ''� ��an order is received. In Assemble to Order (3 - ATO) systems, the materials and manufactured components are held in stock, and assembly is carried out only after the order is received. Build to Order (BTO) is a variant of ATO where there is hardly any in-house component manufacturing, and purchased components go directly ��;��\¬`�� (5) in that standard materials are bought on ‘Purchase to Stock’ basis, but in-house manufacturing begins only on order. In some industries where the customization – responsiveness squeeze is prominent, the Build to Forecast (BTF) approach is often adopted, wherein manufacturing is initiated and items in production are matched with customer orders, essentially meaning that the position of the Decoupling point is not entirely �?� �

Systematic approaches are needed to improve MTO systems and manage the above challenges effectively. Hendry (1998) proposed a useful framework to help MTO systems evolve into WCM organisations: rethinking the partition between line and functional layout, and thus re-positioning the decoupling point; and improved methods of operation and control. In an effort to structure the application of Lean principles to HVLV systems, Jina et al (1997) have indicated the key challenges as lack of understanding, demand turbulence, and supplier relationships Solutions proposed are adoption of Design for Logistics & Manufacturing (DFLM) principles, turbulence reduction through streamlined order taking, process decoupling, and facility partitioning using runner-stranger segregation. Donk (2001) cites an example of the food processing industry as a traditionally MTS industry turning MTO.

In conclusion, improvements are needed as well as feasible in MTO systems through systematic application of Operations Management principles and Lean Thinking. However it needs concerted and coordinated action, some examples of which are shown through the case studies and analysis.


Case StudiesIn this section, we focus on the practical aspects of MTO systems through three contemporary Indian case studies, to help understand the evolving modus operandi �� overall scheme. The cases pertain to three companies operating in India in Component manufacturing, Shipbuilding, and Electrical equipment industries. These industries have traditionally been MTO but are now moving towards a hybrid MTO-MTS structure due to changing consumer preferences, increasing competition, redesigned manufacturing practices �� '�� observations/ descriptions over the previous two years (2008-2010), and represent a spectrum of MTO systems in terms of Volume-Variety combinations.

Figure 2: Sectors Placed on the Volume-Variety Matrix

A. Case Study of an Indian Component Manufacturer “A”:

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‘A’ is a machined component manufacturer in central India, producing over 300 different varieties of gears and shafts to be used in farm equipment, commercial vehicles, and passenger automobiles. The lead times for producing these items could range from a week to a month; a normalized item type representing the entire range would have an average production lead time of about 15 days.

Demand Management System

The Sales & Marketing (SM) team is the main source of demand information for the factory. Once customer demand is either obtained or derived or forecast, these estimates are translated into exact monthly demands for each variety of gears and shafts. This information is then passed on to the factory. Though the products �� ;��>��demand are largely smoothened by the SM team. This allows ‘A’ to avoid maintaining stocks of any of its products, and follow an essentially MTO manufacturing approach.

'� ��+��&��

��'�� '��'�� is diagrammed below:

Figure 3: Process Flow Diagram for Gear/Shaft Manufacturing


Problems Observed

The factory however started facing some challenges a few years into its course of operations.

a. WIP inventory of the factory was increasing

Comment: An MTO system is typically expected �� WIP inventory levels. However in this case, though the products were manufactured on MTO basis, WIP inventory was increasing.

b. The Delivery dependability of the factory had ��

Comment: Z�� '�� the proportion of orders delivered to the customer by the agreed date. Low dependability indicated that the factory was regularly unable to meet the committed dates.

c. The overall factory effectiveness was only 50-60%

Comment: The factory effectiveness was calculated as per the Total Productive Maintenance formula Overall Equipment Effectiveness = Availability x Performance x Quality. The corresponding value �� ¿³��> �� below par, and indicated possible problems with one or more of these three constituent factors.

Analysis and Insights

The analysis of the situation yielded some interesting insights and guidelines for managing such scenarios:

1.�Around 70-75% of the customer orders (by volume) taken up by the SM team were ‘GC intensive’. The revenue differential between the GC and non-GC orders was ~5-10%, and the orders were prioritised based on the total revenue generated. Thus the orders which formed the top 10-20% of the order book, when sorted as per ‘Revenue per GC hour’ formed the bottom 10-20%. Thus though the factory had a capacity utilization of ~80%, the actual output capacity was only ~50-_��> �� ?'�� problem.

2. ‘Critical’ and ‘Rush’ orders further aggravated the situation. To accommodate these orders, some current orders were removed from the production cycle and the new orders were taken up, which resulted in a huge rise in the WIP �� '�� ?'��frequent machine breakdowns. This explains the inventory and delivery dependability problems. In fact, preventive maintenance is a very important activity for such sectors which are highly machine intensive, and whose operations require high reliability.

3. The customer orders need to be well balanced in terms of the operating machine hours. This requires a co-ordination between the SM and Production teams before taking up any major order. Basically a trade-off needs to be achieved between the gain in revenue/machine hour and the loss of customer delivery dependability, which if not addressed could seriously affect the company’s market position.

Activity Legend:1. Forged blanks, the main raw material for gears and shafts, are procured from external suppliers.2. These blanks are then sent to another supplier to perform the turning operations.¤ ��'�� >��'��>��> ��>'��

and heat treatment in that sequence are performed on the parts. One of these, GC (a Gear Cutting operation), is considered to be the bottleneck operation in the entire process.

¬� �� > �� '�� '��> �� despatched out in lots to the respective customers.


B. Case Study of an Indian Ship Manufacturer “B”

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“B” is one of India’s largest shipyards, comprising 2 docks – one used for construction of new-build vessels, and the other used for ship repairs. Besides construction of conventional vessels like bulk carriers and tankers, the shipyard also undertakes defence orders. The ship building industry has been traditionally a Design to Order - ETO - MTO industry globally (varying between the three based on customer requirements), and the Indian industry is no exception.

Need For a Change

“B” sustained the initial years on government orders and built mostly large conventional vessels like bulk carriers and tankers. During this time, the yard used to ��¤;¬�� ''��companies were reluctant to place orders with the shipyard due to the higher lead times as also uncertainty of schedules. The shipyard was working on self-�� '��=��which received orders mostly from PSUs.

Liberalization and economic reforms of 1990s however changed the market scenario. The government subsidized shipbuilding by providing an import duty waiver for ship-building materials. There was a shift in policy wherein the government shipping companies would place international tenders for availing the best prices. With entry of foreign players into the market and being itself exposed to the global markets, the shipyard �� depend on the government for survival, else it would �� ;'�� enterprises which closed operations over the years.

The Change

All this resulted in the shipyard undertaking major restructuring of its strategy and operations, some of which are outlined below:

1. Outsourcing about 60 % of the production to subcontractors, with only the complex blocks being constructed by the yard.

Comment: This results in increased capacity without actually having to build additional capacity. Also the talent pool within the organisation can be better utilised by employing them for high skilled activities. The expertise and economies of scale of subcontractors can be leveraged which would in turn result in a lower cost for “B”.

2. Expansion of facility to accommodate bigger vessels.

Comment: Bigger vessels i.e. the conventional vessels are attractive as they (1) offer a stable business and (2) increase the top line.

3. Partnering with other players to focus on small ship market segments.

Comment: The small ship segment is a growing market and comprised ~18% of the total ship building market. “B” was non-existent in this market, whereas to compete globally it is essential to establish a presence in this segment. Also small ships are ideal to extend market share in terms of volume sales and hence establish a wider presence. ��'��higher than for big ships. However, since “B” does not have the necessary expertise to enter this market, partnering is essential.

¬� !�� ';��'�� 'building businesses which offered higher margins (~15-20% more) than the conventional ship building business.

Comment: This helps in diversifying the risk of the business “B” is currently into, and also adds ��'��

5. Stocking ready to make packages to reduce lead time

Comment: “B” holds vessel design packages along with the critical machinery, equipment and control systems in its stock. These ready-to-make packages on offer to prospective customers are ��>�� time in delivering the services and procuring the machinery and equipment.


Other Operations Related Observations

Despite the above changes that have taken place in “B” to keep up with the changing macro-environment and competitive scenario, some features of the manufacturing process and layout seem to be in contrast to those adopted by major players abroad.

Firstly, the shipbuilding processes in “B” are more ‘Process Focused’ as opposed to a ‘Product focussed’ strategy adopted by many major international players. This is because of the low volume of manufacturing that prevents standardization to a level adopted abroad. Secondly, the shipyard takes up orders of different types of vessels – aircraft carriers, bulk carriers, tankers etc. that too in smaller numbers. This results in a high product variety, thus ‘low volume and high variety’ still characterizes “B”, and the strategic changes mentioned earlier are an attempt to break away from this segment and move towards ‘Moderate Volume Moderate Variety’.

The layout of the shipyard is also process oriented. The processes are performed in different shipyard shops ��>��>'�'��>��>

blasting, painting etc. The components are taken to these different shops where they undergo that particular process. This is in contrast to the design adopted abroad wherein similar blocks are produced in large volumes �� '�� structure (Joshin, 2010).

C. Case Study of an Indian Electrical Equipment Manufacturer “C”

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‘C’ is a major Indian manufacturer of medium voltage switchboards used in distribution stations, electric utilities, and captive power plants (used in iron & steel, paper, cement industries etc.). Medium voltage �� '�� '�� ¯hence are typically manufactured only after the order is received from the Sales/Marketing team (Subramanian et al, 2011).

'� �� +�� Z�� &�� ]�*� ��Was

The typical order cycle is shown as follows, whereby every order goes through two major stages:

!�� !��


1. Design and Engineering Stage (Engineering Cycle)

Once the order is received, the design department analyses the requirements and checks for similar designs in the database. Similar requirements are drawn from the database, and new requirements are designed afresh. Based on the designs, the engineering division decides the technical (electrical and mechanical) parameters required for the order.

2. Order Planning and Execution Stage (Manufacturing cycle)

SOE then passes on the designs to the Material planning department through SAP, which generates ��\��`��'�� The BOM is then categorised into ‘Stock’ and ‘Non-stock’ components. Stock components are drawn from the material store, while Non-stock components are ordered through Purchase Orders to suppliers. The highest lead times among these are for Copper/Aluminium bus bars - the primary conductors - and FRP (Fibre Reinforced Plastic) sheets used for insulation purposes. On arrival at the Material Planning stage, the order is entered into the manufacturing cycle database. The dates for various manufacturing activities are then calculated backward from the delivery date committed to the client.

The switchboards (henceforth referred to as panels) ��'�� batches, with long waiting times for all activities on the order. Effectively, the lead time for a particular order would thus be the lead time for a single panel times the number of panels. The entire panel set for an �� '��'��'��>�� be conducted at this place. Thus the product remained stationary while the workers moved between customer orders.

\��+��

However the factory faced certain problems as observed over time:

�� $��~ �� inventories were ~30% of total monthly sales (volume-wise).

b. Raw material inventory: Though a customized product, many of the components which completely depended on the customer requirements were forecast/ ordered in advance based on sales/marketing estimates, and stocked in the factory. This was essential to keep a steady �� ''��> ��'�� lead time components such as FRPs and bus bars.

c. Supplier risk: However the actual material delivery from the supplier’s end was deferred till the order actually arrived at the factory. Thus the supplier bore the risk of stocking these ‘A’ class items in his premises.

d. Scalability: Increasing demand volumes posed a problem in the current way of working, as the batch production approach did not seem scalable to meet increasing demand for panels.

Changes Implemented

Hence it was decided to change from the batch production system to the line production system. This was to be done through a ‘Lean implementation’ program for which specialised consultants from the US were hired. Lean adoption however required broader changes such as restructuring of the layout as well as the processes.

Layout Changes

� The concept of assembly line was introduced, and 5 assembly lines were formed with 6 workstations in each line.

� The work content in each workstation would vary based on the order. However the ‘takt’ time or the �� '�� ?� �� ] ��> ��work content adjusted according to this target time.

� The panels had to be physically transported from one station to the other by use of hand/motorised


trolleys. This was typically done by the group-leader in-charge of the respective line.

Product Design Changes

�� '��assembly line necessitated a re-look into the design aspects of the panel. The changes made in the design of the panels included:

1. Reducing the number of variations in the panel by making it suitable for all conditions.

For example: The variations for short circuit current rating of 25KA and 31.5KA were combined into a single design suitable for both. This indeed required considerable research as the panel cost had to be maintained at competitive levels.

2. Reducing the variations in the panel components

To illustrate, Bus bar variations for current ratings of 630A and 1000A were combined into a single design.

3. Reducing the variations in materials used for panel components

For e.g. many of the bus bar variations in Copper were replaced by Aluminium equivalents, thus reducing overall cost, and also standardizing the material.

¬� ��'��'��

For example, replacing many of the copper bus bars with aluminium reduced the panel weight, thus facilitating easier movement.

Changes in Material Supply

1. Kitting was introduced to deliver components to respective workstations. The work was performed by dedicated group of low-skilled people called ‘spiders’ who were responsible for supplying required components to workstations at the right time.

2. The need to keep the line running required that the components delivered were on time and of the

right quality. This was ensured a day in advance by the Logistics group for all the orders due on line that day.

\��+�� [��_��

1. The line could handle only the standard activities of panel assembly. As the product was inherently ��=� > �� '�� segregating the standard and non-standard panels before allocating them to the lines. Increasingly, the percentage of non-standard activities in the panel kept rising, with the end result that much of the processing on the panel had to be done after it came out of the line. Thus a panel would typically spend a day on the line with minimum processing done, and then would be transferred to a separate '��'��;�� tasks were performed. Often the panel would come out of the line with just 15% processing completed. The non-standard activities had to be performed on a batch basis thus overriding the �� '�>and the time spent by a panel on the line was no indication of the lead time.

2. All these initially resulted in performance parameters such as lead times, delivery reliability, �� '��actually worsening compared to before the lean implementation.

3. There was ambiguity in measuring worker performance, as the metrics of measuring the worker performance on the line and in the batch processing system are different

Technical & Quality Challenges

1. As per IEC standards, the redesigned products needed to be ‘type-tested’ for their short circuit capacity and other parameters, and commercial production could not begin unless the prototype products passed these tests. Also every redesigned/new product needed time to rectify errors in design, manufacturing etc. before production could begin on a large scale. However even before


the completion of type-testing, the Marketing/Sales divisions would inform their customers of the redesigned products, and even commit orders for the same. This put extreme pressure on the manufacturing and design departments to deliver the products in much lesser time, often at the cost of quality, which in turn resulted in increased customer complaints.

2. Another problem was that most panels were given a green signal for despatch by the quality personnel under the pressure from the manufacturing people in-spite of observing quality problems. The reason for this being that the Quality head reported to the Manufacturing head, and the performance incentives of the employees were closely linked to ‘volumes’.

Learnings And InferencesIn this section we summarize the observations and insights from the industrial case studies, and relate �� The section is primarily targeted at giving practicing managers a capsule summary of the key trends in the three industries considered in the Cases section as well as the general trends in MTO industries.

Case Study A:

This situation is at the higher volume end of MTO, though individual orders may not be in very large quantities. While the number of variants is large, the range of product and process designs is moderate. The insights gained from the case study combined with evidences from literature lead us to the following inferences about trends in the functioning of MTO systems in the component manufacturing industry.

¦�� and marketing departments plays a very important role in deciding the workability of MTO systems in such volume-variety scenarios.

Case Study B:

This situation is at the lower volume end of MTO, where order sizes may be as small as one, and each unit produced may be completely designed for the customer. The trends observed in this case study have been compared with those of similar (same industry) organisations abroad, mostly in East Asia. This comparison brings forth some salient features of the Indian industry.

Table 1: Learnings from Case Study A

Observation Inference Academic Basis

There was a capacity constraint at the GC operation

The typical constrained resource is the machine hours at a particular operation

Constraint Based Planning & Scheduling (The Goal)

Hence the evaluation metric was Revenue/ GC hour

Hence the Sales/ Marketing team must evaluate each order’s acceptance with respect to its ‘Revenue generated per constraint machine hour’. Order �� '�� to be done based on this metric

Under the “Manufacturing Marketing Interface” in Sharma et al 2005


Table 2: Learnings from Case Study B

Trends in Indian Context

Trends in International Context

Differences and Reasons

Majority of the activities outsourced to subcontractors are the less complex ones

Primarily the more complex activities get outsourced

�� development of capable vendors, currently lacking with Indian players. Hence the primary need is to develop competent suppliers.

The product mix is changing from only big conventional ships, to a mix of big ships (still ~60%), smaller ships, high-tech vessels and ship-repair activities

The product mix has a large % of big high-tech ships (~90%)

1. The cash-conversion cycle in India is much longer than foreign countries, which can be mainly attributed to higher lead times for manufacturing big ships in India (2-3 years for a 100,000DWT ship in India vs. 8 months for a 500,000DWT ship abroad) Thus capital gets locked-up for long durations causing ��

2. Since most of the complex activities are performed in-house, space requirements are considerably higher than for foreign players. This capacity limitation reduces the number of ships that can be concurrently handled.

3. Small ships require much less space (~50%) and lower lead times (1/6th) than big ships; effectively translating to 1 big ship ~ 10-12 small ships (time-and-space-wise)

Shop layout and m a n u f a c t u r i n g processes are process oriented

Shop layout and manufacturing processes are product oriented

The products manufactured in India are typically the Low volume – High variety category, necessitating a process structure; internationally volumes are higher and variety lower.Increasing standardization can lead to product focussed layouts. Hence more focus should be on introducing commonality and standardization in products/ designs.

Ready-made packages are stocked to reduce lead time

This aids in standardization and hence reduced lead times, but higher inventory/ commitment.

Case Study C:

This situation is the most classical form of MTO, with moderate variety and low-medium volumes for each order/ �� >�� '�'��~��''��"��'��!"!��\��et al 1997), and “Marketing in the Emerging Era of BTO Manufacturing” (Sharma & LaPlaca, 2005).


Table 3: Learnings from Case Study C

Observation/ Insight from the case Insights from Academic Literature

Inferences

Lean concepts may not always be directly �''�� customization

Inadequate understanding of the meaning and implications of High Variety and Low/ medium Volume causes �� ''�� principles (Sharma et al, 2005)

Both practical and academic literature corroborate that implementation of lean principles in traditional MTO environments cannot be a blanket approach, but a systematic adaptation of the concepts outlined in previous sections

For lean implementation in MTO systems, Product Design needs to undergo basic changes such as incorporating standardized common parts, which can be manufactured faster and stocked

The lean principle of DFLM is even more critical for such systems; the cost and complexity penalties of not exploiting it are much higher (Sharma et al, 2005)

Making changes in the design of products, components, and sub-assemblies is an integral aspect of lean implementation in High Variety, To Order systems. However this needs to be done carefully based on situational peculiarities.

�� goods inventories due to misinterpretations/ changes in customer requirements, and delays in customer pick-ups

There must be close interaction between the sales and manufacturing functions; Order commitment must be after the promise dates have been agreed upon by Marketing/ Sales, Design, Manufacturing and Quality heads

"�� through having products First Time Right (FTR) at every stage of the process

Make to Order processes will impact communication between manufacturing and marketing. Traditionally links between these functions have not been very strong (Hilderbrand, 2008).

Inadequate communication regarding demand pipelines �� manufacturing as well as higher inventory/ shortage and other expenses.

The Manufacturing-Marketing link has to be extremely strong for MTO organisations, since unsold products may not be saleable to other �� '�� '��

The Quality function needs to be an independent agency reporting directly to the Factory head rather than the manufacturing head.

In cases where the Manufacturing head and Factory head are the same, the Quality function’s reporting should be to the next higher level in the hierarchy.

�� '��;�'��problem and may not be readily generalized for the industry as a whole. However it does suggest that in the organisation structure design, it is advisable to keep the reporting line of the Quality function independent from Manufacturing/ �� interest affecting product quality.


The case study also indicates that lean implementation was primarily introduced to bring in a greater degree of standardization. Earlier, the factory was operating as more of a combination between ETO and MTO. However with implementation of Lean, many parts got standardized and commonality was introduced. Thus an increasing application of lean to HVLV systems results in moving the decoupling point to the right in the continuum in Figure ]>��'��\�`��\¬`��\¤`�

General Inferences and Concluding Remarks¦��=�� >'�� '�� '�� be adopted by practicing managers in their respective organizations, and identifying potential areas for exploration by researchers. The cases studied represent different Volume-Variety combinations in MTO systems. Accordingly the trends and approaches evolved to cope with the challenges of managing the multiple objectives of variety/ customization, delayed manufacturing, and quick response depend on the scenario in that particular industry. From ��>�� =�� ~

Table 4: General Learnings

Related Function in Value Chain

Inferences Generalizable across Industries

Product Development Use of standard and common parts in product designs should be increased, to help achieve shorter lead times for products manufactured in MTO systems

Manufacturing Z��'�� '�� environment. This will help in examining the feasibility of implementing concepts like lean manufacturing to traditional systems.

Marketing/ Sales The link between the manufacturing and marketing has to be strong and bi- �� ®��?�� up orders based on manufacturing parameters such as constraint resource ��®�� '��

Vendor Management The relationship with suppliers and vendors has to be strengthened to ease the process of introducing changes and reducing response times in HVLV systems.

General The link between the industry and academia should be further strengthened �� '��|��

The ‘Understanding MTO Systems’ section of the paper indicated that a reasonable knowledge base exists in academic literature, on different ways of dealing with market uncertainties and requirements relating to traditional as well as hybrid MTO systems. The data for the case studies on the three industries has been gathered in the past 2 years, and a look at the issues dealt with tells us that many of these aspects are no different from those pointed out in MTO related academic literature written prior to the cases. Since most of the literature reviewed for this paper has been written around the 1997- 2006 period, it is apparent that many of the improved methods of dealing with �� $� ��'�� the academic literature is either not in a format that is easily understandable and implementable for Indian managers,


or there is lack of proper communication links between the academic and industrial worlds. Indeed, many of �� '� �� they even cropped up.

A principal objective of this paper, as mentioned in the Introduction section, has been to attempt to address gaps in academic literature vis-à-vis understanding and implementation of MTO systems in India. Though not completely exhaustive in terms of coverage of Indian �� > �� clearly the aspects that are, or could potentially be, major concerns for all the players. A quick review yields the following:

� All the case studies have attempted to address gaps (1) and (2).

� $� �� '�� > �� £ [ ��attempted to address gaps (1) and (3), and case B has been an attempt to address gap (2).

�� '� �� with the support of industry professionals, to develop similar case studies in other industries and companies which have traditionally been MTO oriented, but are increasingly moving towards MTO-MTS combinations. Comparative studies analysing the similarities and differences between MTO systems, in the major manufacturing sectors in India and those abroad, offer potential for further understanding. The academic studies should also be extended to the other manufacturing system support functions such as shop ��$��>��''��>�� management for MTO systems etc. Such studies would offer new insights into evolving MTO systems as a whole rather than just the manufacturing aspects.

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(1998), Make-to-order versus Make-to-stock in a Production-inventory system with General Production Times, IIE Transactions, 30(8), 705-713

� H. Arslan, H. Ayhan & T.L. Olsen (2001), Analytic models for when and how to expedite in make-to-order systems, IIE Transactions 33 (11), 1019–1029.

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� Dirk Pieter van Donk (2001), Make to Stock or Make to Order: The Decoupling Point in Food Processing Industries, International Journal of Production Economics, 69.3, 297-306

� Feagin, J.R., Orum A.M., and Sjoberg G. (Eds). (1991), A Case for the Case Study, Chapel Hill, NC: University of North Carolina Press.

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� Jay Jina, Arindam K. Bhattacharya and Andrew D. Walton (1997), Applying Lean Principles for High Product Variety and Low Volumes: Some Issues and Propositions, Logistics Information Management, 10-1, 5-13

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� Soman, C.A., Donk, D.P. van & Gaalman, G.J.C. (2007), Capacitated planning and scheduling in combined make-to-order and make-to-stock food industry: An illustrative case study, International Journal of Production Economics, 108, 1-2, 191-199.

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� §�� X ��>[��!��Z��£ ��Z�� \}��¬`> %�� Combined Make-to-Stock and Make-to-Order Manufacturing System, Annals of Operations Research>]}_>]�¤¿]¤¬�


UDYOG PRAGATIThe Journal for Practising Managers

Guidelines for Authors

ScopeUdyog Pragati invites original contributions in the form of ‘State of the Art’ Papers and Case studies on the application of industrial engineering and management concepts/techniques in industrial and government organisations.

Types of PapersThe following types of papers will be considered for publication in the Journal.½ �� '�'�� ½ [�� ½ �� '�'�� ½ ��'�'��½ %�'��'�'��

A paper should contain 3000 to 5000 words. In addition, discussion on paper published previously could also be incorporated.

Submission of ManuscriptsThe following guidelines are to be adhered to while submitting the manuscript.1. The desired order of content is a) Title e) Main Text b) Author (s) f) Acknowledgment ` ��\�` �` ��>�� d) Abstract h) Appendices

Tables and illustrations, complete with titles, labels and art-work should be placed in the text at the appropriate locations. The diagrams, graphs, etc. should be sent in original.

2. The abstract should be brief, self-contained, explicit and should not exceed 200 words.

3. The manuscript should be typed on one side of good quality white bond paper in double spacing. Mathematical terms, symbols and other features that cannot be typed should be inserted neatly into the text by hand in black ink.

The author should also send an ‘electronic version’ of the paper on diskette (3.5” Floppy Disk) using standard software - Word/Word Perfect.

4. The equations should be numbered sequentially in parentheses by the right margin. The theorems, propositions, corollaries, etc. should be numbered in one sequence as, for example, (1) Proposition, (2) Corollary, (3) Theorem, etc.

�� |� �� year of publication i.e. (Tapley and Lewallen 1967). They should be mentioned in alphabetical order in the ��'�� ~

a) B.P. Tapley and J. M. Lewallan, (1967), “Comparison of several numerical optimization methods,” Journal of Optimization Theory and Application, Vol. 1, No. 1, o.32

b) Neil Botten and John McManus, (1999), ‘Strategic Management Models, Tools and Techniques’, Competitive Strategies for Service Organisations, Macmillan Press, pp. 107-161.

6. The authors should submit a brief statement of their '�� > �''��held, research interests, published work, etc.

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10. All manuscripts have to be sent in triplicate to the % ��>© ��>#$�$%>!��"��>��- 400 087.


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NATIONAL INSTITUTE OF INDUSTRIAL ENGINEERINGMumbai - 400 087 INDIAMANAGEMENT DEVELOPMENT PROGRAMMES 2012-13 FOR PRACTISING MANAGERS, ENGINEERS, PROFESSIONALS AND ADMINISTRATORS

SL. NO. COURSE DAYS DATE COURSE LEADER/S

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22 ��%¢$[!$�$�#��#�¢%�� 3 03.12.2012 MANI MADALA (Ms)

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For Postage REGISTRATION No. RN 31664/78

Management Development Programme (MDP) Fee (Exclusive of service taxes and others)

Duration SAARC Other Countries

5-day programmesResidential ` 20,000/- US$ 2000Non-Residential ` 15,000/- US$ 1500

3-Day ProgrammesResidential ` 12,000/- US$ 1200Non-Residential ` 10,000/- US$ 1000

Service Tax (Presently 10.30%) will be charged extra.

Residential Fee : Includes tuition fee, course material, lodging & boarding and Institutional amenities. The rates are applicable for Double occupancy AC room

Non-Residential Fee: Includes tuition fee, course material, working lunch, tea/coffee

NITIE is exempted from Income Tax and no TDS is deductible

Refund Rules : Fees once paid can be adjusted against future nominations only. In case a course is cancelled on account of inadequate participation or any other unforeseeable reasons, the participants will be informed of the cancellation by e-mail or Fax and the fee will be refunded. NITIE will not be liable for any other expenses incurred by the company or the participant.

Eligibility : Person with relevant experience and responsible position in Industry.

Enrolment : �� #��>Z��>��>��>%?'��\��`and the present functions. Fees can be sent by Demand Draft drawn in favour of “NITIE, Mumbai” addressed to:

Assistant Registrar (Programme)National Institute of Industrial Engineering (NITIE), Vihar Lake, Mumbai – 400 087, Tel. No. : (022) 28573371,

Fax No.: (022) 28574033/28573251, E-mail: [email protected] / [email protected] Website : www.nitie.edu(for details of courses see backcover and page no. 67)

Edited by Dr. (Ms.) Mani K. Madala and published and printed by Dr. U. K. Debnath,National Institute of Industrial Engineering, Vihar Lake, NITIE, P.O., Mumbai 400 087 and printed by him at

ALCO CORPORATION, A2/72 Shah & Nahar Industrial Estate, Lower Parel (West), Mumbai - 400 013.

NATIONAL INSTITUTE OF INDUSTRIAL ENGINEERING Mumbai - 400 087 INDIAMANAGEMENT DEVELOPMENT PROGRAMME CALENDAR 2012-13(APRIL 2012 TO MARCH 2013)For Practising Managers, Engineers, Professionals and Administrators