issn 0970-3365 vol. 36, no. 2 april-june, 2012 2012_web.pdf · vol. 36, no. 2 april-june, 2012 issn...

NATIONAL INSTITUTE OF INDUSTRIAL ENGINEERINGMumbai-400 087 INDIA

Vol. 36, No. 2April-June, 2012

ISSN 0970-3365

IN THIS ISSUE...

Buyers’ Expectations, Perceptions and Satisfaction on the People (Employees) Mix of Cellular Services Marketing

A.N.V. Durga AnupamaDr. Mamilla RajasekharC. Saradamma

Increasing Bottom-Line Through Six Sigma Quality Improvement Drive: Case of Small Scale Foundry Industry

Darshak A. Desai

�� !��!��!��

Dr P. David JawaharS. N. Raghavendra

Performance of traditional X-Bar chart with correlated data

Sukhraj SinghDr. D. R. Prajapati

A Conceptual Model for Gauging Overall ��!��"��

#$%��&�� $'��*��C.Muralidharan S.G.Deshmukh

Selection of Facility Location for Spinning Industry: Analytical Hierarchy Process (AHP)

Dr. Mohan KhondGovind Ingale

UDYOG PRAGATI - The Journal for Practising ManagersVol. 36, No. 2, April-June, 2012

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

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iVol. 36, No. 2, April-June, 2012

IN THIS ISSUE…

Page No,

1. Buyers’ Expectations, Perceptions and Satisfaction on the People (Employees) Mix of Cellular Services Marketing (A case study of Tata Indicom of Andhra Pradesh)

1-10

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2. Increasing Bottom-Line Through Six Sigma Quality Improvement Drive: Case of Small Scale Foundry Industry

11-23

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iiVol. 36, No. 2, April-June, 2012

Page No,

4. Performance of traditional X-Bar chart with correlated data 34-41

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6. Selection of Facility Location for Spinning Industry:��'��*�'�+

55-63

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1Vol. 36, No. 2, April-June, 2012

Buyers’ Expectations, Perceptions and Satisfaction on the People (Employees) Mix of Cellular Services Marketing (A case study of Tata Indicom of Andhra Pradesh)

* Ph.D scholar, Dept. of Commerce, Sri Venkateswara University, Tirupati-517 502, Andhra Pradesh. E-mail: [email protected] mobile: 92485 48668

** Professor, Dept. of Commerce, Sri Venkateswara University, Tirupati-517 502, Andhra Pradesh. E-mail: [email protected] mobile: 093470 36765

** PhD scholar, Dept. of Commerce, Sri Venkateswara University, Tirupati-517 502, Andhra Pradesh. mobile:09290684806

Arithmetic growth of number of customers, cut-throat ��$��)�$�#��$��#�� 8��)�� !"��#��$$��$�� services right from its inception in 1996. At this juncture, the �� *�� $�� $��+��)�� $+��/��4�$��5�$��7/��$��8��#��#��$��#�� ##�� $��to improve the same.

Key Words: Cellular services, customer expectations, customer perceptions, customer satisfaction, People, 7Ps of services marketing, factor analysis.

A.N.V. Durga Anupama *Dr. Mamilla Rajasekhar **C. Saradamma ***

Introduction:To be successful in service business, people inside and outside of your business are responsible for every element of your sales and marketing strategy and activities. In the jargon of services marketing, /��refer to the customers, employees, sub-contractors, agents, management and everybody else involved in it. Service people are critical in their interactive marketing, in the sense, that if promises are not kept, customers �� >�� Z[��Zeithaml, 2010). In services marketing, to the customer the people involved are generally inseparable from the business encounters. Understanding the customer better allows designing appropriate products. Hence, the role of �� $��8��#� plays a critical role in recruiting the right persons who have the interpersonal skills, attitude, service knowledge and abilities and in providing proper training and motivation to obtain a form of competitive advantage.

Indian Telecom Industry: India’s digital cellular service network is one of the third largest in the world after China and USA with nearly 300 million connections. Telecommunication sector in India can ��<��>]��Z��^�providers, and cellular services. Cellular services can be further divided into two categories: Global System for Mobile Communications (GSM) and Code Division Multiple Access (CDMA). The GSM sector is ruled by Airtel, Vodafone, and Idea Cellular while the CDMA sector is dominated by Reliance and Tata Indicom. _�� *�� !�� [�'�`� #�'�`� �!��`��%�'�$��!��{�|�#��}�"~#��operators providing mobile services in 23 telecom ��Z��&��`�[`��"^�covering 3500 towns across the country. Table 1 shows


the customer market share and their revenue market share of key mobile operators in India during 2009:

Table 1: Market shares of Indian majors

Telecom operator Customer market share

Revenue market share

[�� 24.3% 33.8%

Reliance Communications

18.9% 11.5%

[�'� 12.7% 10.2%

Idea Cellular 11.2% 12.1%

��/�� The Tata Group’s Tata Indicom telecom initiatives are currently spread over four enterprises: Tata Teleservices and its subsidiaries: ��Z#��^`�%�'��<��[��`��<��$��Z��^`��!��$�{�`��|��!`�<��incorporated in 1996. Starting with the major acquisition �� $�� Z��^� ��`� ��<� ��Z#��^��~��2002, is targeting to achieve 100 million subscribers by 2011. The company launched mobile operations in January 2005 and today it enjoys its presence in all of India’s 22 telecom circles. Tata Teleservices was �� >�� "~#�� with the Andhra Pradesh circle as launching pad. Tata ��!��#��`� ��=��`��and ECI Telecom for the deployment of a reliable, ��<��=$��!��!��>��of Tata Indicom includes: 1. Mobiles, including prepaid ��!��!��`��$��=�`��$��`��$��!�� `� }$� �"��]`� �$� [��`� �$� ��calling card, including India calling and Global calling card. It has entered in to an agreement with Quippo ��<�� |��!��Singapore government in January 2009 to merge its �� <�� !�� <�� !!�� and maintain signaling towers in India.

Review of literature and need for the present study: �� !�� the customer satisfaction through raising the offered service quality (Fornell et al., 1996 and Audin et

��.2005) noted that switching costs and service quality are the most important factor for determining customer loyalty. Perceived service quality is often viewed as a pre-requisite for loyalty and is included in models as an outcome variable (Cronin and Taylor 1992). Customer satisfaction can prevent customers from defecting (Reichheld and Sasser, 1990). Service quality has been described as a form of attitude, but not an equivalent to satisfaction that results form the comparison of expectations with performance Z��$`��{��[��~��<`��{{�^$�Customer expectations and perceived performance of services have been found to be the main antecedents of perceived service quality besides customer care �� purchase of the cellular services (Girish Taneja 2007).

The present research study has been undertaken with an objective to achieve customer satisfaction being a service industry which involves a high level of people interaction. At this backdrop besides a multi-dimensional competition emanating from foreign companies such as %��`��!��[�'��!��>��to satisfy customers who make judgments and deliver perceptions of the service. An important marketing task is to set standards to improve quality of services provided by employees by providing proper training to them and monitor their performance.

Objectives of the study: Following are the objectives set by the authors for their study:

1. To study and analyze the needs and preferences of the customers towards People (employees only).

2. To determine the gap (satisfaction/dissatisfaction) between the services expected by customers and the services offered by Tata Indicom with reference to the People.

3. To measure the satisfaction of customers of Tata cellular services towards people (employees) of it.

Locale of the study: The study aims at analyzing the expectations, perception, and customer satisfaction on people of Tata Indicom cell phone services in Chittoor district of Andhra Pradesh.


�� The sample was selected by using systematic judgment sampling. The frequency distribution of the respondents is done according to gender, age, education, occupation, and income (table 2).

Table 2: Sample selection (sample size=100)

;�� #�

1. Gender Men= 60 Women=40

2. Age (yrs) <25 yrs =25 25-35 yrs= 30 35-45 yrs =22 > 45 yrs=25

3. Education SSC=14 Inter=16 31=graduates 24=PGs 14=Professional

4. Occupation Students=21 Self-employed=37 Employed=37 Professional =5

5. Monthly income

Rs. 0=21

<Rs. 5000=41

Rs.5000-10000=21

Rs.10001-15000=10

Rs.15001-20000=4

> Rs.20000 =3

Hypotheses of the study: Against the background of the research objectives cited above and the review of literature, the following hypotheses were set:

1. All the assumed factors are not equally important �� ]!�� <��regard to People of Tata Cellular Services.

2. All the assumed factors are not equally important �� !��!�� <��regard to People of Tata Cellular Services.

�$� "�� >�� <�� performed by Tata Cellular Services.

Collection of data and its analysis: The research has made use of both secondary and primary sources of data. A questionnaire was designed to collect primary while journals, magazines, reports and websites were used for collecting secondary data. Factor analysis test is used for the analysis of this data collected.

Limitations of the study: The limitations of the study are: 1. The period of the study was restricted to 2010. �$� �� !�� =�� <�� >�� 100 customers only even though there are 1.3 lakhs of customers scattered around the Chittoor district. 3. The customers hesitated to disclose the complete information on their satisfaction with Tata Indicom as they felt that the researcher was the representative of ��!��$�[��`��have been more realistic and strategic in its application.

Factor analysis:I. Factors determining the expectation levels of customers towards People: L�� was applied on opinions expressed by the respondents and to group together variables that are highly correlated (Nargundkar 2005). This tool is most appropriate in this context in the sense that it can explicitly extract the ��the expectations and perceptions about the employees of Tata Cellular services. This analysis involves three ��!�� >��`� �]�� matrix, secondly, deciding how many factors are to be interpreted, and thirdly, rotating these retained factors. The Eigen value or the total variance explained by a factor represents the sum of the squares of the factor loadings of each variable on that factor. It indicates the relative importance of each factor in accounting for the particular set of variables being analyzed (C.K. Kothari 20105�� #�� #�� %�D� �� #�� #��'DY�� .

�� $$�� are estimates of amount of variance each variable accounts for and shares with all other variables included in the factor analysis. ��<��the given factor have very high association among themselves and vice versa.

�� $�� :��Z Measure of sample adequacy such as @��+� �� ;�� (Chi-Square is approximately 133.416 for 105 dof at 0.032% �� >��^� �� [��*>��*O8��4[>O5� �$��#� � �:�� was used to measure sampling adequacy (table 3). The KMO index ranges from 0 to 1, reaching 1 when each variable is perfectly predicted without error by the other variables.

Table 3: Tests of sample adequacyi. Kaiser – Meyer – Olkin

Measure of Sampling 0.428��$� [��!�� Chi – Square 133.416

Dof 105��>�� 0.032

The KMO test interprets that the sampling data is 42.8% adequate for this factor analysis and thus falls in the meritorious range of 0.8 and above (Hair ��) 1998).


Since the number of factors that are extracted and summaries have been prepared and presented in table 4 and table 5.

Findings: Through SPSS the principal components analysis with Varimax Rotation were used for the sample for generating and extracting factors as a result of which seven factors (with Eigen value of more than 1.0 which is ��>��^��]!��>��${�}�`��<��$

Table 4: Summary of the results of Factor analysis explaining the expectations of buyers of cellular services towards People

!�� 4�)��)��)��)��)��)�� 5�� %\��4��$�� $�%��%\��:�� $�� 5

Loadings of

��

Commu-��

/��#�� #��

Factor I:Assurance:7. Knowledge of executive on the

problem.0.726 0.573

Variance explained =10.117%Eigen value = 1.841No. of variables = 3

8. The customer support representative was courteous.

0.630 0.594

6. �>��=��$ 0.501 0.645Factor II: Professionalism:

3. Helps to know the cause and gives solution.

0.816 0.725Variance explained= 9.771%Eigen value =1.710No. of variables = 4

14. Gives importance to the customer values and maintains long-term relations with them.

0.630 0.682

4. Handles issues with professionalism. 0.094 0.66115. Having technical knowledge. 0.078 0.605

Factor III: Transaction abilities: 5. Ability to complete transaction. 0.555 0.572 Variance explained = 9.228%

Eigen value = 1.397No. of variables = 1

Factor IV: Responsiveness:12. Responsive to special needs. 0.587 0.742 Variance explained = 9.209%


Factor V: Problem resolution:11. Resolves mistakes quickly. 0.722 0.658 Variance explained = 8.916%


9. [��!��issue or need.

0.366 0.573

Factor VI: Co-operation and knowledge: 2. Representative appears knowledgeable. 0.737 0.565 Variance explained = 8.361%


13. Future co-operation given by Indicom’s executive.

0.460 0.534

1. ��=��>��!��$ 0.265 0.682Factor VII: Promptness :

10. Representative responds in a timely manner.

0.865 0.781 Variance explained = 8.343%Eigen value = 1.025No. of variables = 1

Total variance explained : 63.945% Total no. of variables : 15Source: Primary data

Naming of factors and discussion: The seven extracted factors have been given appropriate names on the basis of variables represented in each case as shown in table 4. These factors representing values of service expectations by Tata Indicom customers have been discussed below:

L�� R�Z� "��Z� The total variance explained in table 3 reveals that this factor has explained a highest variance of 10.117%. Three variables were loaded on this factor. The researcher has named ‘assurance’, and it includes knowledge, courtesy, and ��>�� =�� <�� factor loadings such as 0.726, 0.630 and 0.501 respectively on its attributes. These three attributes have high communality indicating that the attributes within the factor 1, have high association among themselves. It could be concluded that these attributes are powerful and strong that determines customer satisfaction.

L��R� ��Z�/��$Z It is the second most important factor with explained variance of 9.771%. ��>�� >�� factor that includes help to know the cause and to give solution, importance to the customer values and long-term relations with them, professionalism, and technical knowledge with higher factor loadings such as 0.816, 0.630, 0.094 and 0.078 respectively as �� the decision of mobile service customers while choosing a particular service provider.


L�� R� ��Z� �� Z� O��one variable was loaded on this factor and accounted for 9.228% of the total variance with a factor loading of 0.555. The higher value of communality for the attribute indicates that higher amount of variance is explained by the extracted factor.

L�� R� ��Z� !��Z The variable loaded on this factor accounted for 9.209% of variance. The higher factor loading 0.587 indicates that the factor 4 underlines the above variable It could be concluded that responsive to special needs plays a successive role which determines customer satisfaction.

L��R��Z�/��$��Z�Two variables were loaded on this factor, and together they accounted for 8.916% of the variance. It includes skills for resolving mistakes quickly and being helpful and his ability to resolve issue or need with factor loadings of 0.722 and 0.366 �� #�� $$�� that higher amount of variance is explained by the extracted factors and association between themselves.

The attribute skills for resolving mistakes quickly plays a leading role in gaining customer satisfaction and the attribute helpful and his ability to resolve issue or need has shown least leading role.

L��R��Z�?�*��Z�This is the sixth factor, which accounts for 8.361% of the variance for the three variables, such as knowledge, cooperation and quick diagnosis of the problem with factor loadings 0.737, 0.460, and 0.265 respectively. However, it �� =�� >�� problem ‘has shown least leading role.

L�� R� ��Z� /��$��Z� There is only one variable loaded on this factor and it accounted for variance of 8.343%, and a factor loading of 0.865 which are relatively

very high when compared to all other factors and variables. Its high communality 0.781 indicates that the timely response of the company representative is important and plays a successful role that determines customer satisfaction.

II. Factors determining Perception levels of customers towards people:�#�� !�� [�� sphericity (approx Chi-Square is 394.830, degree of freedom is ��`��>��$��^��#��_=��Z�#_^��Z�$��^��<��<��>��$

Through SPSS the principal component analysis and Varimax method were used for extracting factors and six factors (factor loadings for each of 15 sub-factors i.e., variables under study is more than 0.48) with an explained variance of 70.22%, and Eigen values of more than 1 were retained. Table 5 shows all ��>��<��`��names of the factors and their loadings.

Table 5: Summary of the results of factor analysis explaining the perceptions of buyers of

cellular services towards People

!�� 4�)��)��)��)��)�� 5�� %\��*��)��4��$�� $�%��%\�� $�� :��5

Loadings of

��

Commu-��

/��#�� #��

Factor I: CRM:

13. Future co-operation given by Tata Indicom.

0.889 0.815Variance explained =23.445%Eigen value = 3.517No. of variables = 4

14. Gives importance to the customer values and maintains long-term relations with them.

0.854 0.820

3. Helps you to know the cause and gives solution.

0.498 0.640

9. Representatives on being helpful and his ability to resolve your issue or need.

0.486 0.533

Factor II: Promptness:

��$� �>��=��$ 0.879 0.843 Variance explained = 12.190%Eigen value =1.829No. of variables=3

11. Resolves mistakes quickly. 0.765 0.655

10. Representative responds in a timely manner.

0.548 0.642

Factor III: Knowledge:

12. Responsive to special needs. 0.812 0.714 Variance explained = 10.453%Eigen value = 1.568No. of variables = 3

7. Knowledge of representative on our problem.

0.789 0.714

�$� ��=��>��!��$ 0.533 0.698


!�� 4�)��)��)��)��)�� 5�� %\��*��)��4��$�� $�%��%\�� $�� :��5

Loadings of

��

Commu-��

/��#�� #��

Factor IV: Professionalism:

4. Handles issue with professionalism.


5. Ability to complete transaction. 0.717 0.685

Factor V: Appearance:

2. Representative appears knowledgeable.

0.839 0.749Variance explained = 8.063%Eigen value = 1.210No. of variables = 1

Factor VI: Technical knowledge and courtesy:

8. The customer support representative was courteous.


15. Having technical knowledge. 0.625 0.569

Total variance explained: 70.22%

Total number of variables: 15

Source: Primary data

Naming of factors and discussion: The six factors named in table 5 are explained below:

L�� R� %Z� ?!>Z This is the most �� <��high Eigen value and total variance. The co-operation given by representatives, importance to long term relations with customers, customer values, helping the customers know the cause for the problem and giving solution, and being helpful to resolve your issue or need contributed with higher factor loadings of 0.889, 0.854, 0.498 and 0.486 respectively with an explained variance of 23.445% and with high communalities ranging between 0.815 and 0.533 indicating that the attributes within this factor have very high association among them. Hence it should be given top priority to be monitored.

L��R�CZ�/��$��Z With an explained variance of 12.190% ��>��=��`��=��of issues, representatives responding timely with high factor loadings such as 0.879, 0.765, and 0.548 respectively and with high communalities indicating that the variables within factor 2 are closely associated.

L��R�FZ�[�� #�Z It accounted for 10.453% of variance and three variables namely responsiveness to special needs of

the customers, knowledge on customer’s !��`� �� =� ��>�� same contributed by 0.812, 0.789 and 0.533 respectively. The attributes responsive to special needs and knowledge of representative on our problem play a leading roles in gaining customer satisfaction and the �� =�� >�� !�� shown least leading role.

L��R�KZ�/��$Z Two variables were loaded on this factor and together they accounted for 9.003% of the variance with such high factor loadings as 0.766 and 0.717 respectively. The higher value of communality for the two attributes indicates that higher amount of variance is explained by the extracted factors. It could be concluded that the above attributes are powerful and strong variables which determines customer satisfaction.

L��R�\Z�"��Z There is only one variable, namely the courteousness of the company executive, loaded on this factor and it accounted for 8.063% of variance with a factor loading of 0.830 indicating that this factor plays a successive role in gaining customer satisfaction.

L�� R� 'Z� �� 8�� #�� Z� With a total variance of 6.868% two variables, namely the courtesy of the customer support staff, and their technical knowledge with factor loading 0.819 and 0.625 respectively were loaded on to this factor. Their communalities indicate that their courtesy plays a leading role in gaining customer satisfaction but their technical knowledge has shown the least role.

Measurement of buyer’s satisfaction: �� >��response. Failure to meet the needs and expectations is assumed to result in dissatisfaction with the product or service, �� Z�� [�� ^$�


The mean scores for expectations as well as perceptions, and the gap between these two for all the given variables are calculated �� >�� <�� !��!�� with the Tata Indicom cellular services (Table 6).

Table 6: Mean scores, gaps and satisfaction on 15 statements of Gap analysis on Tata Indicom’s

sales and service executives

Sl. No. Attributes Perception

Means (P)Expectation Means (E)

Gap(P –E)

% of satisfaction

2. Representative appears knowledgeable

1.980 2.030 -0.05 97.536

1. ��=�� >�� problem

2.070 2.260 -0.19 91.592

5. Ability to complete transaction

1.950 2.350 -0.4 82.978

8. The customer support representative was courteous

1.950 2.350 -0.4 82.978

3. Helps you to know the cause and gives solution

2.010 2.470 -0.46 81.376

7. Knowledge of representative on our problem

1.780 2.190 -0.41 81.278

6. �>��=�� 1.780 2.270 -0.49 78.4144. Handles issues with

professionalism2.100 2.710 -0.61 77.490

9. Representatives on being helpful and his ability to resolve your issue or need

1.810 2.450 -0.64 73.887

15. Having technical knowledge

1.900 2.610 -0.71 72.796

10. Representative responds in a timely manner

1.850 2.840 -0.99 65.140

11. Resolves mistakes quickly 1.660 2.630 -0.97 63.11712. Responsive to special

needs1.850 3.080 -1.23 60.064

13. Future co-operation given by Tata Indicom

1.870 3.550 -1.68 52.676

14. Gives importance to the customer values and maintains long-term relations with them

1.720 3.380 -1.66 50.887

Note:� �$� |�!��>��!�� ]!��$ 2. A positive gap indicates that customers perceived that service

delivery exceeded their expectations; while a negative gap indicates that service delivery did not meet their expectations.

Graph 1: GAP ANALYSIS

On the basis of mean scores calculated, it was found that customers have ranked top


the attribute ‘representatives appears knowledgeable’ <�� {�� =� ��>�� the problem by employees with 91% satisfaction. This is followed by the ability to complete transaction, courtesy of customer support executive, ability to help customer know the cause of and solution for the problem, and the thorough knowledge of the executive on the problem with 82.978, 82.978, 81.376, and 81.278 respectively. On factors 6, 4, 9, 15, 10, 11, and 12 the consumer derived satisfaction ranging between 78% and 60%. The future cooperation and respect for customer values accounted for the lease satisfaction levels such as 52.676% and 50.887% respectively. This is shown in radar graph 1.

Acceptance or rejection of the formulated hypotheses�� [�� tools used only two hypotheses were accepted and the third one is rejected as shown in table 7.

Table 7: Acceptance/rejection of the formulated hypotheses

S. No. M�� "��9�!�(��

1. All the assumed factors are not �� !�� the customer’s expectations with regard to People of Tata Cellular Services.

Accepted (Table no.4)Tool used for testing:

Factor analysis

2. All the assumed factors are not �� !�� the customer’s perceptions with regard to People of Tata Cellular Services.

Accepted (Table no. 5)Tool used for testing:

Factor analysis

3. "�� >��with the services performed by Tata Cellular Services.

Rejected (Table no. 6)Tool used for testing:

Mean gap analysis

Suggestions on people mix: Suggestions have emanated from the sample respondents in the course of various interviews conducted in this research work. In addition, the researcher has also gathered a number of suggestions from various experts on the subject �� >�� >�� in Chittoor district of Andhra Pradesh. The researcher

also had arrived at his own suggestions to overcome a number of problems that are discussed earlier and to provide maximum level of satisfaction to the sample respondents. All these suggestions have also been incorporated under the following headings indicating the nature of these suggestions.

%�� G�� $�� H%*&HY� 4��#��5Z It is concluded from the gap analysis table 6 that there is a slight negative gap between perceptions (1.980), (2.070) and expectations (2.030), (2.260) for the attributes 2 and 1, namely ‘representative appears =��<�� =�� >�� !��with 97.536% and 91.592% of satisfaction. This is said to be the highest satisfaction zone.

And it is also evident from the same table that there is a little negative gap in between perceptions (1.950), (1.950), (2.010) and (1.780) and expectations (2.350), (2.470), (2.190) and (2.190) for the attributes 5, 8, 3, and 7 with 82.978%, 82.978%, 81.376% and 81.278% of satisfaction respectively. This can also be taken granted as the higher satisfaction zone.

Hence it is suggested to that the Tata Indicom should maintain the same level of performance in order to retain as loyal customers in the medium and long run. Otherwise it may have to lose these customers to Airtel, and Vodafone. Advertising by celebrities is very much essential for this purpose.

C�� G��$��'D*7&Y�4$� ��$��5Z It is evident from table 6 that there is a moderate gap in between perceptions (1.780), (2.100), (1.810) and (1.900) and expectations (2.270), (2.710), (2.450) and (2.610) for the attributes 6, 4, 9, and 15 with 78.414%, 77.490%, 73.887% and 72.796% of satisfaction respectively. This moderate negative gap indicates that customers perceived that service delivery did not meet their expectations completely. The Tata Indicom should make a focus on internal marketing such that the service representatives are equipped with sound �� =��<��`� ��`� ��>��`� ��!��and professionalism because they have to handle different types of customers effectively. Executives should inculcate the ability to view the experience from newer perspectives.


It is also observed that there is a negative gap in between perceptions (1.850), (1.660) and (1.850) and expectations (2.840), (2.630) and (3.080) for the attributes 10, 11, and 12 with only 65.140%, 63.117% and 60.064% of satisfaction respectively. This negative gap which is considered to be more than moderate indicates that customers have perceived service delivery far less than their expectations. Hence it is suggested that the Tata Indicom should not only give rigorous training to services executives but also should see that the front end employees are given authority to solve the problems quickly and timely instead of resorting to red tape and buck passing. As well they should be trained for their high-tech (technical quality) and high-touch (functional quality) during their interactive marketing during delivering the service. Executives should also be given empathy training to improve their emotional competence.

3. G�� $�� \\Y�4��5Z It is concluded from the gap analysis table 6 that there is a high negative gap between perceptions (1.870), (1.720) and expectations (3.550), (3.380) for the attributes13 and 14 with only 52.676% and 50.887% of satisfaction respectively which are said to be the least levels of satisfaction. As well these two factors are in the gambit of high Eigen values of more than 1 and 3 for expectations and perceptions respectively and total variances of more than 8 and 23 respectively for the ��!��$�[��these are either totally neglected or not perceived well by customers. Hence the Tata Indicom should focus on CRM, the lifeblood of service organizations, while inculcating the culture among executive to give utmost respect to the values of the customers.

Conclusion: ��will enable mobile companies to enhance customer services and reduce the customers’ mobile usage costs. Gap analysis between expectations and deliverables should be carried out regularly to measure and monitor the current levels of satisfaction for customers on various factors and sub-factors and to know what the pluses and minuses of the mobile company are, so as to improve the factors on which it is losing out.

Scope for further research: The scope of the study can be extended to large samples across India in order the survey results to be more reliable and comparative studies can be conducted to know the expectations, perceptions and satisfaction of the consumers towards cellular services offered by established brands.

Acknowledgement: We, three, together acknowledge Dr. K.V.S. Sharma, Professor, Dept. of Statistics, Sri Venkateswara University, Tirupati, Andhra Pradesh for his professional guidance in the use of factor analysis in this area of services marketing.

References:1. Mamilla Rajasekhar, A.N.V.Durga Anupama and M.Muninarayanappa (2011), “Expectations, Perceptions and satisfaction of cellular service consumers on 7Ps of service marketing”, ;$��of Business Management Studies, Vol. VII, No.1, pp. 1 – 12.

2. Paikar Aposva (2004). Determinants of customer satisfaction for cellular service providers. q ��#�/��#��) Vol. 28, No. 1: January – March.

3. Shirshendu Ganguli (2007), “Drivers and Effect of Customer Satisfaction and Other Factors on Churn Among Indian Cellular Services Users”, The Icfai University Journal of Marketing Management, Vol. V, No. 3: p.p. 7 – 17.

4. Girish Taneja and Neeraj Kaushik (2007), “Customer’s Perception Towards Mobile Service Providers: An Analytical Study”, The Icfai University Journal of Marketing Management, Vol. V, No. 3: p.p. 39 - 52.

}$� ��<��[��$��$�"��retention management processes. ��Marketing. Vol. 40, Nos. 1&2: p.p. 83-99.

�$� "�� "�� $� ��{$�Satisfaction and loyalty: Customer perceptions of Malaysian Telecommunication service providers. ��>��8��#�Vol. VII, No. 1: pp. 6-18.


7. Girish Taneja and Nearaj Kaushik. 2007. Customer’s perception towards mobile service providers: An analytical study. Journal of Services Marketing. Vol. V, No. 3: p.p. 39-52.

8. Shirshendu Ganguli. 2007. Drivers and effect of customer satisaction and other factors on churn among Indian Cellular Service users. Journal of services Marketing, Vol. V, No. 3, p.p. 7-17.

9. Soderlund, Magnus. 1998. Customer satisfaction and its consequences on customer Behaviour Revisited. International Journal of Service Industry Management. Vol. 9, No. 2: p.p. 169-188.

10. Zeithaml VA and Bitner MJ. 2010. Service Marketing. International Edition, New Delhi: MC Graw Hill.

11. Kim H (2000), “The Churn Analysis and Determinants of Customer Loyalty in Korean Mobile Phone”, Korean Information Society Review, p.p: 1 – 18.

12. Debarati Chaterjee and Ishita Chaudhuri. Aug 2010. A study of market share and factors affecting

the choice of cellular service provider among young age group of Kolkata. Vol. IX, No.3: pp.41-56.

13. Gerpott T, Rams W and Schindler A. 2001. Customer retention, loyalty, and satisfaction in the German Mobile Cellular Telecommunications Market. Telecommunication policy. vol 25, No. 4: pp. 249-269.

14. Kim Moon-koo, Park Myeong – cheol and Jeong Dong- Heon. 2004. The Efforts of customer satisfaction and switching barrier on customer loyalty in Korean Mobile Telecommunication Services. Telecommunications Policy. Vol. 28: p.p. 145-159.

15. Malhotra Naresh K. 2004. Marketing Research. New Delhi: Pearson Eductation.

16. Nargundkar Rajender. 2005. Marketing Research Text and Cases. New Delhi: Tata Mc Graw Hill.

III. Websites:1. www.tataindicom.com

3. <<<$>��$��

4. www.managementparadise.com


Increasing Bottom-Line Through Six Sigma Quality Improvement Drive: Case of Small Scale Foundry Industry

* Department of Mechanical Engineering, G. H. Patel College of Engineering and Technology (GCET), Vallabh Vidyanagar 388 120, Anand, Gujarat, India E-mail: [email protected]

�� #�� #�� $��)� �� )��$��#��)��)�� $��8��#9��$��#�� 8��#��$��$��:�� $��"$��#�� :�� $��$��:��)�;��;�#$��$��#� �� $�� 8��#�� $��$��#�� $��#�:�� )��$�� ;��;�#$��#�� $��(��#�� ;�� ;�#$�� #�;��;�#$��$��*��(��#�� <��=��*>��*"��<�*�$��*?�� 4=>"�?5)� �� $�� $*�� #��:��$��$��;��;�#$��

Keywords:��̀ �[��`��]��`�~�>��#��Analyse-Improve-Control (DMAIC), Small-scale industry, Foundry industries, Indian industries.

Darshak A. Desai *

IntroductionIn the era of globalization, producing better quality castings as per the international standards needs multidirectional competitiveness. Indian foundry industries are continuously on the alert to gain a competitive edge. Yet, the focus is deviating from overall operational excellence. Meeting customer demands <��>��`��<��]��them through quality and productivity improvement. To compete globally, foundry men have to move ahead from the slogan of “Satisfying Customer” and adopt and rigorously endeavor for “Customer Delight.” Gaining competitive edge is the constant quest for the Foundry Industries world wide. For that they are using many tools and techniques that have long been ��<��!��$�~��!��innovative ways of producing castings and providing services, there remain one basic constraint, that is, organizations that produce better quality castings and services than their rivals beat the competition time and again. For global competitiveness, foundry industries are trying many techniques such as Quality Circles, ��#��Z��#^`��_�"��>��etc. All these techniques are well capable of producing the desired results but the darker side of the coin is the issues related with their implementation and longer ��!�� &�� >��$�#��`� ��!��>�!��$�[��hour is to strike global optima and not to waste time, ��>��!��$��men thus need a break through strategy, which can have ��>��$

To compete globally the basic commitment of foundry men should be to continuously pursue overall organizational excellence. Meeting the customers’


requirements at minimum possible cost and time is the main mantra for success for any sort of business. To put it in more technical terms, achieving functional requirements of the product at competitive cost <�� !��>�� !�� organizational success. And that comes through overall operational excellence.

Six Sigma is one such technique available to bring the breakthrough improvements almost in every sector through overall operational excellence. The pinpointed attack of this technique on root causes, guarantees the targeted results, both in terms of improvements desired �� !�� >]��$� [�� !�� Z�� al, 2008) is it possible to achieve six-sigma quality in *��>��`��!!��absurd. It is common knowledge that rejections can be as high as 20-40% at casting stage, 10-20% at rough ��`��}��>��$�This is especially true for entirely new castings being ��!��>��$

This paper is an attempt to introduce Six Sigma as an effective quality improvement drive to the foundry industries, especially small scale jobbing foundries. The paper discusses the phase wiz implementation of DMAIC of three critical castings produced by the small jobbing foundry. At the end, it also illustrates the �� >��!!��Six Sigma.

2 Literature Review2.1 Six Sigma - an overview

�� >�� #��`� �� ]!�� >��]��<��<��$��=�� Z�{{�^� ��>�� ]� �� !��aimed at the near elimination of defects from every products, process and transactions. Harry (1998) ��>�� ]� �� !��>��`� �� =�� !��customer satisfaction through statistical tools that can lead to breakthrough quantum gains in quality. Carey (2007) stated that the essence of Six Sigma is found in the reality that business processes are inherently unpredictable. Six Sigma provides a way of measuring

the variability in a process as it delivers services to an end-user or customer. When most people talk about Six ��`� �� =�� ~#��"� Z��>��measure-analyze-improve-control) methodology. This method is used for improving an existing process when it is not meeting customer needs. As per Shahin (2008) the corporate framework of Six Sigma embodies the >�� !�� `�training schemes, project team activities, measurement system and stakeholder involvement. Stakeholders include employees, owners, suppliers and customers. At the core of the framework is a formalized improvement �� <�� <�� >�� !�� >��`�measure, analyze, improve and control (DMAIC). The improvement strategy is based on training schemes, project team activities and measurement systems.

��!��%��=��Z��^��>��of Six Sigma states that it blends correct management, >�� =��improvement to process and products in ways that surpass other approaches. While as per Park (2002) Six Sigma implies three things: statistical measurement, management strategy and quality culture. It tells us how good products, services and processes really are, through statistical measuring of quality level. It is new management strategy under leadership of the top management to create quality innovation and total customer satisfaction. It is also a quality culture. It !��<��>��work smarter by using data information. It also provides an atmosphere to solve many Critical to Quality (CTQ) problems through team efforts. Six Sigma is much more than just a statistical approach to problem solving. It is a company-wide initiative to improve both top line and bottom line through sustained customer satisfaction. The entire movement is driven by the voice of the external customer and concentrates on what is really ��!��Z��`��^$

Six Sigma is a business system with many statistical ��!��`� �� >�� most companies. It is an operational system that speeds up improvement by getting the right projects conducted in the right way (Lucas, 2002). It is a business ��!��!!��=��>��


causes of mistakes or defects in business processes by focusing on process outputs that are of critical importance to customers (Snee, 2004). Linderman et ��$� Z��^� ��>��]��!��!��improvement and new product development by stating that Six Sigma is an organised and systematic method for strategic process improvement and new product and service development that relies on statistical methods ��>��=��>��$

Snee (2004), by putting thrust on customer driven aspects of Six Sigma, says that it is a business improvement �!!�� =�� >�� mistakes or defects in business processes by focusing on process outputs that are of critical importance to customers. While as per Antony et al. (2005) Six sigma provides business leaders and executives with the strategy, methods, tools and techniques to change the culture of organizations.

Six Sigma is a holistic approach to achieving near perfection, expressed in terms of no more than 3.4 errors per million opportunities. This near perfection appears to many to be overkill or to some, an impossible ideal. Nonetheless, Six Sigma has been adopted by many ��!��$��>��<��for manufacturing industries and increasingly, in service industries (Wright and Basu, 2008).

Some of the recent literatures provide further detailed view of Six Sigma, for example, as per Tang et al (2007) Six sigma is a systematic, highly disciplined, customer-�� !��>�� &��<�� business improvement initiative that is based on a rigorous process focused and data-driven methodology. While as per Desai (2008) Six Sigma methodology addresses the major root causes and guarantees the targeted results, both in terms of improvements desired �� !�� >]��$� �� !��`� ��approach and methodology for eliminating defects in any process – from manufacturing to transactional, from products to services. This breakthrough improvement �� !��`� !��>��and quality improvements based on its highly effective approach. It drives customer satisfaction and bottom-line results by systematically reducing variation

in processes and thereby promoting a competitive advantage (Antony and Desai, 2009).

Six sigma as a philosophy seeks to measure current performance and determine how desired or optimum performance can be achieved. Any deviation in the performance of any critical-to-quality characteristic may be considered a defect (Eckes, 2001). Technically, sigma 4|5� is a statistical measure of the quality consistency for a particular process/product. The technical concept of Six Sigma is to measure current performance and to determine how many sigmas exist that can be measured from the current average until customer dissatisfaction occur. When customer dissatisfaction occurs, a defect results (Eckes, 2001).

2.2 Six Sigma and Indian industries

Before and after industrial reforms, Indian industries have undergone many waves of transformation. Initially, the focus was on large-scale public and private sectors, mainly in the core infrastructural production. The main thrust was on productivity based on Taylor’s !��!��>��$��<��major area of concern. Along with productivity, quality also emerged as major area of concern after industrial reforms, that is, globalizations and liberalization. For competing in the global market, overall operational excellence becomes the necessity for the Indian industries to remain globally competitive. Indian industries are becoming more competitive. There has �� >�� <�� `� ��!��in manufacturing, Information Technology (IT) and IT-enabled services. The manufacturing sector has been evincing a keen interest in improving productivity and customer responsiveness through a number of initiatives (More et al., 2008). Six Sigma is the benchmark of world-class organization. Many Indian industries have successfully exploited this breakthrough business ��!�� >��$� ��the penetration Six Sigma in Indian industries is not as encouraging as it should be (Desai, 2006a).

Nothing comprehensive is published regarding status of Six Sigma among Indian industries as a whole so far. Khanna et al. (2002) highlighted the survey results and analysis of TQM status and quality tools being followed


by Indian automobile sector. The survey focused on 23 key quality improvement drives. The weakest among these is Six Sigma. Only 5% of the respondents in Indian automobile organisations claim to follow Six Sigma. The foremost reason for low implementation of Six Sigma in Indian automobile sector has been ��>�� =� �� !� �� $�70% of top management teams have not shown interest in implementation of Six Sigma approach because of high cost involved in training the people for green belt, ��=��=��$�� >��to automobile sector only. The scenario at industry as a whole, especially, foundry industries, is the same. Kumar et al. (2002) have provided the exhaustive list of various quality management techniques which the Indian industries are following at present. The list of techniques is divided in to four categories such as management techniques, analytical techniques, idea generation techniques and data collection techniques. It is observed that application of Six Sigma problem solving approach is very less. This shows immediate need to make Indian industries aware of Six Sigma business improvement strategy by removing the principal misconception about it, that is, it is highly mathematical and costly to implement.

Foundries, and indirectly, their customers, continue to pay a heavy price for poor quality. The immediate fallouts include loss of productivity (saleable castings per poured metal), and the cost of cutting and re-melting rejected castings. Defective castings supplied to the customer lead to wasted machining cost, and may have to be recalled (which involves avoidable transportation cost), or repaired (high labour cost). On the other hand, six-sigma quality is regularly being aimed at and nearly attained in many sectors of manufacturing, including semi-conductors, forging, plastic injection molding, and sheet metal stampings. Defect levels of 100 parts per million (ppm), that is, 0.1% rejections are within reach in pressure die-cast parts during regular !��Z��`��^$

It appears from the researches and surveys conducted and published so far that Six Sigma is not being explored by the developing economies to its full potential and Indian is not an exception in this (Desai and Patel, 2009). Many Indian industries have successfully exploited

this breakthrough business improvement strategy to ��>��$��!��]��Indian industries is not as encouraging as it should be. There are certain issues preventing the full use of Six Sigma as an overall business improvement strategy by Indian industries (Desai and Patel, 2010). And there are certain advantages too for Indian industries, which can be further strengthened to have an edge in the global market by effectively utilizing Six Sigma strategy (Desai and Patel, 2009).

2.3 Small Scale Industries in India

�� Z��^� �� !�� >��role in the economic development of the countries like India. SSI sector is strategically placed in the industrial population of the country and in the global economy as a whole. Their contributions in the Gross Domestic Product (GDP), employment generation as well as in exports are quite considerable. This is one of the fastest growing sectors of Indian industrial population (Desai, 2008). In India, the small and medium enterprises are not <��>��$��!��!��Bank of India has recently recommended that the units with investment in plant and machinery in excess of SSI ��!��$��Z �`��{`��{$��^��treated as medium enterprises EXIM Bank (2005). In ��`��>��!��$��Z ��`{��${�^�Z%��`��}^$

It is evident form the literature published so far that small and medium enterprises of Indian industries needs immediate introduction of Six Sigma methodology. Foundry industries, to become globally competitive, are not an exception. The process improvement methodology, that is, DMAIC can be applied to foundry processes to improve quality of the castings and thus reduce cost which can have ultimate impact on the bottom-line results. To introduce Six Sigma to small foundries, the pilot project, if possible, may be undertaken with simple tools and techniques, so that the misconception that Six Sigma involves heavy statistical tools and techniques can be removed from the minds of small scale foundry men. The next phase can be more meticulous application of DMAIC with superior tools and techniques on couple of core areas. This can be a continuous improvement process.


3 Application of Six Sigma in Foundry Industry: Case Explanation

3.1 The Company

�� >�� casting unit falling under small and medium scale enterprises (SMEs) category. The foundry is fully geared to meet the entire range of requirements of casting of various industries like Cement, Mining, ��>�� ¡� ��`� �� *��Engineering industries in India. They have started �]!�� >�� <��recently shipped to Colombo, Sri Lanka. Apart form �� `� �� >�� expanded its manufacturing activities. The Company was expanded to handle Manganese steel, Ni-hard, and ��<�¡��]�� `� "��`� �� <��$� �� !��`� ��this expansion, is 1500 tones per annum. The expanded facility have state of the art brand new centrifugal and static casting manufacture plant with machining, ��=��>��$��!�� >��condition.

3.2 Project objectives

The foundry in question is producing large variety of castings and on investigating the rejection rates of different products it was found that following three products are having heavy rate of rejection.

1. Cooler plate2. Hammer3. Nose –ring segment

Of late, the company has observed heavy rejections in this product and thus losing customer satisfaction due to related implications attached with rejection of the product. The company was trying to dilute these problems, through different trial and errors but <�� >�� <�� $��!�� rejections, these products are also having major share ��!��>��!��$��`�these products are selected for application of DMAIC

methodology of Six Sigma improvement drive. The �]�� >�� <�� !!�� basics of DMAIC methodology and they realized that the chronic problems being faced by them needs the �� ]��>�� !��`��>��`�>��root causes and then charting the suitable solution and in due course holding the gains. As the project need <�� >]��`� �� `� �� !�� !�� !��>��through quality improvement of critical castings, the project objectives could be summarized as below.

The prime objective is ‘to improve on rejection rate of three critical castings and thus improve overall bottom-��>��!��~#��"��of Six Sigma breakthrough improvement strategy’.

Along with this prime objective, following secondary objective are also considered:

�� to introduce the DMAIC methodology to the >�� !!�� !�� techniques.

�� =�� >��<��~#��"�!��solving technique, so that the same can be applied ��>��!��<��and techniques.

�� to create the guidelines for step-by-step improvements in sigma level for entire operations ��>��$

The following text explains the phase wiz application of DMAIC methodology on the problems in question.

<><�� ?�/��!��

This is one of the most critical phases of DMAIC ��$� �� !�� >�� requirements and links them to business needs. It also ��>��!��*��!�� =�� ]� ��$� �� >�� !��<�� !��>�� !��$��!��!��to envisage the scale and complexity of the problem. Following aspects were covered in this phase on the problems selected.


�� Fixing problem statement to work upon.

�� Critical to quality (CTQ) tree based on costumers’ needs and requirements.

Problem statements

�� !�� !��>� ��measurable, since it forming the base for improvement efforts. Based on the data collected regarding rate of production and rejections for the products in question following problem statement is formed.

}��$��(��?��)�M�$$�� *��#� �#$�� '�7'Y)� H�%'Y� �� %F�'Y��~

CTQ tree

Depending upon the nature of the problem, customers of the project in question are the ultimate customers who are receiving the products of the company and paying the bills. For identifying requirements of the customers, the critical to quality tree being the most ��`��`��<��>��$��will help to understand the critical quality requirements of the product. To improve the quality of the product, these CTQ need to be addressed.

For Cooler plate

For Hammer

For Nose-ring segment

Figure 1: CTQ tree for the castings in question

3.4 Measure phase

During this phase baseline sigma level is calculated and thus the current situation of the process is ascertained. Sigma level can be calculated by different methods, based on type of data. For discrete data Defects per Million Opportunity (DPMO) number is calculated and then from the DPMO-Sigma Level table current sigma level is ascertained.

DPMO =No. of Defects X 106

(no. of opportunities x no. of units)

Where…

No. of defects = No. of rejections (i.e. at least one defect exists to assign the product as defective)

No. of opportunities = No. of CTQs and

No. of units = No. of units produced.

The data for two quarters is collected for all the three products and present sigma levels were ascertained based on DPMO calculations.

For Cooler plate…

DPMO =125 X 106

(3 x 8050 )

=/>O��\%7\�&H

��~�#_��<��>��sigma level as K�%��#$��


For Hammer…



DPMO =175 X 106

(2 x 5160 )

=/>O��%'&\7�K

��~�#_��<��>��sigma level as F�7��#$��

For Nose - ring segment…



DPMO =155 X 106

(3 x 1015)

=/>O��\D&DF�%

��~�#_��<��>��sigma level as F�%��#$�.

3.5 Analyze phase

This is investigation phase. Here, course of action is created to close the gap between how things currently work and how they should work to meet improvement goals. All root causes are investigated and analyzed ��>]��!��$�For all the three castings, after rigorous brainstorming sessions with executives, supervisors and workers who are directly involved in the process a list of probable causes of defects were generated. These probable causes are then categorized as shown in the cause and ��>��$

Frequency of three CTQs of Cooler plate is as shown in >��$��<��}��<��analysis as shown below. This was done to reach up to the root of the problem.

1) Metal Defect

Why?Due to improper testing at the time of material acceptance.

Why?Urgency of material.

Why?Due to delivery commitments.

2) Pin Holes

Why?Due to porosity during casting.

Why?Due to air entering in mould during casting.

Why?Due to improper methods of pouring during casting.

3) Surface Damaged

Why?Due to low value of hardness.

Why?Due to improper cooling of material.

Figure 3: Chart showing frequency of various defects for Cooler plateFigure 2 : Cause-Effect Diagram of all the three castings


Further, multivoting and Pareto analysis were performed during this stage of analysis. Approach to multivoting is subjective. Here, the concerned executives, supervisors and workers were asked to vote each cause based on their understanding of the most probable to least probable root cause for the problem in hand. The causes were voted by the team on a scale of 1–5, with 5 as the most probable root cause for the problem on hand and ��!��$��Cooler plate is illustrated in Table 1. Figure 4 indicates Pareto chart based on average ranking after multivoting for Cooler plate.

Table 1: Multivoting result for Cooler plate

CAUSENO:

CAUSE AVERAGE RANKING

1 Complicated design 4.3

2 ~��<�� 2.5

3 Incorrect Methods 3.5

4 Poor maintenance 4.5

5 Lack of experience 2.2

6 Human error 3.0

7 Old Machines 1.5

Figure 4: Inidcates Pareto chart based on average ranking after multivoting for Cooler plate

These defects were also evaluated on 5- why analysis as shown below. This was done to reach up to the root of the problem.

1) Poor maintenance

Why?��$

Why?~��>��!��$

2) Incorrect Methods of handling the casting

Why?Due to improper layout.

Why?Due to lack of planning

3) Lack of accuracy

Why?Due to human error.

Why?Due to unskilled worker.

Why?Due to small training period.

4) Complicated design

Why?Due to abrupt change in cross-section.

Why?Due to customer requirement.

Frequency of two CTQs of Hammer is as shown in >��}��<$

Figure 5: Chart showing frequency of various defects for Hammer


These defects were then evaluated on 5- why analysis as shown below. This was done to reach up to the root of the problem.

@+� "��Z��/��Why?

Due to poor heat treatment.

Why?

Due to improper quenching.

Why?

Due to lack of oil bath stirring.

2) Metal Defect

Why?

Due to improper testing at the time of material acceptance.

Why?

Urgency of material

Why?

Due to delivery commitments.

Here also multivoting was performed as in the case of Cooler plate. Table 2 illustrates results of mutlivoting for Hammer. Figure 6 indicates Pareto chart based on average ranking after multivoting for Hammer.

Table 2: Multivoting result for Hammer

CAUSENO: CAUSE AVERAGE

RANKING

1 Complicated design 1.0

2 ~��<�� 1.5

3 Incorrect Methods 4.3

4 Poor maintenance 3.5

5 Lack of experience 2.0

6 Human error 3.0

7 Old Machines 1.5

Figure 6: Pareto chart of multivoting result for Hammer

Here also the critical defects were also evaluated on 5- why analysis as shown below. This was done to reach up to the root of the problem.

1) Incorrect Methodology

Why?Due to lack of knowledge.

Why?Due to lack of training.

2) Poor maintance

Why?��$

Why?~��>��!��$

Frequency of three CTQs of Nose-ring segment is as ��<��>��<$

Figure 7: Chart showing frequency of various defects for Nose-ring segment


These defects were then evaluated on 5- why analysis as shown below. This was done to reach up to the root of the problem.

1) Black spot

Why?Due to improper welding.

Why?Welding machine might not be working properly.

2) Cracks

Why?Due to nature of region to be welded.

Why?Due to complicated structure.

Why?Due to customers requirements.

Here also multivoting was performed like earlier cases. Table 3 illustrates results of mutlivoting for Nose-ring segment. Figure 8 indicates Pareto chart based on average ranking after multivoting for Nose-ring segment.

Table 3: Multivoting result for Nose-ring segment

CAUSE NO: CAUSE AVERAGE RANKING 1 Complicated design 2.02 ~��<�� 2.83 Incorrect Methods 3.24 Poor maintenance 3.55 Lack of experience 2.06 Human error 1.07 Old Machines 4.0

Figure 8: Pareto chart of multivoting result for Nose-ring segment

Here also the critical defects were also evaluated on 5- why analysis as shown below. This was done to reach up to the root of the problem.

1) Old Machines

Why?~��>��!��$

2) Defective Raw Material

Why?Due to low quality of materials used

Why?~��>��!��$

3) Incorrect Methods of Pouring

Why?Due to lack of advanced techniques.

Why?Due to lack of awareness.

3.6 Improve phase

��<��>��<��this phase. Here, processes and product performance characteristics are improved for achieving desired results and goals.

Improvement targets

As such the pilot Six Sigma project advocates 50% improvements in the quality of the product/process. ��=�� >�� of infrastructure, availability of funds and manpower, the improvement targets set for the Cooler plate, Hammer and Nose-ring segment are 40%, 35% and 40% respectively. These improvement targets turn out as 4.2 sigma, 3.8 sigma and 3.2 sigma respectively in terms of sigma levels.

Based on the analysis done in previous phase, following improvements were suggested for the three castings.

For Cooler plate:

�� <��=��1500c (presently done at 1000c) and method of


!��>��pouring. This will eliminate pin holes appearing at the manufacturer’s logo on the casting.

�� After every 50 castings produced there should be a material composition check to avoid defects.

�� For proper material handling mechanical roller conveyer should be used. Presently tractor is used for transportation and castings are dumped very roughly.

�� Proper technical training should be imparted to the workers in respective areas especially in moulding. They should be provided with good incentive schemes to stay motivated.

For Hammer:

�� #��>��be maintained around 1300c at time of quenching instead of 800c.

�� Gating system should be revised.

�� Insulating sleeves could be used for riser

design.�� Ceramic gating system could be used

�� Some stirring mechanism should be used at the time of quenching to allow uniform cooling gradually resulting in uniform hardness

For Nose-ring segment:

�� Welding with CO2��]��<��instead of simple Arc welding.

�� Pre heating of welding electrodes and product itself to remove moisture content.

�� Proper technical training should be imparted to the workers in respective areas especially in welding.

3.7 Control phase

The basic objectives of this phase are to ensure that our processes stay in control after the improvement solution has been implemented and to quickly detect

out of control state and determine the associated causes so that actions can be taken to control the problem before non-conformances are produced. Success in this phase depends upon how we did in the previous phases. In control phase, tools are put in place to ensure that the key variables remain within acceptable ranges over time so that process improvement is maintained. Depending up on type of the problem and operating system of the concern, the following control measures were recommended:

�� Periodic review of the various measures suggested in Improve phase.

�� Application Statistical Quality Control (SQC) charts, such as p-charts to check the quality of the castings and keep it under control, thus maintaining targeted sigma level.

4 Concluding remarks

The objective to introduce Six Sigma quality ��!�� >�� <�� achieved. Quality of three critical products was improved by reducing their rejection rates by application of DMAIC methodology of Six Sigma. The implementation of it resulted in understanding the problems from all aspects, qualitatively as well as quantitatively, and laying out the improvements through ��!��$�[��>��of Six Sigma are well reported in large organizations worldwide. But small industries, especially foundries are inherently capable of adopting Six Sigma as breakthrough strategy. They need to show the roadmap. ��!��>��achieved by the foundry in question by this initial effort of Six Sigma are attractive enough for them to deploy Six Sigma company-wide. Following text summarizes the overall conclusion of the study.

[>@� \��]��/��/��

By applying DMAIC methodology of Six Sigma improvement strategy, the foundry could improve quality by lowering the rejection rates in three critical castings. This resulted in to savings in rework time and ��*��$��>��!!��of Six Sigma are indicated in table 4.


Table 4: Overall gain by application of DMAIC methodology

Sr. No. Casting

No. of Rejection / YearAnnual

Financial Savings

Before DMAIC

application

After DMAIC application

1 Cooler plate 125 70 44 %

2 Hammer 175 90 45 %

3 Nose-ring segment 155 120 22.5 %

4.2 Limitations of the study

The project came across certain typical roadblocks such as, lack of proper documentations, poor traceability of past records, unavailability of technical details and records of the products like weekly or monthly rejection rates of particular castings, bill of materials, processing time etc. These inherent limitations were inevitable ��>��!��are of jobbing in nature. This made the work of data collection quite cumbersome and resulted in the tedious ��~�>��!��$�#��<��proper data collection initially making the contribution of time and efforts at other phases limited. However, ��<��>��are the backbone of success of DMAIC methodology.

��`� �� <�� >]��was done based on the discussions with chief of the operations and other involved in the process. This was purely based on their experience and perceptions. Brainstorming sessions need to be conducted for the same, but looking at number of root causes, time proved to be the major constraint. Apart from this, each root ��>]��]�� frequency and therefore its impact on the problem.

4.3 Future scopes

�� >]�� !��*�� be taken up separately one by one and their individual impact on the quality of castings can be further analyzed with tools such a as frequency distribution of occurrence of each root causes,

correlation and regression analysis and design of experiments

�� DMAIC methodology can be applied to other products/processes so as to reduce rejection rates and achieve higher overall quality and productivity.

�� !��~#��"��!!��castings involving more rigorous statistical tools and thus improving the sigma levels further.

Acknowledgements

I would like to thank the editors and the reviewers for their creative comments, which helped me to formulate this paper in better shape. I would also like to thank the concern where the subject study was undertaken.

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�� !��!��!��

* Professor, Bharathidasan Institute of Management Email:[email protected]

** Assistant Professor Bharathidasan Institute of Management, Email: [email protected]

Trichy, Tamilnadu, India.

��$�� $��$��emotional labour strategies such as surface acting and deep acting on performance in a public enterprise in India. This study has developed and tested a structural model to determine ��$��#��(��$��of service personnel in a public sector. The primary data was collected from customer service executives, assistant managers and managers of a public enterprise who interact with customers on a regular basis of a public enterprise through structured questionnaire. The emotional labour questionnaire was adopted from Brotheridge and Lee (2003) and Role Based Performance Scale instrument by Welbourne, Johnson and "$��<�4%&&H5��$� �� $��Based on the 304 responses, this study has produced two �� #�� L��)� �� SA (Surface Acting) and Job Performance, and DA (Deep Acting) and Job Performance. Secondly, the emotional labour ��#��#��#��(��$��#��

_��̀ �� Surface Acting, Deep Acting, Job Performance.

Dr P. David Jawahar *S. N. Raghavendra **

��There is no doubt that we are still labouring emotionally as much as ever in the workplace, faking our emotions or hiding a range of feelings that is felt. As of now it is impossible to imagine a workplace that does not require some form of emotional management on the part of employees. It is very important to regulate emotions at work especially for customer service employees to meet goals (Kanfer & Kantrowitz, 2002). There is no workplace where emotional labour isn’t needed. However, the terminology used in that particular organization could be different as what is called as emotional labour by some could labelled as compassion fatigue, professional face, rapport, emotional armour, engagement or empathy by others – all of which still refer to the same concept, that of presenting an acceptable ‘face’ to the organisation irrespective of one’s real feelings (Sandi Mann 2009). Over the last one and a half decades, research into emotional labour has burgeoned and there have been studies examining the concept amongst doctors, nurses, teachers, people working in the airline industry, magistrates, call centre staff, adventure guides, abortion clinic workers, customer service employees and those working in the tourist industry. Most of these studies have been concerned with emotional labour at the customer-organisation interface. (Sandi Mann 2009).

In this study, the researchers have tried to study the same phenomenon in a public sector in India. This was �!��>�� !�� <�� ]�� strategies of emotional labour helps in these service encounters. The culture prevailing in the public enterprises is very different from a private enterprise among the service personnel. As Kim & Cha (2002) ��>��`� !�� !�� !��


and strategic efforts to satisfy and retain their end customers. This study was carried out to identify if the service personnel in the public sector have felt the need to use emotional labour strategies, and if they are sensitive to the requirements of the customers to be effective in business. In India, the services provided in public sector in yesteryears were not considered to be good by the customer. It was not required of the service personnel to be pleasing to the customer even though it was a stated policy. If a customer who was not happy �� >��`� �� <�� !�� providing service. The reason for this sort of behaviour was that the individual could never be questioned by the organization as to why he never intended to satisfy the customer. This normally was also never a threat to his job unlike the private sector. In the private sector monitoring of the performance of the service personnel is also intense.

��>��under three major headings by Silvestro, Fitzgerald, Johnson and Voss (1992) based on the business processes. The service typology stated by Silvestro et al (1992) is professional service, service shop and mass service. The public enterprise chosen for this research falls under the mass service category. It is an organization where there are many transactions in a limited contact time and with very little customisation. The offering is mainly product-oriented with most �� =��>�� *�� !!�� >�� Z�$��bus service, postal, retail banking and fast foods). Having considered an organization in mass service ��!��>��emotional labour strategy i.e. deep and surface acting on performance in a public sector enterprise.

{>� ��\�Z��'�!��

Researchers in the area of Organization Behaviour of late have been studying the importance of emotions in everyday work life (Arvey, Renz &Watson, 1998; Fisher & Ashkansay, 2000; Lord, Klimoski & Kanfer 2002). Emotional labour or management of emotions in work role is a subject which has received a lot of

attention and focus in recent years. Theories stated by Grandey 2000 and Morris &Feldman, 1996 and empirical studies by Diefendorff & Richard, 2003 and Pugh, 2001 have suggested that management of emotions and emotional displays play a very important role in the success of many jobs.

{>@>� �� Emotional labour refers ��<��=��>��the organizational requirement of emotional display (Hochschild 1983). It has been found to be associated with various job and health outcomes ( Bono and Vey 2005; Zapf 2002 ).The relationship of emotional labour with employees’ work and family have also been focused of late as employees are not in a position to balance the demands of work and family roles (Greenhaus and Beutell 1985). Surface acting (i.e. faking the organizationally desired emotions) relates positively with work family interference (Montgomery et al. 2006 and 2005). Emotional labour refers to the regulation of emotion at work in accordance with the organizational goal (Grandey 2000; Hochschild 1983). Two major strategies involved in emotional labour management are surface acting and deep acting.

{>{>� �� Surface acting refers to the change of emotional expression without changing the inner emotional state. Every service organization has stated emotional display rules that frequently require employees to facilitate positive emotional expression (e.g. smile). If employees choose to adopt surface acting, they might try to simply fake a smile without actually experiencing the positive emotions. The response exhibited may not be congruent i.e difference in the actual felt emotion and the expressed emotion. The extant literature indicates that frequent use of surface acting is generally related to psychological ill-health, negative job outcomes, and burnout symptoms (Beal et al. 2006; Brotheridge and Lee 2002; Grandey 2003; Naring et al. 2006).The extant literature ��>�� !�� in surface acting could be possibly more when they �� >�� <��=� �� affective traits and individual goals. Lower team role performance exhibited by employees who engaged in ��>��$��


people engage in surface acting to create a desirable image (Hakan Ozcelik, 2005). The studies in recent �� >�� <�� acting and important organizational outcomes such as increased emotional exhaustion, reduced commitment, and lower performance (Grandey, 2003; Brotheridge and Grandey, 2002; Erickson and Wharton, 1997).

{><>� ?��!��Deep acting is the act of employees controlling both their internal thoughts and feelings to express the organizationally prescribed emotion (Brotheridge and Lee 1998; Grandey 2000). The extant literature helps us identify the use of deep acting as a strategy does not consistently relate to psychological ill-health; instead, some studies show that deep acting relates positively to psychological well-being, such as job satisfaction and level of personal accomplishment (Brotheridge and Lee, 2002; Grandey 2003).

The employees will have to intentionally modify their own emotions in both surface acting and deep acting. �� !�� >�� ]!�� pleasant internal feelings through psychological mechanisms, such as cognitive change (Grandey, 2000; Gross 1998), before exhibiting the organizationally prescribed emotional expression in the workplace (e.g. smile). These emotional labor strategies may have different consequences, for example, when surface acting, the internal emotions would not align with the external emotional display which is called emotional dissonance, whereas for deep acting, the internal emotions and external emotional display are congruent <��$��<��>��Humphrey (1993), focus on only surface acting and deep acting may not cover the full range of emotional regulation strategies.

It may not be required or possible for an individual to regulate emotions only on the two strategies i.e.: surface and deep acting as some service personnel may exhibit their genuine and authentic emotions when interacting with their customers. It is important to note according to an empirical study by Diefendorff et al. (2005) which tested an alternative emotional labour strategy called the naturally felt emotions, it may be possible for some individuals to express the

organizationally expected emotions naturally without actively modifying their emotions. Factor analysis showed that the expression of naturally felt emotions was a unique regulation strategy and it was predicted by both work and personality factors. So far, the role of expression of naturally felt emotions on employees’ health and well-being is still unknown.

In short, Deep acting has been called “faking in good faith” as authenticity is seen and surface acting is called “faking in bad faith” as it is not authentic and it could be done just to retain the job and not to help the customer or the organization (Raforli & Sutton, 1987). According to recent emotion regulation lab studies, both surface and deep acting techniques may result in the required emotional expression. Such studies may help explain how emotional labor can relate functionally to performance measures but can be dysfunctional for the individual’s health and stress.

{>[� ��!��>��the aggregated value to the organization of the discrete behavioural episode that an individual performs over a standard interval of time (Motowildo, Borman and Schmit, 1997). In this research, Job performance is used synonymously as work effectiveness and is used to identify to what extent the employees has accomplished their assigned tasks. Job performance is divided into two dimensions - task performance and contextual performance. Task performance consists of two types- executing technical process and maintaining and servicing technical requirements (Motowildo, Borman and Schmit, 1997). Contextual performance ��!��>��organization i.e. helping others, cooperating with others, following the stated rules and procedures to carry out task activities (Motowildo, Borman and Schmit, 1997). Due to the prevailing gap in literature with regard to generic factors to measure job performance, researchers have constructed factors underlying job performance �!��>��$

�� !��>�� used the Role Based Performance Scale developed by Welbourne, Johnson and Erez (1996) to identify Job Performance. In Role-Based performance scale,


role theory and identity theory are used to develop a theory-based generalizable measure of performance. Role theory provides an explanation as to why work performance should be multidimensional and identity theory suggests how to determine which dimensions to include in a model of work performance. By using these two theories it is suggested a measure of !��>��$�$��`�Career, Innovator, Team member and Organization citizen. There has been ample evidence that roles have been recognized as central to understanding employee behaviour in organizations (Katz &Kahn, 1978).

{>|� }��!� ��~�� !�� It is established with the studies conducted by Grandey 2003, Groth, Michael Paul, Dwayne Gremler, Henning-Thurau and Walsh (2009), �� !� �� !�� !��!�� !�� !�� >�� customers than surface acting ( inauthentic emotional labour display). The study by Diefendorff et al (2005) ��>�� !� ��Z~�^`� �� Z��^�and expressions of naturally felt emotions were distinct constructs because it was argued that naturally felt emotions is a proxy for low levels of SA or that it is redundant for DA. It also proved that individuals displayed naturally felt emotions more than SA or DA and it plays a very important role in displaying emotions at work. SA is not all that bad as it could be better than displaying felt emotions on a bad day. Individual who tend to value positive interpersonal interaction focussed on genuine emotional display. Morris and Feldmand (1996) stated that if frequency is more, regulation of emotional display will have greater need. The available literature states the important role emotions play in a service encounter (Menon and Dube, 2000).The ��!�� !�� the customers judgement of the quality of service provided (Pugh 2001; Winsted 2000). Tan et al (2004) ��>��!��!��interacting with customers was linked to customer satisfaction. So, it is known that managing employees who interact with customers like the customer service personnel is an important criteria to maintain loyal customers (Albrecht & Zemke,1985; Schneider &

Bowen, 1985).The service employees customer orientation is a very important aspect which helps them exhibit the behaviour during personal interactions with customers (Henning-Thurau, 2004).The customers perception on the employees customer orientation is also a very important aspect in a service company’s value creation process (Brady & Cronin,2001; Hennig-Thurau, 2004; Walsh& Beatty, 2007). As stated earlier deep acting emerge as an important driver of service delivery outcomes. Surface acting does not have the same positive effect on customers. Surface acting is not a crucial problem as along as a customer recognizes it. No matter which emotional labour strategy is adopted by the employee, the true emotions often leak out and is detected by customers (Ekman, 2001 and 2003).This detection by the customer is only by chance (Ekman & O’Sullivan,1991). Pursuing a ‘always smile’ customer service strategy may not be most effective for improving customers experiences, such a strategy can have detrimental effects on service and experience. The study by Cote & Morgan (????) states that surface acting may be less effective than deep acting in eliciting desired customer responses and faking emotions may increase staff turnover. Empathy training as an intervention is given to service employees to put themselves in the shoes of customers (Parker & Axtel, 2001) as this could help them to engage in deep acting.

In a study conducted by Kafetsios and Loumakao (2007) �� <�� >�� <�� strategies were consistent predictors of work outcomes and job satisfaction as anticipated by Gross (1998) �!��>�� !$� �� at this point also refer to the proposition stated by _��`��Z��^��of emotional labour on job role, career role, innovator role, team role and organization role in performance is high in mass service. It has been stated that it may be easy to fake an emotion in a highly standardised situation, and in a less standardised situation the true ��>��Z��!�`��^$

The study by Markus Groth, Thorsten Hennig-Thurau, |�� Z��{^� ��>�� >��link between employee deep acting and customer �� >�� =��<��


acting and customer orientation. With reference to the above stated literature the researchers were keen to �� ~�� !��$�� !�� >�� !��`�<��stated below:

M%Z� ��#��#�� $��

MCZ� �� #�� #�� $��

3. Proposed research model�� !� �� <`� �� !�!��!��!��$��!��<��!�� ~��!� �� !�� $��<��]!��!��!��<��<��]��#��%��<��$

Figure 1: Research Model

Surface Acting

Deep Acting

Job Performance

4. Research methodology"��<��=��`��!��!��<�� !�� *��!�� !��!��$�~��<��"�� ]��`��#��#��

!�� !��_�� !��'��Z��^`�<��<��_��$��<��!�� [�� Z��^� �� [��`�� &�Z�{{�^�<��!��<��<��*�� *��<��$

4.1. Measurement of variables

Surface acting and Deep acting��=��<��]��<��!��!��`��<��]��[�� Z��^� <�� !�� $� �� <�� !� �� ]��<�� =�� <��>��!��“Never” to “Always”.

Performance�� !�� !�� !�� `� �� &� Z�{{�^� �� !� ��>�� $�$� *��`� ��`� ��`� �� &��$��<��>��!��¥'��!��¤��¥ ]��¤$

4.2. Analytical methods: �� !��!��!��`��Z��^��!��Z��#^$�%��<��was used for the analysis.

4.3. Reliability: �� !�� common method of reliability test namely Cronbach’s ��!��>��$��`�~��<��$��}`��$��$��!��$

4.4 Validity: The convergent validity of each ��`�� `� ��>��]��¥��]��Z�% ^¤�� <�� $��$� ��% ��`�~��<��$��{`��$��}��$}}��!��$�� !��


reliability values are greater than the minimum level. The study also employed discriminant validity by examining the square root of AVE value of a construct, which should be greater than the correlation between the constructs used in the model or squared correlation between the constructs should be less than the AVE (Fornell Larcker, 1981). The AVE values are greater than the r square value (0.098), which indicates that all the constructs have good discriminant validity

|>� '�!��In this section the results of the research model is presented. In order to test the proposed hypotheses in the research model, this study employed a construct �� >��$�Visual PLS was used to compute the construct scores. Using these construct scores as a base, the study explored the relationship between the variables using SPSS (version 18). The construct correlation has ��!�� $��>��between SA and Performance and DA and Performance <�� >�� $�� Z�� ¦� ��$�}�`�-0.313, p< .01) respectively. Although the bivariate ��>��<��`��!��!��>��model as a causal effect. The results are given in Table 2 and Figure 2.

��@��}��!��~��

Hypothesis IndependentVariable

DependentVariable

Correlation(r)

Sig. (two tailed) Result

H1 SA Performance - 0.256 0.000** ��>��

H2 DA Performance - 0.313 0.000** ��>��

Z§§^�"��>��$��

��{��\��!��

HypothesisEntire

SampleEstimate

Mean ofsubsamples

StandardError T-Statistic R Square

Value Result

H1 -0.276 -0.272 0.0757 -4.4660.174

��>��

H2 -0.33 -0.3592 0.0742 -3.809 ��>��

It is seen that Hypotheses 1 and 2 deals with the relationships between emotional labour strategies i.e.: SA and DA and Job Performance.

�� <�� !�� >�� >��`� �� `� �� a Bootstrap and Jacknife procedure to estimate standard errors for calculating ‘T” values in using normal PLS. The relationship between SA and DA to �� <�� >�� $�}�levels. The path linking SA and Job Performance was �� >�� Z[�� ¦��$��`� �¦�$��^��`� ��!��=��~��Performance was also found to be negatively related ��>��Z[��¦��$��`��¦��$��^$

��{��

SA

DA

JP

RSq = 0.174

-0.276 (-4.466)

-0.330 (-3.809)

�>� ?��"��This study aims to understand the relationship between SA, DA and Job Performance. It was found that there is a negative relationship between them. In simple words, when customer service personnel increase their engagement in surface acting and deep acting , their *��!��$��>��is in contrast with Grandey 2003, Groth , Michael Paul, Dwayne D Gremler, Henning-Thurau and Walsh (2009) >��!��<��!��the customer. This, surely states that the emotional labour strategies impact performance.

With reference to this study, it does make sense as the customer service personnel interact for very short time


duration with the customer. This means most of the interactions are very short. This also takes the research to the point as stated by Diefendorff et al (2005) that the naturally felt emotions could play a bigger role in the interactions in this context. Though this type of business has few competitors as of now, the customers depend on them as this is provided by the government. Hence it is considered safe by the customer to utilize this service.

When the service provider expresses a good cheer, it in turn increases the possibility that the customer will report a positive service encounter (Pugh, 2001; Tsai, 2001).The way the service person interacts, is also not noticed in most occasions as long as the customer gets what he has asked for. Normally, these service encounters are in small counters and there is normally a queue to avail these services. Hence, if a service personnel increases the labouring strategies this could need additional time to provide service and hence will have an impact on the performance. The customer service personnel play a vital role in satisfying the �]!��`�<��their perceptions about service quality

(Barnes & Morris, 2000). If the customers’ perceptions do not meet their expectations, it could be evaluated as poor service (Boshoff & Mels, 1995). Therefore, �� >�� *�� service interaction’s overall excellence or superiority (Parasuraman et al., 1985). As a result the customers waiting in the queue would get delayed and would identify the service personnel as too slow at work. This could be a very important point to note in these types of services. As the customer feels, the lesser time he waits in the queue the better the service provided by the service provider.

It is also important to know that customer’s behaviour �� !��<�� service encounter Weick’s (1996). Thus a pleasant customer could bring out a pleasant response from the service provider which could lead to higher satisfaction for the customer of the service provider. There is also another valid point to be considered that most

customers would not expect emotional responses from this service person as that is the prevailing belief in the minds of customers. Hence, if a customer service person even extends a smile it may or may not impact the customer. It is unknown if this could have a negative impact on the customers. The customer service person most times also feel that there is no incentive for the amount of effort they put in, from the organization. It was also understood during the discussion by the researchers that there is a huge shortage in staff in these organization. Hence, the customer service person does ��<��=�� >�$�� be understood that when emotional labouring strategies i.e. SA and DA are engaged at a minimal level the performance of the customer service personnel could be better.

The authors believe that it is worthy to investigate further in the following manner: (a). this study could �� ]�� emotions in such service encounters in both private and public enterprises in India., (b) there is an opportunity to explore further on the same between different countries.

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Performance of traditional chart with correlated data

* Research Scholar in the department of Mechanical Engineering, PEC University of Technology, Chandigarh-160012 (India). E-mail: [email protected]

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

Shewhart charts are the prominent charts used to detect the larger shifts in the mean of the process. They are widely used in the all type of the processes as well as in the service industries. It is usually assumed that the observations from the process output are independent and identically distributed (i.i.d.). But in actual practice, for many processes the observations are �� #��effect on the performance of the Shewhart (standard) chart. The performance of the traditional (Shewhart) chart is studied for the i.i.d. data and for the positively correlated data. The Average Run Lengths (ARLs) at various sets of parameters of the chart are computed by simulation, using the MATLAB software. The behavior of the chart at the various shifts in the process mean is studied and analyzed. It is concluded that when the level of correlation increases, both the false alarm rate and out of control ARL increase. In this paper, optimal schemes of the chart for each level of the correlation are suggested.

Key Words: Traditional charts, Average run length, independent and identically distributed data, Autocorrelation, MATLAB

Sukhraj Singh*Dr. D. R. Prajapati**

INTRODUCTIONControl charts are one of the powerful tools of the Statistical Process Control (SPC) techniques, which are widely used in industry for process improvement and for estimating parameters or monitoring the variability of a given process. Shewhart charts were originally developed by Walter F. Shewhart (1931). The limits of the chart are known as upper control limit (UCL) and ��<��Z�"�^$��>��LCL = � ��ª�«¬��"��¦� +��ª�«¬�`�<��

�� ª�� !�!�� deviation.

Figure 1 shows the sample chart with upper and lower control limits.

Figure 1 Sample chart

The process is assumed to be out of control when the sample average falls beyond these limits. The Shewhart ��Z®��ª^�in processes. It is assumed that the process observations are i.i.d but usually there is some correlation among the data and when this correlation is build up automatically in the entire process, this phenomenon is called Autocorrelation.


The presence of autocorrelation in the processes gives a profound effect on control charts developed for i.i.d observations. When such an autocorrelation exists, standard control charts may exhibit an increase in the frequency of false alarms. There is an increased likelihood that the data will exhibit autocorrelation in systems where the process time is longer than the time between samples collected.

Several researchers have examined control chart behavior in the presence of autocorrelation. If process measurements are autocorrelated, then standard constructions of control charts will violate the assumption that samples are independent. In-control ARL is reduced due to the autocorrelation in the data; moreover it also affects the behavior of the Shewhart control chart at various shifts. A quality engineer will search the assignable causes behind the more number of the false alarms at different shifts but will found nothing. This is how the autocorrelation in the measured data affects the performance of the control chart. The type 1 error rate for control charts is sensitive to autocorrelated data; that is, control charts are subject to increased false alarms and, hence, shorter Average Run Lengths (ARLs). Even at low levels of correlation, dramatic disturbances can occur in the chart properties.

In the industry, if there is autocorrelation, then the autocorrelated data is normally distributed. The autocorrelated data can be generated from a set of uniformally distributed data with mean of 0 and standard deviation of 1 with the help of the MATLAB.

Figure 2 shows the distribution of the 10,000 independent and identically distributed (i.i.d) random numbers following the normal distribution.

Figure 2 Distribution of the 10,000 i.i.d data

The autocorrelated data is generated from the normally distributed data. Figure 3 clearly shows that the 10,000 correlated numbers are distributed normally.

Figure 3 Distributions of 10,000 positively autocorrelated numbers

When the successive observations are uncorrelated, there is no memory in the data: previous observations do ��$��!��randomly around the mean. In most of the applications of control charts, it is assumed that the data exhibits this kind of behavior. However, in actual practice, most data sets show some form of serial correlation. In Figure 4, successive negatively correlated data points are shown. In negative correlation, the observation below the mean tends to be followed by an observation that is larger than the mean value, and vice versa. Thus the sequence of observations exhibits alternating behavior.

Figure 4 Negatively correlated data

The data shown in Figure 5 is positively correlated. In positive correlation, if the current observation is on one side of the mean, the next observation will most


likely be found on the same side of the mean. Positively correlated data are characterized by runs above and below the mean. Positive correlation is more often encountered in practice than negative autocorrelation.

Figure 5 Positively correlated data

Autocorrelation is inherent to many processes like:

�� Chemical Process: - Due to inertia of large batches, �� <� �� `� �� =� ��feed-forward process control systems.

�� Manufacturing Processes: - Due to computer controls of production equipments and downstream pooling of the multiple stream processes.

�� Service Processes: - As described in queuing theory, <��the waiting time of the previous customers.

As discussed earlier the autocorrelation among the data plays an effective role. When traditional charts are applied to the autocorrelated data, the false alarm rate increases and performance of the chart is suspected. So improvement in the traditional (Shewhart) is needed to improve its performance for the correlated data. The next section deals with the literature review in this area.

2.0 LITERATURE REVIEWVarious researchers did remarkable contribution in �� >��and development of the ��$��>��'��'��<��=�Z�''^��!��of the control chart. The research work done so far in

modifying the chart for the autocorrelated data is discussed as follows:

Yang and Hancock (1990) presented a correlation model to describe the correlated data in a sample. Prybutok et al. (1997) found that Shewhart control charts were designed to be used with independent observations. But the actual data were positively autocorrelated. They compared the simulation study of the performance of ��>]��<�� control chart with the variable interval technique when the data were autocorrelated. Samples of size 1 were employed in the investigations which were conducted by varying the interval length, autocorrelation parameter, and shift in the process mean. Adjustments for false alarms caused by the presence of autocorrelation were implemented. The results suggested that the average time to signal (ATS) depend on the shift magnitude and the amount of autocorrelation in the process. They showed that, in general, when monitoring an autocorrelated process it was advantageous to use the variable interval technique ��>]��!!��increased in proportion to the shift in the process mean.

��$�Z��^��&��<��>]��control limits for residuals to monitor the performance of a process yielding time-dependent data subject to shifts in the mean and the autocorrelation structure. The effectiveness of the framework was evaluated by an Average Run Length study of both and EWMA charts using analytical and simulation techniques. The ARLs were tabulated for various process disturbance scenarios, and recommendations for the most effective �� <�� $� �� >�� research presented motivation to extend the traditional !�� !�� Z�$�$`� �� «��variance). The results showed that decrease in the underlying time series parameters were practically impossible to detect with standard control charts.

Chou et al. (2001) found that when the chart was applied to monitor a manufacturing process, three parameters should be determined: sample size, sampling interval between successive samples and the control limits for the chart. They developed the economic design of charts for non-normally correlated data


for the juice production process. A sensitivity analysis was performed to show the effects of non-normality ��>��!��chart.

Loredo et al. (2002) presented a method for monitoring the autocorrelated processes based on regression adjustment. The performance of the residual-based control chart in terms of the ARL was compared to the observation-based control charts via Monte Carlo simulations. They showed that the observation-based control charts performed very poorly when data were correlated over time. They suggested that the residual-based control charts were superior for all cases considered. It was also suggested that a residual-based control chart can be used to detect the shift in the process mean. They recommended residual-based charts for chemical processes where many of the variables were highly autocorrelated.

Liu et al. (2003) found that control charts were widely applied to monitor manufacturing processes. They found that Gordon and Weindling (1975) presented a cost ��>��!��<��limit chart: i.e., the sample size, the sampling interval between successive subgroups, the control ��>��`��<��>��>��$��`��is assumed that the measurements within a sample were independently distributed. However, this assumption �� !��>� !��processes. They studied the effect of correlated data on the design of warning limit charts by combining |��Z�{�}^��<�� ̄��=��Z�{{�^��$�[��`��<��>��!��`��>��was affected by the correlated data. Highly correlated data or independent data resulted in a longer run in the warning band.

'��$�Z��^�!��>��'��'��<��=� Z�''^� �� three types of non-random disturbances referred to as level shift, additive outlier and innovational outlier, which were common in autocorrelated processes. An

autoregressive of order one AR (1) model was considered to characterize the quality characteristic of interest in a continuous process where autocorrelated observations were generated over time. The performance of the proposed procedure was evaluated through the use of a numerical example. Preliminary results indicated that the procedure could be used effectively to detect and classify unusual shocks in autocorrelated processes. Castagliola and Tsung (2005) investigated the effect of skewness on conventional autocorrelated SPC techniques and provided an effective approach based on a scaled weighted variance approach to improve SPC performance in such an environment.

Hwarng (2005) developed a neural-network-based ��>��!��$��>��<��mean shift, to recognize the presence of autocorrelation, and to identify shift and correlation magnitudes. They had also done research in case of a variable sample size and sampling interval. A chart with variable sample size and sampling interval (VSSI) has been shown �!��<��>]��!��&��and sampling interval.

Claro et al. (2008) proposed the Double Sampling control chart for monitoring processes in which

�� <��>�� model. They considered sampling intervals that were ��>��!��!�$�The Double Sampling chart was substantially more ��>��<��%��!��Size (VSS) chart. To study the properties of these charts they derived closed-form expressions for the ARL taking into account the within-subgroup correlation. '�� <�� >��!��!��!��$

Chen and Cheng (2009) found that in the design of the ��`��!��&�� and

�� =�� Z�� deviations from the center line) must be determined. They addressed this problem under the assumption that the quality characteristic followed an autocorrelated process with known covariance structure but unknown marginal distribution shape. The two methods for


��=�`��&��of-control ARL while maintaining the in-control ARL ��!��>��`�<��!��!��$��#��calculate the ARL values as if the sample means were independent normal random variables; Second Method calculate the ARL values as if the sample means were an AR(1) process. Second Method outperformed the First Method when the correlation and mean shift were both high.

Costa and Castagliola (2011) found that measurement error and autocorrelation often exist in quality control applications. Both have an adverse effect on the

�� !��$� �� undesired effect of autocorrelation, Authors build-up the samples with non-neighbouring items, according to the time they were produced. In order to counteract the undesired effect of measurement error, they measured the quality characteristic of each item of the ��!�� $� �� !�� <��assessed when multiple measurements were applied and the samples were built by taking one item from the production line and skipping one, two or more before selecting the next.

3.0 FORMULATIONAutocorrelation means the correlation between the data observed is build up automatically. The series of normally distributed i.i.d numbers is developed using the MATLAB software. The correlated data �� >�� Z�^� �� $��>��Z�^��be calculated from equation 1 as suggested by Keller (2005).

--------------------- (1)

Where,

"��>��b’ (the slope of the line) may be calculated as

and R2 may be calculated as

The procedure to implement the chart is given in the next section.

4.0 PROCEDURESimulation of the data is done with the help MATLAB 6.5 software. The autocorrelated numbers are generated from the series of data taken from the normal distribution with mean of 0 and standard deviation of 1. The initial process is in control, the time interval in which the process remains in control. When the assignable cause occurs, the process mean shifts �| units, then the average number of sampling before ��!��Z°±�^$��following procedure may be adopted to calculate the ARLs of the chart.

Step 1 Take the observations from industry at random basis.

Step 2 Observations are generated randomly at given mean and standard deviation.

Step 3 For simulation 10,000 observations are generated with a sample size of 4.

Step 4 The observations are generated in such a way that there should be positive correlation with their previous data.

Step 5 Those sets of parameters of the control chart which gives the Average Run Length (ARL) of approximately 370 are considered for comparison.

Step 6 For the selected combinations the ARLs are calculated at various shifts in process mean, at different width of the control limits (L) and at ��>��Z�^$


5.0 COMPUTATION OF ARLsThe ARLs of the traditional (Shewhart) are calculated at various levels of correlation i.e. at lowest �� >�� $�� >�� `� �� <�� limits (L). Following tables gives the ARLs at various �� >�� Z�^�and L.

Behavior of the traditional (Shewhart) control chart when the data is i.i.d. from the normal distribution of mean of 0 and standard deviation of 1. Table 1 shows the ARLs of traditional chart at various width of control limits (L).

Table 1 ARLs of the chart for random i.i.d. data

Shift (in

mean)L = 2.9 L= 2.95 L=3.0 L=3.05 L=3.1

0.0 285.7 312.5 371 400.0 555.60.2 250.0 263.2 294.1 303.0 312.50.4 208.3 181.8 270.3 227.3 285.70.5 144.9 142.9 155.2 178.6 169.50.6 99.0 114.9 120.5 133.3 111.10.8 66.2 68.0 76.3 99.0 74.11.0 38.3 38.2 44.1 46.5 51.01.2 26.4 27.7 30.2 29.5 31.41.4 16.1 17.5 19.2 20.0 20.31.5 13.1 13.7 14.9 17.9 15.71.6 11.0 12.0 12.3 13.3 13.41.8 7.7 7.9 9.0 9.4 9.52.0 5.7 5.9 6.3 6.7 7.12.5 3.1 3.0 3.2 3.4 3.53.0 1.9 1.9 2.0 2.1 2.13.5 1.4 1.4 1.5 1.5 1.54.0 1.2 1.2 1.2 1.2 1.2

Since the in-control ARL of traditional chart is 370. So, ARLs at different levels of correlations are calculated by keeping the in-control ARL of approximately 370. Control limits are adjusted in such a way that the in-control ARL of approximately 370 may be maintained. Table 2 shows the ARLs of the traditional chart at ��>��Z�^��$�`��$��$�$

Table 2 ARLs of the ��/��correlation(r) of 0.1, 0.2 and 0.3

r= 0.1 r= 0.2 r= 0.3Shift (in

mean)

L= 2.95

L=3.0 L = 2.9 L= 2.95

L = 2.9 L= 2.95

L=3.0 L=3.1

0.0 368.3 384.6 322.5 370.37 312.5 322.6 368.5 381.40.2 283.0 263.7 222.2 290.6 188.7 243.2 294.1 284.20.4 168.6 217.4 172.4 175.4 166.7 129.9 200.0 232.10.5 119.3 153.9 117.5 126.6 113.6 117.7 161.3 179.40.6 92.6 123.5 102.0 105.3 105.3 102.0 119.1 131.10.8 69.9 72.5 57.5 69.9 69.4 67.1 73.5 84.21.0 34.5 40.5 36.0 39.5 37.6 44.8 44.8 51.31.2 26.3 30.2 21.2 30.2 24.1 26.3 30.8 38.01.4 16.6 18.1 15.0 17.4 16.3 20.3 21.2 27.71.5 13.6 15.1 12.5 14.8 15.2 15.3 18.3 21.11.6 10.8 12.9 10.6 12.2 12.3 12.8 13.9 17.31.8 8.0 8.8 8.0 8.5 8.4 9.3 10.3 12.62.0 5.7 6.3 5.8 6.4 6.3 6.8 7.0 9.22.5 3.1 3.3 3.0 3.3 3.3 3.4 3.7 3.93.0 1.9 2.0 1.9 2.0 2.0 2.1 2.2 2.53.5 1.4 1.5 1.4 1.5 1.5 1.5 1.6 1.64.0 1.2 1.2 1.2 1.2 1.2 1.2 1.3 1.4

Table 3 shows the ARLs of the traditional chart at ��>��"��Z�^��$}��$�$

Table 3 ARLs of the ��/��correlation(r) of 0.5 and 0.7

r= 0.5 r= 0.7Shift (in

mean)L = 2.9 L= 2.95 L=3.0 L = 2.9 L= 2.95 L=3.0 L=3.1

0.0 303.0 369.6 400 303.0 303.0 370.1 416.70.2 238.1 313.3 344.8 243.9 243.9 322.5 384.60.4 175.4 227.7 256.4 178.6 212.8 243.9 285.70.5 113.6 163.9 163.9 144.9 126.6 191.7 222.20.6 92.6 126.7 108.7 128.2 120.5 141.5 196.10.8 63.7 104.2 76.9 95.2 102.0 118.9 156.31.0 42.6 51.3 46.1 47.0 60.6 68.1 82.61.2 28.7 37.5 35.5 33.9 41.8 43.9 55.61.4 19.8 23.3 21.3 26.5 26.4 28.6 37.01.5 15.7 21.5 19.5 20.5 20.9 24.6 29.91.6 13.4 17.1 16.7 16.3 18.0 22.1 27.51.8 9.3 12.2 11.7 13.3 13.6 12.4 19.92.0 7.3 8.6 8.4 9.3 9.5 11.2 13.72.5 3.4 4.4 4.3 4.9 5.6 5.1 7.03.0 2.4 2.4 2.7 2.9 3.2 3.0 3.83.5 1.7 1.8 1.8 2.0 2.1 2.3 2.54.0 1.3 1.4 1.4 1.5 1.6 1.6 1.7


Table 4 shows the ARLs of the traditional chart at ��>��Z�^��${��$�$

Table 4 ARLs of the ��/��correlation(r) of 0.9 and 1.0

r= 0.9 r= 1.0Shift (in mean) L=2.9 L=2.95 L=3.0 L=2.9 L=2.95 L=3.0 L= 3.1

0.0 370.4 434.8 454.5 303.0 312.5 373.1 416.70.2 322.6 370.4 294.1 243.9 294.1 337.5 384.60.4 255.1 256.3 277.8 178.6 270.3 243.9 285.70.5 200.0 208.3 217.4 144.9 217.4 207.1 222.20.6 151.2 175.4 175.4 128.2 192.3 151.5 196.10.8 121.7 109.9 101.0 95.2 117.7 138.9 156.31.0 70.1 78.1 77.0 47.0 72.5 74.4 82.61.2 44.3 47.2 57.5 33.9 56.5 45.9 55.61.4 28.6 35.0 44.3 26.5 40.8 30.7 37.01.5 26.2 32.5 34.8 20.5 34.6 27.8 29.91.6 22.3 25.2 26.3 16.3 29.4 23.0 27.51.8 13.0 18.1 20.8 13.3 21.8 14.4 19.92.0 11.4 14.3 15.1 9.3 16.4 11.8 13.72.5 5.6 7.6 8.2 4.9 8.6 6.7 7.03.0 3.3 4.3 4.5 2.9 5.1 3.3 3.83.5 2.3 2.8 2.9 2.0 3.3 2.6 2.54.0 1.9 2.0 2.1 1.5 2.2 1.9 1.7

6.0 ANALYSISThe schemes that are having consistently low ARLs than others are selected as optimal scheme. The optimal schemes of the traditional chart at the various levels of the correlation are selected. The behaviors of these schemes at the various shifts in the process mean and �� >�� Table 5.

Table 5 Optimal schemes for the different level of the correlation

r=0.1 r=0.2 r=0.3 r=0.5 r=0.7 r=0.9 r=1.0 r=0Shift (in

mean)

L= 2.95 L=2.95 L=3.0 L=2.95 L=3.0 L=2.9 L=3.0 L=3.0

0.0 368.3 370.37 368.5 369.6 370.1 370.4 373.1 3710.2 283.0 290.6 294.1 313.3 322.5 322.6 337.5 294.10.4 168.6 175.4 200.0 227.7 243.9 255.1 243.9 270.30.5 119.3 126.6 161.3 163.9 191.7 200.0 207.1 155.20.6 92.6 105.3 119.1 126.7 141.5 151.2 151.5 120.50.8 69.9 69.9 73.5 104.2 118.9 121.7 138.9 76.3

r=0.1 r=0.2 r=0.3 r=0.5 r=0.7 r=0.9 r=1.0 r=0Shift (in

mean)

L= 2.95 L=2.95 L=3.0 L=2.95 L=3.0 L=2.9 L=3.0 L=3.0

1.0 34.5 39.5 44.8 51.3 68.1 70.1 74.4 44.11.2 26.3 30.2 30.8 37.5 43.9 44.3 45.9 30.21.4 16.6 17.4 21.2 23.3 28.6 28.6 30.7 19.21.5 13.6 14.8 18.3 21.5 24.6 26.2 27.8 14.91.6 10.8 12.2 13.9 17.1 22.1 22.3 23.0 12.31.8 8.0 8.5 10.3 12.2 12.4 13.0 14.4 9.02.0 5.7 6.4 7.0 8.6 11.2 11.4 11.8 6.32.5 3.1 3.3 3.7 4.4 5.1 5.6 6.7 3.23.0 1.9 2.0 2.2 2.4 3.0 3.3 3.3 2.03.5 1.4 1.5 1.6 1.8 2.3 2.3 2.6 1.54.0 1.2 1.2 1.3 1.4 1.6 1.9 1.9 1.2

Tables 2, 3, 4 and 5 show the following facts:

(i) All the schemes have the in-control Average run length (ARL) of approximately 370.

(ii) When autocorrelation exist in the observations, false alarm rate increases in all the shifts in the process mean.

(iii) When the level of autocorrelation increases, false alarm rate increases at the same width of the control limits (L).

(iv) The Width of control limits (L) also affects the Average Run Length (ARL) of the traditional chart.

(v) The out-of-control Average Run Length decreases on lowering the width of the control limits (L) at the same level of the correlation (r).

6.0 CONCLUSIONSIn this paper, the effect of correlation in the observations on the traditional (Shewhart) chart is studied. The performance of the traditional is studied for the random observations and the positively correlated observations. The performance of the chart in the two different situations is monitored and measured in terms of the Average Run Lengths (ARLs). As the width of the control limits (L) increases, the out-of-control ARLs at various shifts in the process mean also ��$��>��Z�^��the observations is very strong i.e. r = 1.0, the ARL at


�ª��!��`�<��`��the same shift in the process mean without correlation. In this paper, optimal schemes for the various levels of the correlation are suggested that may be very useful at ��!��$

REFERENCES��Castagliola, P. and Tsung, F. (2005).

“Autocorrelated SPC for non-normal situations”, Quality and Reliability Engineering International, Volume 21, Issue 2, pp. 131-161.

��Claro, F. A. E., Costa, A. F. B. and Machado, M. A. G. (2008). “Double sampling control chart ��>��!��¤`�Pesquisa Operacional`�%��`�'��`�!!$�}�}�}��$

��Chen, H. and Cheng, Y. (2009). “Designing charts for known autocorrelations and unknown marginal distribution”, European Journal of Operational Research, Volume 198, Issue 2, pp. 520-529.

��Chou, C.Y., Liu, H.R. and Chen, C.H. (2001). “Economic design of averages control charts for monitoring a process with correlated samples”, International Journal of Advanced Manufacturing Technology`�%��`�'��`�!!$��{�}�$

��Costa, A. F. B. and Castagliola, P. (2011). “Effect of measurement error and autocorrelation on the

chart”, Journal of Applied Statistics, Volume ��`��`�!!$��$

��Duncan, A. J. (1956). “The economic design of charts used to maintain current control of a

process”, Journal of the American Statistical Association`� %�� }�`� '�� `� !!$� ��242

��English, J. R., Lee, S. C., Martin, T. W. and Tilmon, C. (2000). “Detecting changes in autoregressive processes with and EWMA charts”, IIE Transactions, Volume 32, Issue 12, pp. 1103 - 1113.

��Gordon, G.R. and Weindling, J.I. (1975). “A cost model for economic design of warning limit control chart schemes”, AIIE Transactions, %��`�'��`�!!$��{��{$

��Hwarng, H. B. (2005). “Simultaneous ��>��in AR (1) processes”, International Journal of Production Research, Volume 43, Issue 9, pp. ��$

��Liu, H. R., Chou, C. Y. and Chen, C. H. (2003). “The effect of correlation on the economic design of warning limit charts”, The International Journal of Advanced Manufacturing Technology, %��`�'��`�!!$��$

��`� $� '$`� ��=!�!��`� ~$� �� [��`�C. M. (2002). “Model-based control chart for autoregressive and correlated data”, Quality and Reliability Engineering International, Volume 18, !!$��{��{�$

��`��$�Z��}^`�¥��]��~��>��¤`��#|��<��`�'�<�~��$

��'��`� �$`� ��=��`� #$� �� `� �$�(2003). “Using neural networks to detect and classify out-of-control signals in autocorrelated processes”, Quality and Reliability Engineering International`�%��{`��`�!!$��{��}��$

��Prybutok, V. R., Clayton, H. R. and Harvey, M. �$�Z�{{�^$�¥"��!��>]��sampling interval Shewhart control charts in the presence of positively autocorrelated data”, Communications in Statistics - Simulation and Computation`�%��`��`�!!$��$

��Shewhart, W. A. (1931), “Economic Control of Quality of Manufactured Product”, D. Van '��"��!��`��`�'�<� �̄�=$

��Yang, K. and Hancock, W.M. (1990). “Statistical quality control for correlated samples”, International Journal of Production Research, %��`��`�!!$�}{}��$


A Conceptual Model for Gauging Overall Performance in Public Transport: A Case Study

* Professor, Department of Mechanical Engineering, Dr. Paul’s Engineering College, Villupuram - 605109, TamilNadu, India [email protected]

** Professor, Department of Manufacturing Engineering, Annamalai University, Chidambaram - 608002, TamilNadu, India [email protected]

*** Professor, Department of Management Studies, Pondicherry University, Pondicherry - 605014, Pondicherry, India [email protected]

**** Professor, Department of Mechanical Engineering, Indian Institute of Technology Delhi, New Delhi - 110016, India [email protected]

The case study presents a conceptual model aimed at gauging the overall performance of a public transport. M��$�� consisting of eight models namely, fuzzy TOPSIS - fuzzy AHP multivariate analyses - which includes principal component analysis (PCA), discriminant analysis (DA), and multivariate analysis of variance (MANOVA); perceptual mapping (PM), performance importance matrix (PIM); and a composite model integrating conjoint analysis (CA) with the traditional QFD. This case study in a public sector passenger bus transport can incorporate two types of data namely; operational characteristics data and customers’ or passengers’ characteristics data.

Key Words: Overall performance, Public transport, Fuzzy TOPSIS - fuzzy AHP, Principal component analysis, Discriminant analysis, MANOVA, PIM, Conjoint analysis, QFD.

M.Vetrivel Sezhian * T.Nambirajan ***C.Muralidharan ** S.G.Deshmukh ****

IntroductionIn several Asian countries, the public sector occupies an important position in the economy. Bus is a very popular mode of transportation in India because of �� <� �� >��contribution to the national economy. In India, the state road transport undertaking (STRUs), with 58 members, form the backbone of mobility for urban and rural population across the country. They ply over 1,15,000 buses, serving more than 65 million passengers a day and also providing employment directly to 0.8 million people (ASRTU).

Effectiveness is the extent to which the output of service providers meets the objectives set for them. �>�� <�� <�� &��uses its resources to produce outputs — that is, the degree to which the observed use of resources matches the optimal use of resources, to produce outputs of a given quality. This can be assessed in terms of technical, ��`� �� >�� Z[��`�2006). In short, effectiveness is about ‘doing the right �� >�� (Svikis, 2003). Thus improving the performance of an ��&��>��$�A government sector service provider might increase the ��>��]!��of service; viz., a state transport undertaking might ��!��`��`��&�`��`��the same number of passengers. This could increase ��!!��>�� providing satisfactory outcomes for passengers and so, there is a necessity to develop effectiveness indicators (Bhagavath, 2006). However, as it is serving a large amount of passengers, the quality of service is the main concern of this issue. In the road transport sector


in India, liberalization of the automobile industry and rapid increase in per capita income have led to a shift towards personal vehicles. The share of public transport, on the other hand, has declined over time. The economy is now being constrained by the increasing number of vehicles causing congestion, and thus slower speeds on roads, increased pollution. Transport infrastructure is recognized as being the critical constraint here Z��=�`��{{{^$� �>��!��utilization of the available transport infrastructure would require meeting mobility needs through a greater share of public transport (Planning Commission, 2002). The road passenger transport in India is operated partly by public sector and largely by private sector comprising about 29% and 71% respectively of the total number of buses. ‘The public sector should also strive to create customer value just as the private sector does’, for which understanding the customer expectations are of immense importance (Rahman and Rahman, 2009). Over the last few years, companies have gradually focused on service quality and customer satisfaction. ��!��>��!��well as customers, particularly, for transit agencies and passengers. An improvement in the quality of service rendered will attract further commuters. This would ��!�� >��`��and noise pollution, and energy consumption because individual transport would be used lesser (Eboli and Mazulla, 2007). The development of techniques for analysis of customer satisfaction is very much essential, in order to identify various aspects of the offered services and hence to enhance customer satisfaction. The measure of how products and services supplied by a company meet or surpass the customer’s expectation is seen as a vital performance indicator. In a competitive marketplace where businesses compete for customers, it is seen as a key indicator as to how successful the organization is in providing quality services.

The level of expectation can also vary depending on other factors, such as, other products against which they can compare the organization’s products. The usual measures of getting this involve a survey with a set of statements using a Likert’s scale (1 to 5). The customers are asked to evaluate each statement in terms

of their perception and expectation of performance of the service being measured (Wikipedia). Initially, when a customer survey is to be conducted, a questionnaire �� !��$�� >��!��to be considered is to be listed. The various inputs could be obtained for the same through brainstorming. Experience and thorough knowledge are vital for evolving a comprehensive and adequate list of questions or parameters (Chidambaranathan et al 2009). The experience and expertise of academicians and experts from the sector concerned would form a part of the exercise. The list of questions evolved may initially be administered to a group of customers to ascertain if all the parameters are important or necessary.

2. Literature Review2.1 Performance improvement studies conducted

in the transport sector

A discussion is presented herein about some of the literature available on studies conducted in the transport sector in various countries to gauge the customer satisfaction and quality of service using various tools and techniques. Mazzulla and Eboli, (2006) have presented the use of service quality index for measuring the effectiveness of supplied services in a public transport. It is calculated using the service quality attributes and their weights; and this can be used by transport agencies to improve the quality of supplied service. Transit cooperative research program (TCRP), (1999) focuses on how to measure customer satisfaction and how to develop transit agency performance measures. It is of interest to transit managers, and transit planners, as it provides methods to identify, implement, and �� >��quality service. Extending the quality of public transport (EQUIP) project report, (2000) is a handbook for the self-assessment of internal quality performance by local passenger transport operators for continuous improvement. Quality approach in tendering urban public transport operations (QUATTRO), (1998), calls for a series of recommendations to public authorities, operators and, for some aspects, to the equipment manufacturing industry, which is a major contributor to total quality in public transport. In the literature,


there are many techniques for measuring service quality and customer satisfaction, for public transport as in other service industries. These techniques are based on customer evaluation. The evaluation of service quality and customer satisfaction can be obtained according to different methods: (i) by asking customers the perception/satisfaction on service quality, (ii) by asking the expectation/importance, or (iii) by asking both perception and expectation; in addition, (iv) perception can be compared with the zone of tolerance of expectations (the range as ��>�� ]�� acceptable level of expectations). (Figini, 2003), (v) a rating or ranking of individual service attributes can be asked from the customers. Furthermore, (vi) a rating on overall satisfaction can also be asked. The above studies, particularly TCRP, EQUIP and QUATTRO have given some procedures, methodologies and tools for assessing the customer satisfaction, customer perspectives, quality of service, etc., based on studies conducted in Europe and other western countries. Some of the tools and techniques used include benchmarking, customer surveys, factor analysis, regression analysis, structural equation model (SEM), scattergrams, multi-variate correlation, cluster analysis, conjoint analysis, and quadrant analysis.

Some of the studies conducted in India in the public transport systems such as Singh, (2005), Ramanathan and Parikh, (1999), Bhagavath, (2006), Vijayaragavan (1995) and Deb (2008) have concentrated on the operational aspects, the economics and other issues. Among these only, Vijayaragavan (1995) discusses the necessity to include focus on customers and social needs for improving STRUs in India. The present study has included the passengers’ perspective of quality of service as done in Europe and other western countries but with relevance to the Indian context.

2.2 Performance improvement techniques used in this study

The present study has included both operational variables as well as the passengers’ perspective of quality of service as done in Europe and other western countries. The reviews the literature pertaining to

tools used in this study which includes FTOPSIS and FAHP; factor analysis, discriminant analysis, MANOVA, perceptual mapping, PIM, and QFD. The literature review provides the theoretical foundation for the present research work. The literature review of the various tools and techniques used in this study is presented in Table 1. The following observations are made:

(i) Very few cases of use of Fuzzy AHP and Fuzzy TOPSIS in transport sector

(ii) Principal componanat analysis has been extensively used in the transport sector.

(iii) Discriminant analysis has not been used as tool for performance analysis study in the transport sector.

(iv) MANOVA has not been applied in the transport sector.

(v) As for the literature on Performance importance matrix, the perspectives of customers is abundant, the same from the perspectives of service providers, namely, the managers’ is scarce. There are limited number of articles to date namely Rajesh et al., (2010) and Kitcharoen, (2004) involving a comparison between end user and the service provider perspectives. But there has been no such work reported in a bus company. The use of radar chart in unison with PIM could aid in better understanding of performance criteria.

(vi) Quality function deployment has been used in the transport and related sectors.

(vii) Conjoint analysis has been rarely used in this sector.

There are many performance improvement tools and techniques that have been used individually in transport sector in the literature. The present study has included some them as well as added a few others to develop an integrated module combining them. This would help to get a better perspective of the performance of the transport and aim at operational excellence.


3. Case studyIn this case study a state road transport undertaking (SRTU) located in south India, operating passenger buses has been chosen. It is one of the leading public sector bus transport corporations generating consistent returns as well rendering excellent service over the �� }�� $� ��present case study has been conducted in three bus depots of Villupuram division of the SRTU. There are various divisions such as Chennai, Villupuram, Kumbakonam, Salem, Coimbatore, Madurai, and %��!��$��!��98, 95 and 94 respectively. These depots each employ around 180 bus crew members, 30 maintenance staff, 10 managerial staff, and 15 -18 administrative staff. The average number of passengers travelling every day is approximately 1,50,000 for depot-I, around 1,25,000 for depot-II and less than 1,00,000 passengers for depot-III.

This study on performance improvement methods in a public sector passenger bus transport was carried out by collecting two types of data namely; operational characteristics data and customers’ or passengers’ characteristics data. The performance improvement �� <�� >�� !�� (i) fuzzy TOPSIS - fuzzy AHP-ANOVA combining the operational characteristics data and the customer characteristic data; (ii) multivariate analyses of customer characteristic data, which include principal component analysis (PCA), discriminant analysis (DA), and multivariate analysis of variance (MANOVA); (iii) perceptual mapping (PM) with PCA and DA, (iv) performance importance matrix (PIM); and (v) a composite model integrating principal component analysis (PCA), and conjoint analysis (CA) with the traditional QFD. Table 2 gives the summary of the characteristics of the various models.

3.1 Operational characteristics data

The details of the sixteen sub-criteria used for data analysis are explained in Table 3 under three main criteria namely – safety, operation and cost/earnings. The data had been collected for a period of nine months from respective depot managers. Out of these data,

twelve would be quantitative and four are qualitative in nature.

3.2 Passenger feedback or customer characteristics data (qualitative)

During a period of one month the regular customers, �$�$`� �� Z�� ^�� >�� Z�}�to 60 years) and senior citizens (above 60 years) of the three depots were surveyed and their feedback is presented in Table 4. A customer survey was done from 150 customers each for all the three depots (50 each for all the age group).

4. Conceptual modelThe conceptual model concentrates on the applications of performance improvement tools like FTOPSIS and FAHP; factor analysis, discriminant analysis, MANOVA, perceptual mapping, PIM, and QFD in public transport company to gauge the overall performance. An overview of this study is presented as ��<��$��*��work are:

i. To rank the bus depots based on both operational characteristics and customer characteristics using FTOPSIS-FAHP-ANOVA model (Phase-I).

��$� �� >��importance rating using principal component analysis (PCA) model; develop a model to predict performance using discriminant analysis (DA); and identify the customer perceptions based on grouping criterion using multi-variate analysis of variance (MANOVA). Identify the best performing depot using all the above multi-variate analyses models (Phase-II).

iii. To ascertain the strengths / weakness of the depots using perceptual mapping (Phase-III).

iv. To develop a performance importance matrix (PIM) model by identifying difference in perceptions attached by the service providers’ and the customers’ for the service characteristics (Phase-IV).

v. To integrate principal component analysis (PCA), conjoint analysis (CA) and quality function deployment


(QFD) methodologies to improve the performance of passenger transport company and as a result achieve operational excellence (Phase-V).

This study could give a clear indicator to the management that it comes up with action plans both for short term as well as medium term for all the depots, to meet the expectations of customers. When the customers’ feedback is appropriately attended to, it in turn may be a customer retention strategy (short-term ��>�^��<��!��the transport in future, i.e., customer development ��Z��>�^$

5. Results and Discussion� �� <�� !��>� ��«�� >��!!��!��enumerated.

a. Phase– I

Phase – I, consisting of FTOPSIS-FAHP-ANOVA was used to analyse the combined data of the operational and customer characteristics. Operational characteristics (OC) data consists of three criteria and sixteen sub-criteria. In this data, twelve were quantitative and four were of qualitative in nature and were obtained from the depot managers. FTOPSIS-FAHP has been used to analyse OC data. Customer survey has been used for enumerating the customers’ characteristics (CC). The CC data consist of two criteria and eighteen sub-criteria and all are qualitative. One-way ANOVA has been used for analysing CC. The scores of the two analyses are then combined. The methodology which uses all the stake-holders inputs in the form of 34 factors (qualitative and qualitative), were considered to assess and decide the best and the worst among the various alternatives, i.e., depots of a bus passenger transport company. This sequential procedure has the advantage of allowing for a more systematic handling of the uncertainty of information and the vagueness of decision-makers’ feeling. The other advantage is that both quantitative and qualitative data can be handled together by the use of triangular fuzzy numbers and scales and through a process of normalization (Sezhian et al., 2011a).

b. Phase - II

This phase makes use of three multivariate analyses techniques namely PCA, DA and MANOVA to analyse the customer characteristics or passenger feedback data from three depots and to rank them.

(i) Principal component analysis (PCA) is a standard tool used in modern data analysis to validate the factors responsible for service quality in the transport and to rank the depots based on customer characteristic. In this study, eighteen service criteria were taken up for study and the feedback obtained from passengers of the three depots consisting of the three age groups Z�� Z"|^`� ��>�� Z_|^� ��senior citizen (SC)). All eighteen service criteria that were taken up for study, as customer characteristics ��>��PCA model and it also implies that all criteria are of importance to the passengers. PCA returned two factors that are christened as customer expectations (facilities and comfort) and the company responsibilities. The managerial implication of this methodology is that it has given a clear insight into the customer preferences and perspective. The PCA model has also been used to obtain the sub-criteria weights and further to rank the same (Sezhian et al., 2011b).

(ii) Subsequent to the PCA model, the multi- discriminant analysis (MDA) model is used as there are three groups or depots considered. In MDA, the categorical dependent variables are the three depots and the eighteen criteria are the independent variables. ��`��<��>��differences among the groups. In the present case the �� !�� <�� >�� one another. Secondly, the unstandardized canonical ��>��`�� `�are used to derive a function to get the discriminant scores and thus predict the performance of the depots. Lastly, it has been used for classifying the performance of the depots as “Good”, “Medium” or “Poor”. Thus the model has presented a ‘performance determination’ as well as prediction model using multivariate analysis namely multiple-discriminant analysis for a public bus transport company. It is aimed at helping the managers


as well as the company management to formulate their strategies for improvement (Sezhian et al., 2011e).

(iii) Next, MANOVA model was used for the three depots (depot-1, 2, and 3) with three age groups (CG, OG, and SC) as independent variables and with two criteria or quality dimensions, viz. customer expectations and company responsibilities as dependent variables. Here, two types of analyses were carried out - >��`�<��!��!��`�<��age-group as grouping criterion for each of the depots separately. The distinct feature noticed here is, that the various age groups feel differently about both customer expectation and company responsibility factors for each depot. Hence, the depot managers should evolve strategies to meet the expectations of the different age groups for their respective depots. The perceptions and requirements of the various age groups are different hence different approaches (strategies) need to be adopted for customer expectation and company responsibility.

c. Phase – III

Phase – III, consists of two models namely PM with PCA and DA and PIM. As result of applying this model the conclusions drawn are presented:

(i) The perceptual mapping with principal component analysis is used to ascertain the position of the three bus depots and assess their strengths and weakness with respect to the two factors or the two components viz., customer expectations and company responsibilities. �� !�� !�� >��taking in to view the two factors. (Sezhian et al., 2011d)

(ii) The perceptual mapping with DA is developed �� &�� >�� the group centroids (of three depots); it is also called the attribute based perceptual map. This map helps to ascertain the position of the depots and their criteria-wise proximity to the depots. The attribute that is positioned nearer to a depot (i.e., group centroids) is an area of strength or better performance and farther away is a sign of weakness or below expected performance. �� !�� !�� >��taking in to view all the eighteen attributes.

(iii) We can, hence conclude from the above two PM models, that the overall best performing depot could be decided as also the poor performing depot on some sub-criteria. Thus it is apparent the areas where the depots need to show improvements. All the areas of strengths of each of the depots ought to be maintained, and the others factors (a group of criteria / attributes) are weak or grey areas for these depots are to be improved. Thus strategies need to be formulated for improvement.

d. Phase – IV

In phase - IV, the PIM model, which is a potent tool in the hands of the managers to gauge whether their policies and strategies have worked or not. It can also be used to know the customers perceptions of importance to service attributes. The PIM is plotted as a scatter graph with four quadrants. The results help in identifying the factors in evaluating the service provided by the transport and evolving strategies for the future. The criteria in Quadrant-I, has to get the highest priority of the top management and it should keep a close watch of the same, as these are the customer retention criteria. The criteria in Quadrant-II are the ones that need immediate and focused attention from the front-line managers. These are the aspects which may have so far received lesser attention of the managers, but the customers have attached higher importance. If these are given priority it will not only keep the existing customers happy, but also bring in new passengers or customers. Hence, these are the customer development criteria. In Quadrant-III, the criteria are of low priority that can be managed by the second and lower level managers, as they are minor weakness. Those that are found in the Quadrant-IV are the criteria of least priority for which the resources need not be wasted and hence be diverted elsewhere. The customer retention �� !�� >��$�(Sezhian et al., 2011c)

e. Phase – V

In this phase, the composite model developed has used the QFD methodology for converting the customer requirements into quality characteristics for improving the quality of service in a public transport company. The customer characteristics were analyzed using


PCA, and conjoint analysis was applied to prioritize the technical descriptors. This QFD helps prioritise the �� !�� >�� requirements. First, the technical descriptors were inter-related with the customer requirements. Then, the technical assessments of the technical descriptors were analyzed using conjoint analysis and have yielded the following:

(i) There are four attribute groupings namely (a) maintenance strategy; (b) safety and other facilities; (c) training for bus crew and; (d) monitoring and complaints.

(ii) The attribute groups could account for the customer needs and could be taken up immediately as short term goals or later as long term goals for implementation.

(iii) From the above technical assessment, it may also be noted that this sector namely the passenger bus transport is a maintenance intensive sector and the ��>��!��#$

ConclusionsIn this a total conceptual model for a public transport company has been developed. The attributes have been subjected to factor analysis for data reduction, hence only the important ones are retained for further analysis. They are then prioritized using their importance values. Simultaneous analysis of bus depots is possible and so separate evaluating of service quality index for each depot and subsequent comparison has been avoided. Pictorial representation for all attributes is obtained along with bus depot positions simultaneously. Strength and weakness of each of the depots / companies can be obtained simultaneously. The readily available software, SPSS and MS-Excel could be applied for the analysis. In most analyses, only a few critical variables account for most of the variability or uncertainty. These variables are prime candidates for inclusion in the decision tree. Even a small change in value of any one attribute and its impact on depot performance can be analysed by applying the methodology discussed. The proposed methodologies could be extended to include more attributes and bus depots / companies.

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12. Kotri. A, (2006), Analysing customer value using Conjoint analysis: The example of a packaging company, University of Tartu, Faculty of Economics and Business Administration, ISBN 9949–11–479–9, Tartu University Press.

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17. Nargundkar R., (2008), Marketing Research, Tata McGraw Hill, New Delhi.

18. Nigam A., and Kaushik R., (2011), Attribute based- perceptual mapping of prepaid mobile cellular operators: An empirical investigation among management graduates in central Haryana, International Journal of Computational Engineering and Management, Vol.11, 82-92.

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25. Sezhian M.V., Muralidharan C., Nambirajan T., and Deshmukh S.G., (2011 a), Performance Measurement in a public sector passenger bus transport company using Fuzzy TOPSIS and ANOVA – A Case study, International Journal of Engineering Science and Technology (IJEST), Vol. 3(2), 1046-1059.

26. Sezhian M.V., Muralidharan C., Nambirajan T., and Deshmukh S.G., (2011 b), Ranking of a public sector passenger bus transport company using principal component analysis: A case study, Management Research and Practice, Vol. 3(1), 60-71.

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28. Sezhian M.V, Muralidharan C., Nambirajan T., and Deshmukh S.G., (2011 d), Developing a model to predict the performance of a public sector passenger bus transport company using factor and multiple-discriminant analysis: A case study, Industrial Engineering Journal, Vol. 2(26), 35-39.

29. Sezhian M.V, Muralidharan C., Nambirajan T., and Deshmukh S.G., (2011 e), Developing a model to predict the performance of a public sector passenger bus transport company using factor and multiple-discriminant analysis: A case study, Industrial Engineering Journal, Vol. 2(26), 35-39.

��$� ��>$� �� `� Farahani. R.Z, and Rezapour. S, (2010), Fuzzy AHP to determine the relative weights of evaluation criteria and Fuzzy TOPSIS to rank the alternatives, Applied Soft Computing, Vol. 10, 520–528.

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for developing new tourist destinations: The case of Rhodopi mountain TOURISMOS: An International multidisciplinary journal of Tourism, Vol. 1(1), 93-110.

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35. Wang. J.J, and Jing. Y.Y, (2008), A fuzzy multi-criteria decision-making model for trigeneration system, Energy Policy, Vol. 36, 3823–3832.

36. Yeh. C.H, Deng, Chang. Y.H, (2000), Fuzzy Multi-Criteria Analysis for performance evaluation of bus companies, European Journal of Operational Research, Vol. 126, 459-473.

37. Zarina Md Ali, Ismail. M, Suradi. N.R.M, and Ismail. A.S, (2009), Importance-performance analysis and customer satisfaction index for express bus services, IEEE World conference on Nature and Biologically Inspired Computing (NaBIC) 2009, 590-595.

Table 1 Highlights of the Literature review of the Performance improvement techniques used in this study

Performance improvement tools Authors Year Article Focus Area Application

Area

FAHP- FTOPSIS

Yeh 2000have included customer data for analysis of public transport companies with inputs from managers and customers for the same criteria

public transport

��>�� 2010 to evaluate the alternative options with respect to the user’s preference orders non-transport

Wang and Jing 2008 had applied fuzzy TOPSIS for developing a MCDM model for trigeneration system non-transport

PCA

Rahaman and Rahaman 2009 �� !� �� >�� !� ��<��

overall satisfaction and twenty service-quality attributes the railway

Kolanovic et al 2008 choosing the possible attributes affecting the perception of

service quality the port


Performance improvement tools Authors Year Article Focus Area Application

Area

DANargundkar 2008 for classifying new credit card applicant into high risk or

low risk non-transport

Gebauerb and Zedtwitz 2007 �� >�� "��

European manufacturing industry non-transport

MANOVA

Gulbro et al 2000�� >��>��<�� >�� !��activities

non-transport

Park et al 2001explored the differences in the high, medium and low performing supplier groups based on their use of quality management practices

non-transport

PM with PCA

Luyang and Weidong 2010 studied the perception of students on some attributes non-transport

Vassiliadis et al 2006 the proper tourist product characteristics and market

opportunities by the recipients of the tourist market non- transport

PM with DA

Nigam and Kaushik 2011 applied it for prepaid mobile cellular operators in India non- transport

Venkatesh and Nargundkar 2006 the service quality perceptions of domestic airline

consumers in India airlines

PIM

Zarina et al 2009 studied the customers satisfaction level towards the public transportation services provided by a bus company Bus transport

Mathesen and Solvoll 2010 have used it in a Norwegian ferry company ferry company

Rajesh et al 2010 for study of perceptions of 3PL managers and customers logistics

Kitcharoen 2004 studied the perceptions of students and management of Thailand private Universities non-transport

Radar chartAbreha 2007 �� !�� !�� !�� >��

Ethiopia public transport

Rajesh et al 2010 for study of perceptions of 3PL managers and customers logistics

QFD

Mazur and Hopwood II 2007 reviewed the transportation project development process

to yield improved transportation decision making transportation

Hsu et al 2009to improvement of service quality for container terminal operators to improve their service quality in a shipping company

shipping

Genevois and Gurbuzthe 2009 used in automotive sector for developing strategies automotive

sector

Pramod et al 2006 developed maintenance QFD for an automotive service station in south India

automotive sector

CAKotri 2006 for analysing customer value in a packaging company in

Estonia non-transport

Ong et al 2010 to examine how Malaysian consumers make decisions regarding a consumer durable products non-transport


Table 2 Summary of input and output characteristics of various models

S. No. Phase Model Input Model Output Phase Output

1 IFTOTSIS-

FAHP-ANOVA model

Operational characteristics (OC) and customer characteristics (CC)

Best performing depot based on OC and CC

Best performing depot based on OC

and CC. Overview of performance.

2

II

PCA model From phase-I - Customer characteristics (CC)

Data reduction and rank attributes

Best performing depot based on CC.3 DA model From PCA - 18 sub-criteria Model to predict and

classify performance

4 MANOVA From PCA - 2 criteria Age-group wise perception of performance

5

III

PM with PCA model From phase – II – 2 criteria Criteria-wise strength and

weakness of depots Best performing depot based on strength and weakness of depots.6 PM with DA

modelFrom phase – II –

18 sub- criteriaSub-criteria-wise strength and weakness of depots

7 IV PIM model From phase – III –18 sub- criteria

Customer retention criteria (CRC) and customer development criteria

(CDC)

Best performing depot based on CRC

and CDC. Overall performance.

8 VComposite

model (PCA – CA –QFD)

CC form phase – I, Planning matrix from Phase – II to IV and Conjoint analysis of Technical

descriptors

Prioritising the technical descriptors using QFD and

Conjoint analysis

Prioritise the technical descriptors with input

to OC in phase – I.

Table-3 Criteria for passenger feedback

Criteria Description

C1 – Customer Expectations

1 Bus punctuality (C11) arrival of the buses to bus stop as per timings

2 Seat comfort (C12) seats provided are comfortable for travel

3 Cleanliness (C13) upkeep of the buses such as dusting, cleaning, etc.

4 Lighting & entertainment (C14) provision of a lights, TV, radio/FM, DVD, etc.

5 '�<��Z"15) whether new buses are added periodically

6 Seating for handicapped (C16) are handicapped or physically challenged people provided separate seating

7 Seating for elderly (C17) Senior citizens provided separate seating

8 Issue of proper ticket (C18) whether conductor issues proper ticket

9 In time Issue of ticket (C19) whether conductor issues ticket immediately on boarding the bus

10 Issue of proper change (C110) whether conductor issues proper change (correct amount) for the ticket taken


Criteria Description

C2 – Company Responsibility

11 Stopping bus at correct place (C21) bus stopping at the assigned bus stops

12 Backup service during breakdown (C22) in case of breakdown whether a spare or backup bus is provided

13 Provision for luggage (C23) whether the lofted is provided or arrangement to put the luggage carried

14 _��>��Z"24) ��¡��<��<��>��`��!��`��$

15 First aid facility (C25) <��>��

16 Driver behaviour (C26) whether driver is kind or courteous

17 Conductor behaviour (C27) whether conductor is kind or courteous

18 Information to passengers (C28)adequate information about change of route, stops, schedule given to the passengers

Table-4 Operational characteristics obtained from depot mangers

Criteria Description Quantitative units Qualitative

C3 – Safety

1 Accident (C31) The number of accidents occurred (minor, major & fatal). Nos. –

2 Average vehicle age (C32)

The life of the bus on a particular date is the period from date of commissioning. The average age of buses at any point of time is the mean value for all buses.

Years –

3 Breakdown (C33) Breakdowns due to mechanical and electrical failures. Nos. –

4 Tyre problems (C34)No. of Breakdowns due to tyre problems such as tyre puncture, burst, etc. Nos. –

5 Maintenance inside the bus (C35)

Maintenance carried inside the bus to rectify problems in seating etc., reported by bus crew and upkeep of the bus. – Using 5-point

fuzzy scale

C4 – Operation

6 Fuel economy[KM/L] (C41)

Mileage given for the fuel used. Kilometers covered by fuel consumed by the bus. Km per Liter –

7 Engine oil consumption [EOC] (C42)

Total quantity of engine oil consumed by the bus (for top-up & change of oil for the bus). Liters/km –

8 �#��>��Z�"43)Ratio of effective kilometers to scheduled kilometers planned for operation. Percentage –

9 Fleet utility (C44)��!��&��`��$�$`��$ Percentage –

10 Loss of KM (C45)The unproductive bus travel, such as movement between stand & depot, for fuelling, back up trip during breakdown & accidents, transporting staff, etc

Km –

11 Autonomous maintenance (C46)

Maintenance carried by the bus crew such as checking radiator water, tyre pressure, etc. – Using 5-point

fuzzy scale


Criteria Description Quantitative units Qualitative

12 Daily maintenance (C47)

Daily maintenance carried out by the maintenance department such as oil top-up, – Using 5-point

fuzzy scale

13 Other preventive maintenance (C48)

The weekly, monthly and other preventive maintenance done by the maintenance crew – Using 5-point

fuzzy scale

C5 - Cost / Earnings

14 Variable cost (C51)Various cost incurred in operating the bus such as cost of fuel, oil, tyres, battery, spares, etc. Rs. –

15 Earnings per KM (C52)

Earnings that is generated for operating one effective Kilometer Rs. per Km –

16 Earnings per bus per Day (C53)

Earnings that is generated for operating one bus per day, i.e., ratio of total earnings during the period to average number of buses during the same period.

Rs./bus/day –

Figure 1: Flow chart for summary of research methodology for the study conducted in the public transport company

AcknowledgementThe authors would like to thank the Managing Director, Tamil Nadu State Transport Corporation (TNSTC), Villupuram and his subordinates for giving us the necessary permission for data collection in the Villupuram division.


Selection of Facility Location for Spinning Industry:Analytical Hierarchy Process (AHP)

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Preference Level Numerical Value

��!�� !�� 2#��!�� 3#��!�� 4��!�� 5��!�� %��!�� 7%��]��!�� 8 ]��!�� 9

Table 1: Standard Preference Scale

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3. CRITERIA FOR FACILITY LOCATION OF SPINNING INDUSTRY

��=��!��*��!��$��!��<$

Abbreviations Controllable factors�� ]��<��A2 ��]��=��A3 ��!��A4 ��A5 ��<��=��

Uncontrollable factors�� "��A7 |��!��A8 �!!��A9 "�� <��=��

Table-2 Criteria for Facility Location under Consideration

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�$� �!!�� `� �� <��=�� `�!��=��$

{$� "��"��<��<��=��<��!��!�� =�� $� "��<��!!��are important criteria.

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4. FACTORS RATING DECISION:�� !��

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�� ]� ��>�� !�� !�� <��$��>��>��!��$

�� !��<��$��¥��$¤��`� �� <�� >�� !�� ]�� $� [�� <�� !!��

Abb Controllable factors Preference NV

�� ]��<�� ]�� 9

A2 ��]��=�� %�� ]�� 8

A3 ��!�� %��!�� 7

A4 �� #��!�� 4

A5 ��<��=�� #��!�� 3

�� "�� Eq Pre �

A7 |��!�� !��

A8 �!!�� #��!�� 5

A9 "�� Str pre 5

�� <��=�� !�� 2

Table 3: Preference criteria of Facility Location under Consideration

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5. ALTERNATIVES CONSIDERED FOR SPINNING INDUSTRY LOCATION:

�� !�� `� �� #�� $� �� !�� `� ��!��!��*��>��$

��<��

a) Mumbai (M):� #�� `��`��#��`��

b) Pune (P):� �� `�'��=`��

c) Aurangabad (A):� �� `�`��$�[��

d) Solapur (S):� ��!�� !�� _��`�[��`��!�

e) Nanded (Na):� '�� `��'��`��

f) Nagpur (N):� �� >�� %�� ̄�� `� ��`� ��`��`��'��!��~��$��=��$

g) Chandrapur (C):� �� "��!�� [��`|�� |��`�"��!�`��'��!��~��$

h) Jalgaon (J):� �� ~��`��`'��$

i) Kolhapur (K):� �� !�`� ��$

6. RATING FOR THE ALTERNATIVES:

��̀ ��!�� !�� `�� !�� ]� �� !�� <��$

�� !�� !�!�� ]!��$

��`� �� `��!��<��

1) Pair wise comparison:

��>��!��<��!��]�� to ��>��$�$�¥��]��<��¤`

S J P M K A Na N CS � 0.5 0.25 �$�� 0.5 �$�� $�� $�� 0.5J 2 � 0.25 0.2 0.33 0.25 0.2 0.25 �P 4 4 � 0.5 0.33 0.5 0.33 0.25 0.33M 8 5 2 � 0.33 0.33 0.5 0.33 0.25K 2 3 3 3 � 0.33 0.25 0.33 0.5A 8 4 2 3 3 � � 0.5 �

Na 8 5 3 2 4 � � � �N 8 4 4 3 3 2 � � �C 2 � 3 4 2 � � � �

Sum 43 27.5 18.5 16.8 5.5 2.54 5.41 4.79 6.58

Table 4: Pair wise comparison of locations for “Proximity of Raw Material”

2) Normalized matrix:

~�� ]� <�� !�� &�� ]� �2 �� <� �� $�$� ��&��<��]��3.

S J P M K A Na N C Row avg.

S 0.02 0.02 �$�� $�� 0.09 0.05 0.02 0.03 0.08 0.03J 0.05 0.04 �$�� $�� $�� $�� 0.04 0.05 �$�} 0.04P 0.09 �$�} 0.05 0.03 �$�� 0.20 �$�� 0.05 0.05 0.10M �$�{ �$�� $�� $�� $�� $�� 0.09 0.07 0.04 0.12K 0.05 �$�� $�� $�� $�� $�� 0.05 0.07 0.08 0.13A �$�{ �$�} �$�� $�� 0.55 0.39 �$�� $�� $�} 0.26

Na �$�{ �$�� $�� $�� 0.73 0.39 �$�� $�� $�} 0.29N �$�{ �$�} 0.22 �$�� 0.55 0.79 �$�� $�� $�} 0.34C 0.05 0.04 �$�� 0.24 �$�� 0.39 �$�� $�� $�} 0.21

Table 5: Normalized matrix for “Proximity of Raw Material”


3) Resultant matrix:

�� &�� <�� ]�� $�[��<�� =� �� ]$� �� <��]��4��`��4= A� X A3��>��]�� '��]$

Wt.� 0.5 0.25 �$�� 0.5 �$�� $�� $�� 0.5 0.03 0.192 � 0.25 0.2 0.33 0.25 0.2 0.25 � 0.04 0.274 4 � 0.5 0.33 0.5 0.33 0.25 0.33 0.10 0.648 5 2 � 0.33 0.33 0.5 0.33 0.25 0.12 0.922 3 3 3 � 0.33 0.25 0.33 0.5 X 0.13 = 1.078 4 2 3 3 � � 0.5 � 0.26 1.658 5 3 2 4 � � � � 0.29 1.818 4 4 3 3 2 � � � 0.34 2.102 � 3 4 2 � � � � 0.21 1.41

Nmax = 10.06

Table 6: Resultant matrix for maximum Eigen value

"��'��]�<��]$

��`�'��]�¦��$��

'�<`�"��¦�Z'��]��^«�Z��^��¦��]$��]$

"�� ¦�Z��$��{^«�Z{��^� ¦��$��

�� ¦�Z�${��Z��^^«�� ¦�Z�${��Z{��^^«{� ¦��$}�

"�� ¦�"�«�� ¦��$��«�$}� = 0.0857

'�<��"��$��`��]� �� $� �$�$� �� acceptable.

��`� ��<�� !�� $��!��<�

A1 A2 A3 A4 A5 A6 A7 A8 A9 A10

S 0.03 �$�� 0.04 0.02 0.03 0.04 0.05 �$�� 0.03 0.03

J 0.04 0.05 0.07 0.04 0.04 0.04 0.07 0.05 0.03 0.05

P �$�� 0.08 �$�� $�� 0.08 0.20 0.09 �$�� 0.07 �$��

M �$�� 0.20 �$�� $�� 0.08 �$�� $�� $�} 0.22 �$��

K �$�� 0.04 �$�� $�� 0.25 0.05 �$�� $�� $�� 0.09

A �$�� $�� $�� 0.23 �$�� $�{ �$�� 0.24 �$�} �$��

Na 0.29 �$�� $�} 0.20 0.28 0.20 0.29 0.23 �$�} 0.23

N 0.34 0.40 �$�� $�� 0.28 �$�� 0.25 0.22 �$�{ 0.24

C �$�� 0.24 �$�� $�� $�� 0.29 0.37 0.20 0.25 �$��

N �$�� 0.75 �$�� $}� 2.00 �$�� $�� $�� $�� $��

Table 7: Alternative weightage for each criterion

�� <�� $

��>��!��<��!��]�� to ��$

A1 A2 A3 A4 A5 A6 A7 A8 A9 A10

A1 �$�� $�� $�{ 2.25 3.00 9.00 �$�� $}� �$�� 9.00

A2 0.89 �$�� $�� 2.00 �$�� 8.00 �$�� $�� $�� 8.00

A3 0.78 0.88 �$�� $�} 2.33 7.00 �$�� $�� $�� 7.00

A4 0.44 0.50 0.57 �$�� $�� 4.00 0.80 �$�� 0.80 4.00

A5 0.33 0.38 0.43 0.75 �$�� 3.00 �$�� 0.50 �$�� 3.00

A6 �$�� $�� $�� 0.25 0.33 �$�� 0.20 �$�� 0.20 �$��

A7 �$}� �$�� $�� $�} �$�� 5.00 �$�� 0.83 �$�� 5.00

A8 �$�� 0.75 �$�� $}� 2.00 �$�� $�� $�� $�� $��

A9 �$}� �$�� $�� $�} �$�� 5.00 �$�� 0.83 �$�� 5.00

A10 �$�� $�� $�� 0.25 0.33 �$�� 0.20 �$�� 0.20 �$��

Sum 5.44 6.13 7.00 12.25 16.33 49.00 9.80 8.17 9.80 49.00

Table 8: Pair wise comparison of Attributes


��&��<��]�� !��<��

A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 RawAvg

A1 �$�� $�� $�� $�� $�� $�� $�� $�� $�� $�� $��

A2 �$�� $�� $�� $�� $�� $�� $�� $�� $�� $�� $��

A3 �$�� $�� $�� $�� $�� $�� $�� $�� $�� $�� $��

A4 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08

A5 �$�� $�� $�� $�� $�� $�� $�� $�� $�� $�� $��

A6 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02

A7 �$�� $�� $�� $�� $�� $�� $�� $�� $�� $�� $��

A8 �$�� $�� $�� $�� $�� $�� $�� $�� $�� $�� $��

A9 �$�� $�� $�� $�� $�� $�� $�� $�� $�� $�� $��

A10 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02

Table 9: Normalized matrix for Attributes

�� &�� <�� ]� �3� �� $�[��<�� =� �� ]$� �� <��]��`��4= A� X A3��>��]�� '��]$

�$�� $�� $�} 2.50 5.00 �$�} �$�� 2.50 2.50 5.00

X

�$��

=

�${�

�$�� $�� 0.75 �$}� 3.00 0.75 �$�� $}� �$}� 3.00 �$�� $��

0.80 �$�� $�� 2.00 4.00 �$�� 0.80 2.00 2.00 4.00 �$�� $}}

0.40 �$�� 0.50 �$�� 2.00 0.50 0.40 �$�� $�� 2.00 0.08 0.77

0.20 0.33 0.25 0.50 �$�� 0.25 0.20 0.50 0.50 �$�� $�� 0.39

0.80 �$�� $�� 2.00 4.00 �$�� 0.80 2.00 2.00 4.00 0.02 �$}}

�$�� $�� $�} 2.50 5.00 �$�} �$�� 2.50 2.50 5.00 �$�� ${�

0.40 �$�� 0.50 �$�� 2.00 0.50 0.40 �$�� $�� 2.00 �$�� 0.77

0.40 �$�� 0.50 �$�� 2.00 0.50 0.40 �$�� $�� 2.00 �$�� 0.77

0.20 0.33 0.25 0.50 �$�� 0.25 0.20 0.50 0.50 �$�� 0.02 0.39

Nmax. ��$��

Table 10: Resultant matrix for maximum Eigen value

�� "�� ]!��!��$

"��¦�$��¦�$}��"�¦�$��}

'�<�"�� $�� `� �� ]� �� $�$� �� !��$��<��<��!�� !�� $� �� !�� <��`

Sr. no. Attributes Weight

�� ]��<�� $��

A2 ��]��=�� $��

A3 ��!�� $��

A4 �� 0.082

A5 ��<��=�� $��

�� "�� 0.020

A7 |��!�� $��

A8 �!!��¡�� $��

A9 "�� $��

�� <��=�¡�� 0.020

Table 11: Attribute weightage

'�<`��>��!��<��!��$�Z��`��^

�$�$�>��'��!��`

¦��$��}�¼��$��½�$��}��¼��$��}½�$��{��¼��$��}�½�$��{��{{�¼��$��

½��$��{��¼��$��½�$�}{}�{��¼��$��½�$��¼��$��½�$��{{��¼��$��{�½��$�{��{�¼��$��½��$��{��{�¼�0.020408

= 0.075427


Region Final Rating Preferences

'��!� 0.075427 �

'�� 0.072322 2

�� $��}�} 3

��!� 0.050255 4

�� $��}�� 5

�� $��

#�� $�� 7

"��!� 0.040288 8

��!� 0.037537 9

Table 12: Overall weightage

Conclusion:� �� !�� `� <�� !�� ¥<��¤� ��&�� *��`� !��`� �� $� �� ]!�� >�� =��$� �� !�!�� !��!�� !�� !�� =�� $��%�� `� �̄��`��=��`�[��`��!��$

��`��*��<��!$��!��$

_�� ]�� *�� <�� !�� <�� >��%��$

��`��<��<��<�� `� �� =�`� �� `��!��$

'�� !��!�� !��!��]��=��!��$

��!��]�� =� �� =�� &��$��]�� =��<�� <�� $��`� ��!�� !��*�� !�� $� �� !�� *��<��$

��!!��!!�� $� ��<��`� �� !!��!��<��!��!�<��$

Reference:�$� [$~�� ¡� "$� ��

��¥��¤��"��_!��$�#��{{�`

�$� "�=��<�� =�� !�� *��¤�� ¤�� `�%��¡��'��!��!��

�$� "�=��<�� =�� ¡~�� [��=¤#��¤�� `�%��]]]��¡��'��~��}��!��

�$� ��=��=��~��[��=�¥�� !�� !�� !�� !��¤� *�� %��¼¼¼%�$��$��

}$� �� "��$�� !�� *��>�� !��¤�!��!��$��Z�^��{}��}


�$� �� $�� !�� #|��<�� "��!��`� '�<� ��=` �`��{��

�$� �$"$� ��!��`� �$'$� ��¥��!�� ¤��{{��#�|��<��"��!��$

�$� [�� '��=� "��!�� !�� #��{{��{}`��}th��!��$

{$� ��!�� |$� #��=�� ¥�� _!��#��¤�� #�|��<�� "��!��$

��$� �$'$� "�� ¥�� _!��#��¤�� #�|��<�� "��!��$

��$� [��<�`� �$� �� |��`� ~$� Z�{��^$�� >��#��!!��#��!��$�� $��Z�^`��$

��$� ��=� ��$� Z��}^$� �� Approach.

��$� �� ̄�� Z�{{�^$� ��


UDYOG PRAGATIThe Journal for Practising Managers

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GENERAL MANAGEMENT

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2 � ��'|�� 3 02.07.2012 "��~��¯��'��'

3 #�'�| ��_�� "�'�"�� _'' � 5 09.07.2012 ��%��%��~��

4 ¼" �� '" ��'�"_##'�"��_' 3 16.07.2012 # ��'��

5 � ��_'�� "��% ' ��_��_� 3 23.07.2012 '��

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8 �� ¡��# �#�'�| # '��_��#�'�| �� 5 13.08.2012 #��_��~� �̄¯��

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5 20.08.2012 ��%��%��~��

10 ¼" �� '" ��'�"_##'�"��_' 3 20.08.2012 # ��'��

11 "_#� ��% �� |¯ 3 27.08.2012 �_#�� [��"��¯ �̄

12 #�'�| �� ~ ��¡�� #�[��~�'| 3 03.09.2012 '��

13 #�'�| �� "��% ' �� 3 03.09.2012 ��~��

14 �~�� '�� ' �� ~"��_' 3 01.10.2012 ��~��«�~�# ��#

15 "_'_#�"��_��#�'�| �� 3 08.10.2012 "��_��~� �̄¯��

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18 �� "��% �� '��_'��'~�"_##'�"��_'�� 5 05.11.2012 BHASIN H V

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5 03.12.2012 BHASIN H V

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26 ��'�'" ��_�� '|�' �� 5 10.12.2012 [��

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INDUSTRIAL SAFETY AND ENVIRONMENTAL MANAGEMENT30 SUSTAINABILITY STRATEGIES IN PUBLIC SECTOR

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49 MANAGERIAL DECISION MAKING FOR TECHNICAL PERSONNEL

3 30.07.2012 GANAPATHY L

50 SIX SIGMA FOR BUSINESS EXCELLENCE 3 13.08.2012 MADDULETY K

51 PLANT LEVEL ENERGY CONSERVATION PRACTICES 5 20.08.2012 BISWAS P K

52 PROJECT MANAGEMENT 3 03.09.2012 GANAPATHY L

53 SUPPLY CHAIN MANAGEMENT FOR BUSINESS EXCELLENCE

3 24.09.2012 VERMA RAKESH

54 PROJECT MANAGEMENT 3 08.10.2012 KHANAPURI V B

55 LOGISTICS & DISTRIBUTION MANAGEMENT 3 08.10.2012 KAMBLE SACHIN

56 SIX SIGMA FOR BUSINESS EXCELLENCE 3 19.11.2012 GHOSH SADHANA (Mrs)

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58 MANUFACTURING MANAGEMENT 3 03.12.2012 KAMBLE SACHIN

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