INTERPRETIVE STRUCTURAL MODELING BASED APPROACH FOR ADOPTING CPFR IN INDIAN INDUSTRIES

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A Monthly Double-Blind Peer Reviewed (Refereed/Juried) Open Access International e-Journal - Included in the International Serial Directories

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VOLUME NO.2(2012),ISSUE NO.12(DECEMBER) ISSN2231-5756

INTERNATIONAL JOURNAL OF RESEARCH IN COMMERCE, IT & MANAGEMENT

CONTENTS

Sr.

No.

TITLE & NAME OF THE AUTHOR (S)

Page No. 1. EFFECTIVENESS OF PAY-FOR-PERFORMANCE AND FIXED-PAY PRACTICES: AN ASSESSMENT OF PAY SATISFACTION, COMMITMENT AND

TURNOVER INTENTION

PRINCY THOMAS & DR. G. NAGALINGAPPA

1

2. ROLE OF CORPORATE GOVERNANCE ON PERFORMANCE OF PRIVATE COMMERCIAL BANKS IN BANGLADESH: AN ECONOMETRIC ANALYSIS

DR. MD NAZRUL ISLAM, MOHAMMAD MASUD ALAM & MOHAMMAD ASHRAFUL FERDOUS CHOWDHURY

6

3. IDENTIFYING OPPORTUNITIES, CHALLENGES AND INFRASTRUCTURE REQUIREMENTS FOR ESTABLISHING SECONDARY MARKETS IN ETHIOPIA

KANNAN SIMHAKUTTY ASURI & LETENAH EJIGU

12

4. A NOVEL BANKRUPTCY PREDICTION MODEL BASED ON SUPPORT VECTOR DATA DESCRIPTION METHOD

ALIREZA DEHVARI, FEZEH ZAHEDI FARD & MAHDI SALEHI

17

5. ANALYSIS OF FACTORS INFLUENCING EXPORT VOLUME: THE NIGERIAN EXPERIENCE

KAREEM, R.O, OKI A.S, RAHEEM, K.A & BASHEER, N.O

24

6. A MODEL FOR ORGANIZING, MEASURING, ANALYZING STUDENTS’ KNOWLEDGE AND PERFORMANCE

ROY MATHEW

32

7. DETERMINANTS OF CUSTOMER LOYALTY AND SUBSCRIBER CHURN OF MOBILE PHONE SERVICES IN GHANA

JACOB NUNOO & CHRISTIAN KYEREMEH

38

8. FACTORS AFFECTING CUSTOMERS’ ATTITUDE TOWARDS INFORMATION TECHNOLOGY ADOPTION IN COMMERCIAL BANKS OF ETHIOPIA: A CASE STUDY OF SELECTED BANKS IN MEKELLE CITY

ZEMENU AYNADIS

42

9. EFFECTIVE USE OF TRAINING FEEDBACK FOR REINFORCEMENT OF LEARNING AND EMPLOYEE DEVELOPMENT

AJAY KR VERMA, SUDHIR WARIER & LRK KRISHNAN

53

10. IMPACT OF DEMOGRAPHIC VARIABLES ON FACTORS OF JOB SATISFACTION OF EMPLOYEES IN PUBLIC SECTOR: AN EMPIRICAL STUDY

DR. RIZWANA ANSARI, DR. T. N. MURTY, NILOUFER QURAISHY & S A SAMEERA

62

11. SUBSCRIBERS’ ATTITUDE TOWARDS DTH SERVICES

M. J. SENTHIL KUMAR & DR. N. R. NAGARAJAN

69

12. ISSUES AND CHALLENGES INDIAN BUSINESS: VISION 2020 WITH THE REFERENCE OF MICRO, SMALL AND MEDIUM ENTERPRISES (MSMEs) IN INDIA

DR. M. L. GUPTA, DR. SHWETABH MITTAL & PRIYANKA GUPTA

73

13. ENHANCING JOB SATISFACTION OF SOFTWARE PROFESSIONALS: THE RELEVANCE OF EMOTIONAL QUOTIENT

V. ANOOPKUMAR & DR. R. GANESAN

82

14. A SURVEY ON CONSUMER ATTITUDE TO CHOOSE AND USE VARIOUS TELECOM SERVICES

V. BALAKUMAR & DR. C. SWARNALATHA

88

15. COUNTERPRODUCTIVE WORK BEHAVIOUR (CWB) AND LOCUS OF CONTROL (LOC) AMONG MANAGERS

DR. RISHIPAL & PAWAN KUMAR CHAND

94

16. CORPORATE GOVERNANCE FAILURES IN INDIA - A REVIEW

KAISETTY. BALAJI & DR. Y. VENU GOPALA RAO

98

17. SIGNIFICANCE OF INCLUSIVE GROWTH IN INDIAN ECONOMIC DEVELOPMENT – A STUDY

DR. T. C. CHANDRASHEKAR

103

18. A STUDY ON EMPLOYEE JOB PERFORMANCE (A COMPARATIVE STUDY OF SELECT PUBLIC AND PRIVATE ORGANIZATIONS)

S.FAKRUDDIN ALI AHMED & DR. G. MALYADRI

110

19. ORGANISATIONAL AND ENVIRONMENTAL DETERMINANTS OF PERFORMANCE APPRAISAL SYSTEM: A REVIEW AND FRAMEWORK FROM CONTEXTUAL PERSPECTIVE

SAPNA TANEJA, DR. RAVIKESH SRIVASTAVA & DR. N. RAVICHANDRAN

117

20. E-LEARNING INITIATIVES TO AUGMENT BUSINESS PERFORMANCE: AN EMPIRICAL STUDY OF SELECT AUTO COMPONENT FIRMS

DR. AISHA M. SHERIFF & GEETHA R

127

21. INTERPRETIVE STRUCTURAL MODELING BASED APPROACH FOR ADOPTING CPFR IN INDIAN INDUSTRIES

RAJESH A. KUBDE & DR. SATISH V. BANSOD

136

22. TECHNOLOGY TRENDS AND IMPACT OF ROBOTICS IN THE CORPORATE WORLD AT DIFFERENT LEVELS OF MANAGEMENT

P. POONGUZHALI & DR. A. CHANDRA MOHAN

141

23. PERFORMANCE APPRAISAL ACT AS A MAJOR MOTIVATIONAL SOURCE

NAILA IQBAL

147

24. FOREIGN DIRECT INVESTMENT FLOWS INTO INDIA AND THEIR CAUSAL RELATIONSHIP WITH ECONOMIC GROWTH SINCE LIBERALISATION

S. GRAHALSKSHMI & DR. M. JAYALAKSHMI

150

25. INCLUSIVE GROWTH AND REGIONAL DISPARITIES IN ANDHRA PRADESH

V. VANEENDRA NATHA SASTRY

159

26. STRATEGIES TO COPE UP WORK - PLACE STRESSORS: AN EMPIRICAL STUDY IN EDUCATIONAL INSTITUTIONS

B. LAVANYA

162

27. DETERMINANTS OF JOB SATISFACTION AMONG EMPLOYEES IN INFORMATION TECHNOLOGY INDUSTRY IN DELHI

BRAJESH KUMAR & DR. AWADHESH KUMAR

166

28. MODERN CHALLENGES TO WOMEN ENTREPRENEURSHIP DEVELOPMENT: A STUDY OF DISTRICT RAJOURI IN JAMMU AND KASHMIR STATE

AASIM MIR

169

29. INTERNATIONAL HRM CHALLENGES FOR MNC’s

B. G. VENKATESH PRASAD & N. CHETAN KUMAR

173

30. INSIDER TRADING: GOVERNANCE, ETHICAL AND REGULATORY PERSPECTIVE

NIDHI SAHORE

177

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CHIEF PATRON

PROF. K. K. AGGARWAL

Chancellor, Lingaya’s University, Delhi

Founder Vice-Chancellor, Guru Gobind Singh Indraprastha University, Delhi

Ex. Pro Vice-Chancellor, Guru Jambheshwar University, Hisar

FOUNDER

FOUNDER PATRON

PATRON

LATE SH. RAM BHAJAN AGGARWAL

Former State Minister for Home & Tourism, Government of Haryana

Former Vice-President, Dadri Education Society, Charkhi Dadri

Former President, Chinar Syntex Ltd. (Textile Mills), Bhiwani

CO

CO----ORDINATOR

ORDINATOR

AMITA

Faculty, Government M. S., Mohali

ADVISORS

DR. PRIYA RANJAN TRIVEDI

Chancellor, The Global Open University, Nagaland

PROF. M. S. SENAM RAJU

Director A. C. D., School of Management Studies, I.G.N.O.U., New Delhi

PROF. M. N. SHARMA

Chairman, M.B.A., Haryana College of Technology & Management, Kaithal

PROF. S. L. MAHANDRU

Principal (Retd.), Maharaja Agrasen College, Jagadhri

EDITOR

PROF. R. K. SHARMA

Professor, Bharti Vidyapeeth University Institute of Management & Research, New Delhi

CO

CO----EDITOR

EDITOR

DR. BHAVET

Faculty, M. M. Institute of Management, Maharishi Markandeshwar University, Mullana, Ambala, Haryana

EDITORIAL ADVISORY BOARD

DR. RAJESH MODI

Faculty, Yanbu Industrial College, Kingdom of Saudi Arabia

PROF. SANJIV MITTAL

University School of Management Studies, Guru Gobind Singh I. P. University, Delhi

PROF. ANIL K. SAINI

Chairperson (CRC), Guru Gobind Singh I. P. University, Delhi

DR. SAMBHAVNA

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VOLUME NO.2(2012),ISSUE NO.12(DECEMBER) ISSN2231-5756

DR. MOHENDER KUMAR GUPTA

Associate Professor, P. J. L. N. Government College, Faridabad

DR. SHIVAKUMAR DEENE

Asst. Professor, Dept. of Commerce, School of Business Studies, Central University of Karnataka, Gulbarga

ASSOCIATE EDITORS

PROF. NAWAB ALI KHAN

Department of Commerce, Aligarh Muslim University, Aligarh, U.P.

PROF. ABHAY BANSAL

Head, Department of Information Technology, Amity School of Engineering & Technology, Amity

University, Noida

PROF. A. SURYANARAYANA

Department of Business Management, Osmania University, Hyderabad

DR. SAMBHAV GARG

Faculty, M. M. Institute of Management, Maharishi Markandeshwar University, Mullana, Ambala, Haryana

PROF. V. SELVAM

SSL, VIT University, Vellore

DR. PARDEEP AHLAWAT

Associate Professor, Institute of Management Studies & Research, Maharshi Dayanand University, Rohtak

DR. S. TABASSUM SULTANA

Associate Professor, Department of Business Management, Matrusri Institute of P.G. Studies, Hyderabad

SURJEET SINGH

Asst. Professor, Department of Computer Science, G. M. N. (P.G.) College, Ambala Cantt.

TECHNICAL ADVISOR

AMITA

Faculty, Government M. S., Mohali

FINANCIAL ADVISORS

DICKIN GOYAL

Advocate & Tax Adviser, Panchkula

NEENA

Investment Consultant, Chambaghat, Solan, Himachal Pradesh

LEGAL ADVISORS

JITENDER S. CHAHAL

Advocate, Punjab & Haryana High Court, Chandigarh U.T.

CHANDER BHUSHAN SHARMA

Advocate & Consultant, District Courts, Yamunanagar at Jagadhri

SUPERINTENDENT

SURENDER KUMAR POONIA

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Bowersox, Donald J., Closs, David J., (1996), "Logistical Management." Tata McGraw, Hill, New Delhi.

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Hunker, H.L. and A.J. Wright (1963), "Factors of Industrial Location in Ohio" Ohio State University, Nigeria. CONTRIBUTIONS TO BOOKS

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Sharma T., Kwatra, G. (2008) Effectiveness of Social Advertising: A Study of Selected Campaigns, Corporate Social Responsibility, Edited by David Crowther & Nicholas Capaldi, Ashgate Research Companion to Corporate Social Responsibility, Chapter 15, pp 287-303.

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Garg, Sambhav (2011): "Business Ethics" Paper presented at the Annual International Conference for the All India Management Association, New Delhi, India, 19–22 June.

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Kumar S. (2011): "Customer Value: A Comparative Study of Rural and Urban Customers," Thesis, Kurukshetra University, Kurukshetra. ONLINE RESOURCES

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INTERPRETIVE STRUCTURAL MODELING BASED APPROACH FOR ADOPTING CPFR IN INDIAN INDUSTRIES

RAJESH A. KUBDE

ASSOCIATE PROFESSOR

MECHANICAL ENGINEERING DEPARTMENT

PROF. RAM MEGHE INSTITUTE OF TECHNOLOGY & RESEARCH

BADNERA- AMRAVATI

DR. SATISH V. BANSOD

PROFESSOR

MECHANICAL ENGINEERING DEPARTMENT

PROF. RAM MEGHE INSTITUTE OF TECHNOLOGY & RESEARCH

BADNERA- AMRAVATI

ABSTRACT

Collaborative planning forecasting and replenishment (CPFR) is the latest technology offers great potential to improve supply chain effectiveness by making a balance between the necessary customer demand and the sufficient product supply. This paper is concerned with the identification of the variables that are important for CPFR adoption. An empirical survey was conducted among Indian industries to investigate the knowledge about CPFR adoption. In order to understand key variables in CPFR implementation, interpretive structural modeling approach have been applied. This structure portrayed to identify mutual relationship between the variables. The aim of this paper is to develop a framework to improve business performance. Such an integrated framework has significant insights into relationship between variables and enables a better understanding of important factors necessary for CPFR adoption in Indian industries.

KEYWORDS

supply chain management (SCM), collaborative planning forecasting and replenishment (CPFR), interpretive structural modeling (ISM).

1. INTRODUCTION

n the context of a dynamic supply chain, improving business performance has become a critical issue for most of the Indian industries. A recent survey on supply chain management practices in India shows that, now, Indian companies is aligning supply chain strategy with business strategy for minimizing inventories (Sahay B.S. et al. 2006). CPFR is one of those supply chain strategy. The term CPFR was first introduced in 1995 in connection with a pilot project between fMart, Warner-Lambert, Benchmarking partners, SAP and Manugistics (Cooke, 1998). The CPFR initiative has proven very valuable to Wal-Mart. The company has a joint initiative with P&G where managers from both companies jointly forecast sales of P&G products at Wal-Mart stores and then jointly plan replenishment strategies (Chopra and Meindl, 2001). In CPFR, a business practice combines the intelligence of multiple partners in the planning and fulfillments of customer demand. The benefits resulting from a successful application of CPFR include reduction in stock-outs, improved inventory management, shorter cycle times, increase in sales revenues, stronger relationships between trading partners, better overall system visibility, customer service and improved cost structures (VICS, 1998).

Most companies and industries can benefit from CPFR. However, companies that experience variation in demand buy or sell a product on a periodic basis and those deals in highly differentiated or branded products will benefit the most (Attaran and Attran, 2007). CPFR adoption in Indian industries is not an easy task. For every organization adoption issue, such as objectives for adoption, strategic drivers of adoption, obstacles for adoption and affected operational areas are extremely important (Borade and Bansod, 2010). Hence, without establishing mutual correlation between these variables, many organizations may not recognize its full potential.

This paper mainly focuses on the factors influencing CPFR adoption and looks forward to develop widely accepted criteria to evaluate successful adoption of CPFR efforts for Indian organizations. It attempts to understand a framework of interrelationships of mutually involved variables. In order to understand key factors in adopting CPFR, an interpretive structural modeling approach have been applied for the design of the framework.

2. LITERATURE REVIEW

In fiercely competitive markets, now many organizations in India started adopting SCM process. The objective of this research is to design CPFR framework. Therefore we studied the research issues related to CPFR. Following sections provide the general issues that are involved in CPFR adoption, which are later employed in ISM. According to Stank et al. (1999), CPFR is a collaborative initiative aimed at “making inventory management more efficient and cost-effective, while improving customer service, and leveraging technology to significantly improve profitability”. By following CPFR, companies can dramatically improve supply chain effectiveness with demand planning, synchronized production scheduling, logistic planning and new product design (Attaran and Attaran, 2007). Lack of visibility of true consumer demand and collaborative relationships based upon joint decision making remains significant barriers to the goal of supply chain integration. CPFR is a strategy which promises to overcome these barriers (Barratt and Oliveira, 2001). Tage Skjoett-Larsen et al. (2003) found the reason why a firm enters into basic CPFR collaboration is the advantage of increased information exchange, minimizing the cost of transactions. Further CPFR collaboration has a network approach focusing primarily on generation of trust in the relationships. Fliedner (2003) found various obstacles to CPFR implementation in the literature, these are; lack of trust in sharing sensitive information, lack of internal forecast collaboration, availability and cost of technology/expertise, fragmented information sharing standards, aggregation concerns (number of forecasts and frequency of generation) and fear of collusion. Albright (2002) contended that CPFR implementation can reduce inventory levels, increase sales, and improve trade partnerships. Thus the literature helps to identify driver/enablers and obstacles related to CPFR. Sections below provide the research methodology for finding out objectives necessary for CPFR adoption, which are later employed in ISM.

3. RESEARCH METHODOLOGY

The CPFR literature emphasize on several criteria as an essential prerequisite. But this research focuses on two objectives as; 1) To find out most important objectives necessary for CPFR adoption

2) To establish a structure of inter-relationships between these objectives.

In this study the objectives were identified from a survey of Indian industries. The questions pertaining to objectives were asked. We surveyed the questionnaire in which 52 questions were presented in a closed-ended and open-ended format. The entire questionnaire consisted of three parts: (1) CPFR adoption, (2) Obstacles, (3) Effect. This may help respondents to browse the questions easily. A nominal question (Yes/No) was asked from respondents in order to clarify whether they have adopted CPFR or not. Respondents were also requested to assign a Likert scale score (5=very high to very low=1) to give importance and

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VOLUME NO.2(2012),ISSUE NO.12(DECEMBER) ISSN2231-5756

weight age to each criterion. In order to respond to the research questions, the Interpretive Structural Modeling (ISM) was employed to extract patterns from data (RezaSigari Tarbrizi et al., 2010).

4. INTERPRETIVE STRUCTURAL MODELING (ISM)

An ISM is a qualitative tool that was developed by Warfield with the objective of understanding the complex relationships among elements related to a subject (Sahney, 2008). According to Singh and Kant (2008), ISM is interpretive as based on group’s judgment and decision whether and how the system’s elements are linked. It is structural as constructed on the relationship’s foundation and final structure is exploited from complex set of systems variables. The adoption of CPFR in an organization depends on objectives. This issue is strongly correlated. The existing autonomous, direct linkages and indirect linkages among the element are identified. Based on primacy, precedence and causality, this element is structured into a multilevel structure. The ultimate result presents a hierarchical structure of the element that is representative of their networks and relationships, with their classification into various levels (Singh and Kant, 2008; Borade and Bansod, 2011).

4.1 STRUCTURAL SELF-INTERACTION MATRIX (SSIM)

For analyzing the adoption objectives in developing an SSIM, the following four symbols have been used to denote the direction of relationship between the variables (Singh and Kant, 2008);

V variable i will help to achieve variable j, A variable j will help to achieve variable I,

X variable i will and j will help to achieve each other and O variable i and j are unrelated.

Based on contextual relationships, the structural self interaction matrix is developed.

TABLE 1: STRUCTURAL SELF-INTERACTION MATRIX (SSIM)

Sr.No. Objectives 11 10 9 8 7 6 5 4 3 2 1

1 Improved communication/ IT adoption V V V V V X X O V V

2 Enhanced information sharing V V V X V A X V V

3 Improved forecast accuracy O V V O V X O V

4 Improved production planning V V V V V V O

5 Enhanced collaboration X V V V V X

6 Improved business performance A X X V V

7 Reduced lead time V V V X

8 Improved responsiveness X V V

9 Improved ordering/ replenishment process V V

10 Reduced inventory/ improved inventory management V 11 Improved channel relations

4.2 REACHABILITY MATRIX

The SSIM has been converted into a binary matrix called the initial reachability matrix by substituting V, A, X and O by 1 and 0 as per case. The substitution of 1’s and 0’s are as per the following rules;

1) If the (i, j) entry in the SSIM is V, the (i, j) entry in the reachability matrix becomes 1, and the (j, i) entry becomes 0, 2) If the (i, j) entry in the SSIM is A, the (i, j) entry in the reachability matrix becomes 0, and the (j, i) entry becomes 1, 3) If the (i, j) entry in the SSIM is X, the (i, j) entry in the reachability matrix becomes 1 and the (j, i) entry also becomes 1, 4) If the (i, j) entry in the SSIM is O, the (i, j) entry in the reachability matrix becomes 0, and the (j, i) entry also becomes O.

After exploiting initially reachability matrix, driving power and dependence power were calculated for each objective. The driving power for each variable is the total number of variables (including itself), which it may help achieve. Dependence power is the total number of variables (including itself), which may help achieve it (Jharkharia and Shankar, 2005).

TABLE 2: REACHABILITY MATRIX

Objectives 1 2 3 4 5 6 7 8 9 10 11 Driving Power

1 1 1 1 0 1 1 1 0 1 1 1 9

2 1 1 1 1 1 1 1 1 1 1 0 10

3 0 0 1 1 0 1 1 0 1 1 0 6

4 0 0 0 1 0 1 1 1 1 1 1 7

5 1 1 0 0 1 1 1 1 1 1 1 9

6 1 1 1 1 1 1 1 1 1 1 0 10

7 0 0 0 1 0 1 1 1 1 1 1 7

8 1 1 0 1 1 1 1 1 1 1 1 10

9 0 1 0 1 0 1 1 1 1 1 1 8

10 0 0 0 1 0 1 0 0 1 1 1 5

11 1 1 0 1 1 1 0 1 0 1 1 8

Dependence 6 7 4 9 6 11 9 8 10 11 8

4.3 LEVEL PARTITIONS

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Objectives Reachability Set

1 1,2,3,5,6,7,8,9,10,11

2 1,2,3,4,5,6,7,8,9,10,11

3 3,4,6, 9,10

4 4,6,7,8,9,10

5 1,2,5,6,7,8,9,10,11

6 6,7,8,9,10

7 6,7,8,9,10,11

8 5,6,7,8,11

9 6,7,9,10

10 6,10

11 1,2,5,6,8,11

4.4 CLASSIFICATION OF VARIABLES

The ISM technique presents a hierarchical structure that depicts the direct and indirect

precedence and causality over and among each other. There exists a group of design characteristics, which are critical to the

maximum detail and attention. They have the highest driving force and the lowest dependence in the system. These are drivers. Another set of design characteristics has high dependence. These are the dependents. Between these two, there are the linkage and autonomous variab

characteristics are dependent on the drivers, but assist the existence and functioning of dependents (Sahney, 2008). Dependin dependence power, all the variables are categorized as autonomous, dependent, linkage and indepen

the objective improved communication/IT adoption has driving power 9 and dependence power of 6 hence it is kept at a position

power of 9 and dependence power of 6. The first cluster includes “autonomous variables” that have weak driver power and weak dependence. These variables are relatively disconnected from the system. The second cluster consists of the dependent variables that have a weak driver p

Third cluster has the linkage variables that have strong drive power and dependence. Any action on these variables will have feedback effect on themselves. The fourth cluster includes independent variables with st

2005).

The findings from the application of the ISM technique are as follows; Cluster 1: autonomous variables

None

Cluster 2: dependent variables

Reduced inventory/improved inventory management Cluster 3: linkage variables

Improved communication, enhanced collaboration, enhanced information sharing, improved responsiveness, improved channel relat planning, reduced lead time, improved ordering/replenishment process, improv

Cluster 4: independent variables Improved forecast accuracy

4.5 FORMATION OF ISM BASED MODEL

Using the final attrition of reachability matrices, a structural model is developed. The variables connected by arrows shows This graph is called as an initial directed graph or initial diagraph. These diagraphs are principally hierarchical results o iteration of reachability matrices. By applying ISM methodology the

those are level I elements (Anantatmula and Kanungo, 2005). Once the level I is determined, it is removed and then next same next level elements (Singh and Kant, 2008). Table III shows that, objective 10 is a level I objective. Therefore it is at the to and 8 is a level II objective so it is below objective 10. Objectives; 9, 7 and 6 comes as

3 and 4 are level IV, objective 5 is level V and objectives 1 and 2 are level VI objectives respectively. All objectives are the relationship among the variables.

FIG.

5. DISCUSSIONS

The analyzed contingent factors were limited to these variables because it was felt that they are most directly linked to CPF the literature and as emerged from investigated cases (Danese, 2007). In order to have a common f

observed that entire 11 objectives are important for CPFR adoption, although in varying degrees. By adopting ISM, we found th

of objectives irrespective of type of industry. Fliedner (2003) research suggested that CPFR would become an indispensable instrument in any future supply chain. Our study shows information sharing and improved communication with IT adoption as the supreme objective of CPFR adopt

has a high dependence and low driving power. Literature reveals that the crux of CPFR is real time information sharing and co Stank et al. (1999) evaluated CPFR attempts to lessen the problem associated with

11 10 9 8 7 6 5 4 3 2 1 0 1

TABLE 3: LEVEL PARTITIONS

Reachability Set Antecedent Set Intersection Set Level

1,2,3,5,6,7,8,9,10,11 1,5,11 1,5,11 VI

1,2,3,4,5,6,7,8,9,10,11 1,2,5,11 1,2,5,11 VI

3,4,6, 9,10 1,2,3 3 IV

4,6,7,8,9,10 2,3,4 4 IV

1,2,5,6,7,8,9,10,11 1,2,5,11 1,2,5,11 V

6,7,8,9,10 1,2,3,4,5,6,8,11 6,8 III

6,7,8,9,10,11 2,3,4,6,7,11 6,7,11 III

5,6,7,8,11 1,2,5,6,11 5,6,11 II

6,7,9,10 2,3,8,9 9 III

6,10 2,3,4,5,8,10 10 I

1,2,5,6,8,11 1,2,5,8 1,2,5,8 II

The ISM technique presents a hierarchical structure that depicts the direct and indirect linkages between the various components in a system, based on primacy, precedence and causality over and among each other. There exists a group of design characteristics, which are critical to the

on. They have the highest driving force and the lowest dependence in the system. These are drivers. Another set of design characteristics has high dependence. These are the dependents. Between these two, there are the linkage and autonomous variab

characteristics are dependent on the drivers, but assist the existence and functioning of dependents (Sahney, 2008). Dependin

dependence power, all the variables are categorized as autonomous, dependent, linkage and independent variables (Borade and Bansod, 2011). In this study, the objective improved communication/IT adoption has driving power 9 and dependence power of 6 hence it is kept at a position

first cluster includes “autonomous variables” that have weak driver power and weak dependence. These variables are relatively disconnected from the system. The second cluster consists of the dependent variables that have a weak driver p

Third cluster has the linkage variables that have strong drive power and dependence. Any action on these variables will have

feedback effect on themselves. The fourth cluster includes independent variables with strong drive power and weak dependence (Jharkharia and Shankar,

The findings from the application of the ISM technique are as follows;

Improved communication, enhanced collaboration, enhanced information sharing, improved responsiveness, improved channel relat planning, reduced lead time, improved ordering/replenishment process, improved business performance

Using the final attrition of reachability matrices, a structural model is developed. The variables connected by arrows shows

This graph is called as an initial directed graph or initial diagraph. These diagraphs are principally hierarchical results obtained from tables indicating the final iteration of reachability matrices. By applying ISM methodology the diagraph is formed. If elements; of which reachability and intersection sets are similar than those are level I elements (Anantatmula and Kanungo, 2005). Once the level I is determined, it is removed and then next same

t level elements (Singh and Kant, 2008). Table III shows that, objective 10 is a level I objective. Therefore it is at the to

and 8 is a level II objective so it is below objective 10. Objectives; 9, 7 and 6 comes as a level III objectives hence it is kept below objectives 11 and 8. Objectives; 3 and 4 are level IV, objective 5 is level V and objectives 1 and 2 are level VI objectives respectively. All objectives are finally connected through arrows to show

FIG. 1: DRIVING POWER AND DEPENDENCE DIAGRAM

The analyzed contingent factors were limited to these variables because it was felt that they are most directly linked to CPF

the literature and as emerged from investigated cases (Danese, 2007). In order to have a common framework for adoption, an ISM model was developed. IT is observed that entire 11 objectives are important for CPFR adoption, although in varying degrees. By adopting ISM, we found th

industry. Fliedner (2003) research suggested that CPFR would become an indispensable instrument in any future supply chain. Our study shows information sharing and improved communication with IT adoption as the supreme objective of CPFR adopt

has a high dependence and low driving power. Literature reveals that the crux of CPFR is real time information sharing and co

Stank et al. (1999) evaluated CPFR attempts to lessen the problem associated with traditional anticipatory demand forecasts by co

2 8 6

Independent 1,5 Linkage

11 9

4,7

3

10

Autonomous Dependent

1 2 3 4 5 6 7 8 9 10 11

Level

linkages between the various components in a system, based on primacy, precedence and causality over and among each other. There exists a group of design characteristics, which are critical to the success of a system and require on. They have the highest driving force and the lowest dependence in the system. These are drivers. Another set of design characteristics has high dependence. These are the dependents. Between these two, there are the linkage and autonomous variables. Their design characteristics are dependent on the drivers, but assist the existence and functioning of dependents (Sahney, 2008). Depending upon driving power and dent variables (Borade and Bansod, 2011). In this study, the objective improved communication/IT adoption has driving power 9 and dependence power of 6 hence it is kept at a position which corresponds to a driving first cluster includes “autonomous variables” that have weak driver power and weak dependence. These variables are relatively disconnected from the system. The second cluster consists of the dependent variables that have a weak driver power but the strong dependence. Third cluster has the linkage variables that have strong drive power and dependence. Any action on these variables will have an effect on the others and a

rong drive power and weak dependence (Jharkharia and Shankar,

Improved communication, enhanced collaboration, enhanced information sharing, improved responsiveness, improved channel relations, improved production

Using the final attrition of reachability matrices, a structural model is developed. The variables connected by arrows shows the relationship between variables. btained from tables indicating the final diagraph is formed. If elements; of which reachability and intersection sets are similar than those are level I elements (Anantatmula and Kanungo, 2005). Once the level I is determined, it is removed and then next same process is reiterated to discover t level elements (Singh and Kant, 2008). Table III shows that, objective 10 is a level I objective. Therefore it is at the top. In the similar fashion, objective 11 a level III objectives hence it is kept below objectives 11 and 8. Objectives; finally connected through arrows to show

The analyzed contingent factors were limited to these variables because it was felt that they are most directly linked to CPFR implementation, as suggested by ramework for adoption, an ISM model was developed. IT is observed that entire 11 objectives are important for CPFR adoption, although in varying degrees. By adopting ISM, we found the level of variable in the hierarchy industry. Fliedner (2003) research suggested that CPFR would become an indispensable instrument in any future supply chain. Our study shows information sharing and improved communication with IT adoption as the supreme objective of CPFR adoption. While reduced inventory has a high dependence and low driving power. Literature reveals that the crux of CPFR is real time information sharing and collaborative inventory management. traditional anticipatory demand forecasts by co-operating with trading

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VOLUME NO.2(2012),ISSUE NO.12(DECEMBER) ISSN2231-5756

partners to better match supply and demand. Therefore, without any doubt, the basic premise of CPFR is improving the demand forecasting process. Hence, while adopting CPFR, management should have the objective of improving the forecasting accuracy, which would ultimately lead to improved business performance by improving order/replenishment process and reducing lead time. Kwon and Suh (2005) observed that effective supply chain planning based on shared information and trust between and among partners is an essential element for successful supply chain implementation. We had also similar observations. Lack of suitable information technology infrastructure and lack of mutual understanding between supply chain partners were found to be the major obstacles for the adoption. In the study, IT adoption derived higher driving power. It implies the importance of this objective in CPFR adoption. Albright (2002) demonstrated that CPFR implementation can reduce inventory levels, increase sales, and improve trade partnerships. Our study, observed highest dependence power for reduced inventory. Basically CPFR is a tool meant for managing inventory.It is observed that there are no autonomous variables in this study. Hence, all the variables focused in the study are important for adoption of CPFR. There is only one dependent variable. Reduced inventory have high dependence power and low driving power. Hence, organizations must understand the effect of this variable on others.

Improved communication, enhanced information sharing, improved production planning, enhanced collaboration, improved business performance, reduced lead time, improved responsiveness, improved ordering process, and improved channel relations. These variables have strong driving power and strong dependence power. Of all the 11 variables, these variables are very important because they strongly influence the adoption process.

Independent variables have very high driving power and low dependence power. Improved forecast accuracy was found to be independent variables in this study. For CPFR adoption, this is a strong driver; which influence all other variables. Hence, management must give high priority and should understand the importance of this variable.

FIG.2. THE DIGRAPH FOR ISM BASED FRAMEWORK

6. CONCLUSIONS

CPFR is a process, which is adopted by organizations for improving business performance. The process is in nascent stage in India. This paper attempted to establish the relationships among the objectives necessary for adopting CPFR by using Interpretive Structural Modeling. Finally, a hierarchical structure of the elements representative of their networks and relationships with their classification into various levels was developed. This will helped organizations to get aware of the importance of each objective for adopting CPFR process. From the study, it is concluded that all 11 objectives are important for CPFR adoption. It is found that major objective of CPFR adoption should be information sharing and IT adoption. It is further concluded that most important strategic driver for CPFR adoption is forecast accuracy. Finally, it is concluded that CPFR improves channel relations, production planning and replenishment process.

Academicians and industries could use results of this study for implementing CPFR in Indian industries. In this study, only 11 objectives were considered; however, many more variables could be used for developing a broader and generic framework. Interpretive Structural Modeling approach could be used for validating the framework.

7. REFERENCES

1. Albright, B., (2002). CPFR’s secret benefit. Frontline Solutions, 3 (11), 30-5.

2. Attaran, M., S., Attaran, (2007). Collaborative Supply Chain Management: The most promising practice for building efficient and sustainable supply chains. Business Process Management Journal, 13, 390-404.

3. Barratt, M., Oliveira, A., (2001). Exploring the experiences of collaborative planning initiatives. International Journal of Physical Distribution and Logistics Management, 31 (4), 266-289.

4. Borade, A.B., Bansod, S.V., (2010). Study of Vendor Managed Inventory Practices in Indian Industries. Journal of Manufacturing Technology Management, 21 (8), 1013-1038.

5. Chopra, S., P., Meindl, (2001). Supply Chain Management: Strategy, Planning and Operation. Prentice-Hall Inc., Upper Saddle River, New Jersey, USA. 6. Cooke, J.A., (1998). “VMI: very mixed impact?,” Logistics Management and Distrbution Report, 37 (12), 51-4.

7. Gene Fliedner, (2003). CPFR: an emerging supply chain tool. Industrial Management and Data Systems, 103 (1), 14-21.

8. Jharkharia, S., Shankar, R., (2005). IT-enablement of supply chains: Understanding the barriers. Journal Enterp. Inf. Manag., 18 (1), 11-27.

9. Kwon, I.G., T., Suh, (2005). Trust, commitment and relationships in supply chain management: A path analysis. Supply Chain Management an Int. J., 10, 26-33.

10. Pamela Danese, (2007). Designing CPFR collaborations: insights from seven case studies. International Journal of Operations and Production Management, 27 (2), 181-204.

Reduced inventory/ improved inventory management

Improved channel relations Improved responsiveness

Reduced lead time

Improved ordering/ replenishment process _{Improved business performance}

Improved forecast accuracy _{Improved production planning}

Enhanced collaboration

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11. Reza Sigari Tabrizi, Y.P., Foong, N., Ebrahimi, (2010). Using Interpretive Structural Modeling to determine the relationships among knowledge management criteria inside Malaysian organizations. World Academy of Science, Engineering and Technology, 72, 727-732.

12. Sahay, B.S., Gupta, N.D., Mohan, R., (2006). Managing supply chains for competitiveness: the Indian scenario. Supply Chain Management International Journal, 11 (1), 15-24.

13. Sahney, S., (2008). Critical success factors in online retail- An application of quality function deployment and interpretive structural modeling. International Journal of Business Information, 3 (1), 144-163.

14. Singh, M.D., R., Kant, (2008). Knowledge management barriers: An interpretive structural modeling approach. International Journal of Management Science and Engineering Management, 2 (3), 141-150.

15. Skjoett-Larsen, T., Thernoe, C., Andresen, C.,(2003). Supply chain collaboration- Theoretical perspectives and empirical evidence. International Journal of Physical Distribution and Logistics Management, 33 (6), 531-549.

16. Stank, T.P., Daugherty, P.J., Autry, C.W., (1999). Collaborative planning: supporting automatic replenishment programs. Supply Chain Management: An International Journal, 4 (2), 75-85.

17. VICS, (1998). Collaborative Planning Forecasting and Replenishment CPFR Guidelines Voluntary Inter Industry Commerce Standards. LawrenceVille NJ. Available at: www.cpfr.org

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