Article2013

Modelling the SCM enablers: an integrated ISM‐fuzzy MICMAC approach

112 citations0/0 files storedAsia Pacific Journal of Marketing and Logistics

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Quality and Supply Management Multi-Criteria Decision Making Collaboration in agile enterprises

Abstract

Purpose The purpose of this paper is to identify the supply chain management enablers (SCMEs) and establish relationships among them using interpretive structural modeling (ISM) and find out driving and dependence power of enablers, using fuzzy MICMAC (Matriced' Impacts Croisés Multiplication Appliquée á un Classement) analysis. Design/methodology/approach A group of experts from industries and academics was consulted and ISM is used to develop the contextual relationship among various SCMEs for each dimension of SCM implementation. The results of ISM are used as an input to fuzzy MICMAC analysis, to identify the driving and dependence power of SCMEs. Findings This paper has identified 24 key SCMEs and developed an integrated model using ISM and the fuzzy MICMAC approach, which is helpful to identify and classify the important SCMEs and reveal the direct and indirect effects of each SCME on the SCM implementation. The integrated approach is developed, since the ISM model provides only binary relationship among SCMEs, while fuzzy MICMAC analysis provides precise analysis related to driving and dependence power of SCMEs. Research limitations/implications The weightage for ISM model development and fuzzy MICMAC are obtained through the judgment of academicians and a few industry experts. It is only subjective judgment and any biasing by the person who is judging the SCMEs might influence the final result. A questionnaire survey can be conducted to catch the insight on these SCMEs from more organizations. Practical implications This study has strong practical implications, for both practitioners as well as academicians. The practitioners need to concentrate on identified SCMEs more cautiously during SCM implementation in their organizations and the top management could formulate strategy for implementing these enablers obtained through ISM and fuzzy MICMAC analysis. Originality/value This is first kind of study to identify 24 SCMEs and further, to deploy ISM and fuzzy MICMAC to identify and classify the key SCMEs that influence SCM implementation in the organization.

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References

1994Vendor Selection Using Interpretive Structural Modelling (ISM)2005Development and validation of a measurement instrument for studying supply chain management practices 2007Performance measurement of supply chain management: A balanced scorecard approach 2008Benefits, barriers, and bridges to effective supply chain management 2006Supply chain risk mitigation: modeling the enablers 2009The impact of “soft” and “hard” TQM elements on quality management results 2000An instrument for measuring TQM implementation for Chinese manufacturing companies 2003Adopting new technologies for supply chain management

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