An integrated interpretive structural modeling and a graph-theoretic approach for measuring the supply chain complexity in the Indian automotive industry

E.G. Kavilal · Shanmugam Prasanna Venkatesan · J Soni Sanket

41 citations0/0 files storedJournal of Manufacturing Technology Management

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Quality and Supply Management Product Development and Customization Supply Chain Resilience and Risk Management

Abstract

Purpose Easily employable quantitative supply chain complexity (SCC) measures considering the significant dimensions of complexity as well as the drivers that represent those dimensions are limited in the literature. The purpose of this paper is to propose an integrated interpretive structural modeling (ISM) and a graph-theoretic approach to quantify SCC by a single numerical index considering the interdependence and the inheritance of the SCC drivers. Design/methodology/approach In total, 18 SCC drivers identified from the literature are clustered according to the significant dimensions of complexity. The interdependencies established through ISM and inheritance values of SCC drivers are mapped into a Variable Permanent Matrix (VPM). The permanent function of this VPM is then computed and the resulting single numerical index is the measure of SCC. Findings A scale is proposed by computing the minimum and maximum threshold values of SCC with the help of expert opinions of the Indian automotive industry. The complexity of commercial and passenger vehicle sectors within the automotive industry is measured and compared using the proposed scale. From the results, it is identified that the number of suppliers, increase in spare-parts due to shortened product life-cycle and demand uncertainties increase the SCC of the passenger vehicle sector, while number of parts, products and processes, variety of products and process and unreliability of suppliers increase the complexity of the commercial vehicle sector. The result indicates that various SCC drivers have a different impact on determining the SCC level of these two sectors. Originality/value The authors propose an integrated method that can be readily applied to measure and quantify SCC considering the significant dimensions of complexity as well as the interdependence and the inheritance of the SCC drivers that contribute to those dimensions. This index further helps to compare the complexity of the supply chain which varies between industries.

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References

2003Towards a Methodology for Developing Evidence‐Informed Management Knowledge by Means of Systematic Review 2001Supply networks and complex adaptive systems: control versus emergence 2005The supply base and its complexity: Implications for transaction costs, risks, responsiveness, and innovation 2008The impact of supply chain complexity on manufacturing plant performance 2010Structural investigation of supply networks: A social network analysis approach 2015Structural drivers of upstream supply chain complexity and the frequency of supply chain disruptions 2002Unveiling the structure of supply networks: case studies in Honda, Acura, and DaimlerChrysler 2005Supply-chain networks: a complex adaptive systems perspective

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