Modeling the Measurement of Supplier Resilience Criteria via an Interpretive Expert-Driven Approach: A Step towards Promoting the Productivity of AutomotiveParts Manufacturing Industry

Subject Areas : Management (Operations Research)

Alireza  Arab ¹ (PhD student in Operations Research, Faculty of Management, University of Tehran,Iran)
Iman  Ghasemian Sahebi ² (Professor of Industrial Management, Faculty of Management, University of Tehran,Iran)
iman  ghasemian sahebi ³ (Ph.D. student of Production Management and Operations Management, Faculty of Management, University of Tehran,Iran)

Received: 2017-02-25 Accepted : 2018-03-15 Published : 2018-11-22

Keywords: fuzzy Delphi, Besunise countinuityinterpretive, Supplier resilience, Structural Modelling,

Abstract :

In the business world today, intense competition among the firms forces them to operate under conditions of uncertainty which, in turn, gives rise to numerous risks that can negatively impact the supply chain and lower profitability and competitive advantage. Expansion and density of communication network in the supply chain, on the one hand, and inevitability of some disorders, on the other, underscore the importance of measuring and evaluating suppliers. Supplier resilience defined as less influenceability from and the capacity to quell disorders, hence, requires more rigorous scrutiny. The aim of the current research was thus to identify and level suppliers’ resilience criteria at Avrand's Industrial Group supply chain using a combination of Fuzzy Delphi (FDELPHI) and Interpretative Structural Modeling techniques. The results of the FDELPHI method showed that 16 of the 27 criteria extracted from the literature review were verified by the team of 10 experts. Further, the Interpretive Structural Modeling was employed to offer a hierarchical model of the verified criteria. The results revealed a five-level hierarchy of resilience criteria with the delay time criterion at the highest level and the most influenceable factor and financial strength criterion as the most influential factor placed at the lowest level.

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