A model for supply chain risk management in the automotive industry using fuzzy analytic hierarchy process and fuzzy TOPSIS

Abstract

Purpose

One of the most important issues in supply chain (SC) management is the identification and management of the risk involved in it. The purpose of this paper is to propose a comprehensive model of supply chain risk management (SCRM) in the product life cycle (PLC) and the operational process cycle (OPC). To decrease the risks in a fuzzy environment, the model considers the organizational performance factors (OPF) and the risk operational practices (ROP).

Design/methodology/approach

Fuzzy analytic hierarchy process is used to determine the weights of the relationships between the PLC, OPC and OPF in the hierarchical structure of the decision problem. In addition, the fuzzy technique for order preference by similarity to ideal solution is employed to recognize the priority of ROPs in dealing with the performance factors. The integrated framework is evaluated using the case study of an automotive company in Iran.

Findings

The results demonstrated that the proposed model can be used to formulate an appropriate method for prioritizing defined alternatives to decrease risk and improve the organizational performance in SCRM under fuzzy conditions.

Research limitations/implications

A major limitation of the study is that a few of the selected criteria for risk assessment are focused only on economic factors. Another limitation of the current study is related to the PLC, OPC and OPF being based on the work of Xia and Chen (2011).

Practical implications

The current study identified the more important stage in the PLC. More significant process in each stage of the PLC and weightier risk factors in each process of the OPC were determined. Some strategies for reducing risk in each stage of the PLC were presented. The best alternatives for reducing risks in SC were indicated.

Originality/value

It is worth mentioning that previous studies have not applied multiple criteria and alternatives to decrease the risks involved in the PLC and OPC parts of the SC under fuzzy conditions. However, it should be stated that some academics have used these techniques separately, in other specialized areas of the SC.

Citation

Salehi Heidari, S., Khanbabaei, M. and Sabzehparvar, M. (2018), "A model for supply chain risk management in the automotive industry using fuzzy analytic hierarchy process and fuzzy TOPSIS", Benchmarking: An International Journal, Vol. 25 No. 9, pp. 3831-3857. https://doi.org/10.1108/BIJ-11-2016-0167