Optimization of supplier selection problem by combined customer trust and resilience engineering under uncertainty

Abstract

Trust between suppliers and producers would increase productivity and quality. Incorporation of resilience engineering has also proven to enhance efficiency. This study presents an integrated customer trust and resilience engineering algorithm for optimum supplier selection in a real auto parts manufacturer. The proposed algorithm is composed of standard questionnaires, fuzzy mathematical programming, statistical methods, and verification and validation mechanism. Resilience engineering (flexibility, adaptability and redundancy) and customer trust (integrity, benevolence, ability and predictability) are considered as outputs and cost and delivery time are considered as inputs to select best suppliers. Fuzzy mathematics is used to achieve improved results due to existence of data subjectivity and uncertainty. Moreover, fuzzy data envelopment analysis (fuzzy DEA) is designed and applied for various alpha cuts. The results show integration of customer trust and resilience engineering will increase total efficiency. The result identifies the most important factors in supplier selection problem with respect to trust and resilience engineering. The best supplier can also be selected. Predictability and redundancy have the highest impact on total efficiency, respectively. This is the first study that simultaneously considers resilience engineering, customer trust, cost and delivery time to select optimum supplier. Second, it uses a robust algorithm to achieve such objective. Third, it is equipped with verification and validation mechanism. Fourth, it is a practical approach for decision makers.