A Novel Improved Grey Incidence Model for Evaluating the Performance of Supply Chain Resilience

Abstract

Under uncertain conditions, stronger supply chain resilience can effectively reduce disruption risks and help enterprises achieve their goal of high-quality operations. This paper constructs a resilience evaluation index system for manufacturing enterprises from the perspective of the supply chain and uses the improved TOPSIS method to quantify the level of resilience. Taking into account that the resilience index is easily affected by nonconventional factors in the real environment, the WAWBO weakening buffer operator and the metabolism idea are introduced to improve the grey prediction method, so as to realize the dynamic prediction of the resilience index. In addition, a supply chain resilience early warning model is constructed by combining it with the quantification method of resilience. Using the data of a Chinese electronics manufacturing enterprise as a case study, the results demonstrate the effectiveness of the proposed resilience quantification method, and the improved grey prediction method has higher prediction accuracy. The study provides a new idea for relevant enterprises to improve the early warning ability of their supply chain, thus promoting the sustainable development of the supply chain.