A Developed Model for Measuring Supply Chain Nervousness, Using Fuzzy-DEMATEL to Analyze the Correlation Between Measurement Factors

Nervousness results from variance and changes in the verdicts of supply and logistics networks and activities. Nervousness is considered a source of confusion in supply chain (SC) systems because it is associated with frequent decision changes. New SC techniques are necessary to handle the growing supply chain nervousness (SCN) from globalization. Although they can be challenging to create, SCN metrics are crucial for assessing and optimizing the operations of a SC. The evaluation of SCN and future improvements in SC performance depend on correctly identifying SCN metrics. In this study, a method for measuring SCN was proposed, and a model was developed. The SCN measurement model seeks to quantify SCN for inclusion in the SC structure to support decision making. To assist organizations in determining the effect of nervousness on SCs and enhancing their general performance and competitiveness, this study quantified SCN, defined SCN metrics, and modeled and assessed SCN indicators. The model includes key SCN measurements, simulating, and evaluation, which can enhance future SC performance and resilience by enabling more precise SCN quantification. The importance of the designated SCN metrics was then determined using a fuzzy decision-making trial and evaluation-laboratory method (FDEMATEL). This method was used to evaluate and resolve complicated, interrelated scenarios, as it can demonstrate how metrics are interdependent and form a map that illustrates their relative relationships. The findings distinguish between cause and effect measurements as well as their interactions. Additionally, the results show the importance of the rankings of the SCN measurements. These outcomes can be used to establish a solid foundation for developing effective decision-making tools for SCN.