Using an Intuitionistic Fuzzy Map to Evaluate the Effect of Risk Variables in Supply Chain Management

Background: The escalating complexity within supply chains can be ascribed to a constellation of factors, notably globalization, competitive dynamics, burgeoning interconnectivity among supply chain partners, and the continuous evolution of technology. This phenomenon correlates with a heightened prevalence of uncertainties and the advent of novel risks, collectively exerting adverse effects on a diverse spectrum of enterprises. For this reason, recognizing, evaluating, and controlling these risks is crucial in the formation of a profitable, competitive, and long-term, sustainably managed supply chain. To control these risks, supply chain risk management has become an important field of study and application. Objective: This study's main goal is to create a model that aids in the analysis and prediction of supply chain hazards. objective: The primary objective of this study is to develop a model that helps to analyze and predict supply chain risks. Methods: To achieve this aim, the study starts with a comprehensive examination of the literature to discover relevant papers concerning the topic of interest. Variables were identified using the identified articles and the document coding standards. Using a fuzzy set technique, the found variables were then converted into linguistic variables and triangle membership functions. Results: Using the adjacency matrix that was acquired during the normalization procedure, an intuitionistic fuzzy cognitive map was produced. Additionally, scenario analysis was done to determine what steps to take. Conclusion: The final model offers a thorough framework for assessing, forecasting, and determining countermeasures for supply chain risks. This work advances the state of SCRM research and practice.