Auto parts supply chain risk assessment and rating models using fuzzy cognitive map and Interpretive Structural Modeling

One of the major challenges in the automotive industry is facing different risks, especially when new products are offered due to meeting the needs of customers, which leads to a lack of accurate identification in changing methods and design, new machinery and materials, demand, production speed ,and so on. These can cause serious injuries and risks. To recognizing these risks, you need to look for the right ways to identify risks and prioritize them to exercise control over critical risks. Therefore, in this paper, after identifying the main areas of identified risks, production line risks were graded and based on that, the fuzzy cognitive maps approach was developed and 13 risks were identified in three groups of technical, strategic and operational risks were analyzed. Then, using interpretive structural modeling approach, the correlation of risks was evaluated and the most important risks were identified using the network analysis process. Finally, the results show that the risks of design errors, low motivation, lack of financial resources, lack of parts and low productivity are among the five main risks in the Isaco auto parts supply chain.