Risk analysis of sequential processes in food industry integrating multi-stage fuzzy cognitive map and process failure mode and effects analysis

Abstract

Abstract Since managers and staff have not understood the actual consequences of risks in the food industry well, risk management methods practically are limited to identification of the type of risks. In addition, created changes in the business environment have led to change in the attitude of risk management to a process-oriented and systematic view. Because managers cannot decide based on the output of risk management process to implement improvement projects and allocate resources to them. This study has been tried to exactly identify and prioritize potential failures of the production process by using an approach based on the multi-stage Fuzzy Cognitive Map (FCM) method and Process Failure Mode and Effects Analysis (PFMEA) technique with the help of the cross-functional team. In this approach, failures are prioritized according to the amount of impact of each failure on other failures, as well as the amount of three factors as severity, occurrence, and detection (outputs of PFMEA). This approach considers process-oriented view in manufacturing system through internal-stage and external-stage relationships between production process failures and covers disadvantages of traditional Risk Priority Number (RPN) score such as disregarding internal relationships between failures. Hence, prioritization of potential failures based on the score which includes RPN determinant factors and causal relationships between failures is performed using the multi-stage FCM and learning algorithm based on extended Delta rule. The results of the proposed approach’s implementation in an active company in the food industry show that prioritization of failures is closer to reality and presents more full prioritization in comparison with approaches such as traditional RPN. The real case study in the food industry has been used to show the ability of the proposed approach.