Prioritization of machines in production industry using fuzzy risk assessment

Abstract Nowadays, industry is developing very rapidly; thus, in the industrial environment, the need for constant risk assessment has become imperative. In the manufacturing sector, one common source of risk is machine and equipment failures, which inherently occur unpredictably. Such failures lead to production stoppages, negatively impacting both the affected organization and the entire supply chain. Therefore, risk assessment and efficient risk management have become essential components of manufacturing process management for companies aiming to stay competitive in a dynamically changing environment, often through the adoption of innovative technologies and solutions. To meet these demands, fuzzy logic has been applied to various traditional risk assessment methods. This paper reviews the literature on the application of fuzzy logic to risk assessment in manufacturing processes and examines the feasibility of using fuzzy logic to assess the risk of machine failure in production lines. In the discussed case, the process of cardboard packaging production was examined, along with an analysis of historical data related to machine failures. The proposed assessment method used a fuzzy inference system based on the Mamdani model, incorporating triangular membership functions. The study also explored the feasibility of applying fuzzy logic to conduct a Failure Mode and Effects Analysis (FMEA), a commonly used risk assessment method recommended in various industrial standards. Fuzzy logic is particularly recommended for expert systems, including FMEA, as it allows for processing input data presented as linguistic variables, providing a natural and intuitive way for humans to assess risk. The risk analysis is extended with an original method for evaluating the priority of resource allocation, supporting the decision-making process in the context of managing manufacturing resources.