ENHANCED FUZZY LOGIC-BASED APPROACH TO INVENTORY MANAGEMENT CONSIDERING SPARE PART LIFETIME AND LEAD TIME UNCERTAINTY

Abstract

Effective management of spare parts inventory is essential for maintaining uninterrupted industrial operations. Traditional systems often struggle to adapt to the dynamic uncertainties of machine health and lead times, which can result in inadequate inventory levels and increased downtime. This research introduces a novel integration of fuzzy logic control to improve inventory management by addressing these uncertainties. The study focuses on spare part management in a wood substitute manufacturing case involving the refiner process, utilizing a min-max inventory strategy in conjunction with a fuzzy inference system that evaluates machine health and lead times. Various fuzzy numbers, including triangular, trapezoidal, pentagonal, and hexagonal, are employed to model these uncertainties, leading to reduced holding and stock costs compared to conventional approaches. The results show that the total cost under the fuzzy inventory control policy is approximately three times lower and substantially less than that of the non-fuzzy policy, as confirmed by 95% confidence intervals that are clearly distinct. However, the analysis reveals no significant difference among the fuzzy numbers, indicating flexibility in selection based on specific application needs. Overall, this work underscores the potential of fuzzy logic control to optimize spare part inventory management in industrial contexts.