Enhancing multi-criteria inventory classification: Resolving boundary issues with VIKOR-Fuzzy Sorting

Abstract

This study addresses the limitations of rigid classification boundaries in the VIKORSort method, a variant of the VIKOR multi criteria decision making approach, by incorporating fuzzy set theory to enhance classification flexibility and improve decision making effectiveness. The proposed method redefines the crisp boundaries of VIKORSort using fuzzy set theory with a trapezoidal fuzzy membership function, which gives the percentage of belonging to each class for each alternative. This enables smoother transitions between predefined classes and offering a more nuanced representation of alternatives’ characteristics. An empirical investigation evaluates the inventory performance of the fuzzy and non-fuzzy variants of VIKORSort, using data from a central warehouse managing 4799 spare parts in the textile industry. Inventory performance is analyzed under a reorder point inventory control policy and a target Cycle Service Level (CSL) constraint. Three CSL scenarios are considered to calculate the costs associated with the classification approaches. The results reveal that the proposed VIKOR-Fuzzy Sorting method outperforms the conventional approach in both cost efficiency and flexibility. In addition to providing a more precise depiction of alternatives near classification boundaries, the fuzzy method enables more effective assignment of target service levels in inventory management. This study thus offers practitioners a robust solution for addressing complex sorting challenges, particularly in inventory systems, by resolving boundary issues inherent in traditional approaches. Additionally, this approach enables businesses to develop more effective service level policies, enhancing overall operational performance.