Performance-based inventory classification methods for large-Scale multi-echelon replenishment systems

Abstract

Inventory classification is a managerial method utilized to group items that share predetermined characteristics, with the intent of assigning group-specific controls and monitoring mechanisms to each established item group. In this paper, we develop a performance-based inventory classification (PBIC) method that finds a grouping solution for a multi-item, multi-echelon inventory system controlled by continuous review. We argue that instead of grouping items based on similarities in unit cost, demand rate, or leadtime, the most effective strategy is to group items based on the information contained in their control policy values and their performance-related parameter values. We introduce a new policy-driven approach for establishing the classification criteria used to group items. We also adopt a ranking method to control the multi-dimensionality of multi-echelon systems in order to determine a one-dimension score. To group items, we improve the Pareto-based (ABC) solution by developing a search-based partitioning solution, utilizing a novel aggregation process. Our findings indicate that the PBIC method significantly outperforms alternative classification methods. Also, the empirical results show that there is a negligible gap between the performance of the PBIC and the optimal (complete enumeration) grouping solution. Finally, we discuss our work in the context of managerial implications highlighting the use of classification for problem aggregation and size reduction, when managers need to perform efficient, yet extensive, and dependable what-if analyses related to inventory management.