Inventory Grouping and Sensitivity Analysis in Multi-Echelon Spare Part Provisioning Systems

Existing research in the area of multi-echelon spare part models have not adequately addressed how to take advantage of the useful concept of inventory segmentation. To fill this gap, we define a new formulation of the multi-echelon repairable parts stocking model for the purpose of finding the best inventory grouping solution. For solving this mixed-integer and nonlinear formulation, we develop a heuristic optimization model based on a greedy approach, which uses the idea that the items having more similar stocking policies should tend to group together. Our findings show that the proposed model provides a near optimal solution, which significantly outperforms the alternative classification and clustering methods in the field. In addition, we highlight the managerial implication of segmentation for problem size reduction, when managers need to perform an extensive sensitivity analysis under time limitations.