A Constrained Clustering Algorithm for Spare Parts Segmentation

This paper explores the application of constrained clustering algorithms to inventory grouping based problems. Our research in this area is based upon a segmentation methodology that considers operationally relevant part attributes. In this paper, we present a brief review of literature on multi-echelon inventory models, and clustering applications to inventory segmentation. Finally, we discuss our segmentation methodology from the perspective of our experiments on a single-indentured, single-echelon, multi-item inventory system. While bringing to focus many important trade-offs in the inventory segmentation process, we also recommend future work to achieve optimal part groups and extend this segmentation methodology to multi-indentured, multi-echelon inventory systems.