Classification methods for problem size reduction in spare part provisioning

Spare part provisioning for asset-intensive companies is a complicated problem due to the large number of items, low demand rates, and multi-echelon environment. A primary strategy for reducing the size and managing a large number of spare parts is using grouping techniques and data aggregation. In this paper, we address the question of how to reduce the size and complexity of large-scale, two-echelon, service part provisioning systems to benefit both inventory service levels and managerial processes while considering performance trade-offs. This paper contributes a performance-based inventory classification approach for a two-echelon inventory model by developing a novel ranking method. First, it defines the concept of the artificial stocking policy as a new classification criterion in the literature. Then, it adopts a non-subjective weighted linear scoring method for ranking items in the entire network. Finally, it presents a heuristic partitioning method, which is evaluated and compared with complete enumeration and eight alternative clustering and classification methods. The proposed model is implemented and tested in the context of the classic repairable spare part inventory model, called the VARI-METRIC. The results indicate that the proposed method is easy to apply and significantly outperforms the alternatives.