Inventory management in supply chain networks involves keeping track of hundreds of items spread across multiple locations with complex interrelationships between them. However, it is not computationally feasible to consider each item individually during the decision making process. The use of clusters of items is preferred for the evaluation of these decisions. In addition, the use of groups of items provides management with more effective methods for characterizing and controlling system performance and results in cost savings such as group discounts. In this research, we introduce a comprehensive clustering methodology for supporting inventory management in supply chain networks. All product characteristics which have a significant impact on the performance of the supply chain are taken into account. The nodes in the network are split into subnodes prior to clustering to reduce the complexity. The average linkage clustering algorithm and the Calinski and Harabasz index are used to identify clusters of similar items. In addition, a set of heuristics is used to capture the relationships between items as specified in the bill of materials for the products. Examples are presented to demonstrate the effectiveness of the clustering methodology as well as the performance of the heuristics, by comparing the results obtained with the optimal solution.
ASJC Scopus subject areas
- Computer Science(all)