Optimizing Inventory Carrying Cost Using Rank Order Clustering Approach for Small and Medium Enterprises (SMES)

For any company, whether big enterprises or small and medium-sized enterprises (SMEs), inventory is one of the key assets. Therefore, inventory-related decisions directly influence the revenue generated by the firm. This work aims to find a sufficient degree of control over each inventory item and to mitigate the inventory management problems of SMEs. Rank Order Clustering (ROC) algorithm is used in this study for multi-item inventory item aggregation. The proposed framework is tested on a medium-sized gearmanufacturing firm that manufactures 40 different types of planetary and customized gear-boxes. The results demonstrate 47.64 % of cost-saving through the proposed methodology of cluster formation using ROC and quantity discounts. This approach helps to identify different assemblies to aggregate the component requirements and to formulate a particular inventory strategy to minimize inventory carrying costs for each component.