Cross-evaluation-based weighted linear optimization for multi-criteria ABC inventory classification

We suggest a new method for multi-criteria ABC inventory classification.Cross evaluation method is incorporated into the weighted linear optimization model.Self-evaluation and peer-evaluation in classifying inventory items are applied.Comparative simulation experiment was conducted with four typical DEA-like models.The proposed method shows better performance in the inventory management cost. Multi-criteria ABC inventory classification (MCIC), which aims to classify inventory items by considering more than one criterion, is one of the most widely employed techniques for inventory control. This paper suggests a cross-evaluation-based weighted linear optimization (CE-WLO) model for MCIC that incorporates a cross-efficiency evaluation method into a weighted linear optimization model for finer classification (or ranking) of inventory items. The present study demonstrated the inventory-management-cost effectiveness and advantages of the proposed model using a simulation technique to conduct a comparative experiment with the previous, related investigations. We established that the proposed model enables more accurate classification of inventory items and better inventory management cost effectiveness for MCIC, specifically by mitigating the adverse effect of flexibility in the choice of weights and yielding a unique ordering of inventory items.