Sustainable Operations Management through Data-Driven Inventory Classification and Material Requirement Planning

Abstract

The increasing complexity of supply chains and the growing emphasis on sustainability have necessitated the development of more adaptive and data-driven inventory management approaches. The traditional inventory management systems fail to address the demand uncertainty, cost-effectiveness and resource utilization at the same time. The current paper will propose a hybrid concept of sustainable operations management that will be grounded in the integration of the ABC-XYZ classification of the inventories and optimization principle of Material Requirement Planning (MRP). The differentiation of inventory items occurred by applying economic value and demand variability with the set of 3,204 stock-keeping units, and thus, it was possible to apply various control strategies. These results reveal that the goods of high value and that has steady demand are concentrated in the major part and must be monitored first and planned properly. Demand variability is an inclusion in classification and it enhances the decision making by taking into consideration the uncertainty in the replenishment process. Besides, the optimization of safety stock and reorder point improves the inventory balance by reducing excess stock and stock out stock. Inventory turnover affects the business performance positively as observed in the analysis whereas holding cost affects the business performance negatively and the inventory should be used efficiently. The study is relevant to the existing literature as it brings to one platform classification, forecasting sensitivity, and optimization of planning. The findings provide practical insights for developing resilient and sustainable inventory systems in dynamic supply chain environments.