Machine Learning Enabled Inventory Control for Deteriorating Items with Carbon Emission and Trade Credit under Learning and Forgetting

Abstract

This research introduces a novel approach to inventory control, leveraging machine learning techniques to optimize inventory decisions for deteriorating items while accounting for carbon emissions and trade credit policies. In contemporary supply chain management, sustainability considerations have become increasingly vital. The carbon footprint associated with inventory management practices necessitates innovative solutions to minimize environmental impact. Moreover, trade credit offers financial flexibility but requires careful management to maintain profitability. To address these complex challenges, our proposed framework integrates machine learning algorithms into inventory control to enhance decision-making precision. The model incorporates dynamic learning and forgetting effects, allowing the system to adapt to changing demand patterns over time. This adaptability is particularly critical when dealing with deteriorating items that exhibit non-constant demand rates. Carbon emissions are assessed throughout the supply chain, and environmentally conscious decisions are made to minimize the carbon footprint. Additionally, trade credit terms are optimized to strike a balance between financial constraints and inventory performance. Our approach demonstrates superior performance in terms of minimizing costs, reducing carbon emissions, and enhancing supply chain resilience compared to traditional inventory management methods. Real-world case studies and simulations validate the effectiveness of our machine learning-enabled inventory control system, showcasing its practical applicability. This research contributes to the advancement of sustainable supply chain management by providing a comprehensive framework that combines AI-driven inventory control, carbon emission reduction, and trade credit optimization, ultimately fostering environmentally responsible and financially viable inventory decisions.