Optimizing Supply Chain Management: The Impact of Machine Learning on Inventory and Logistics
Keywords:
Machine Learning (ML), Supply Chain Management (SCM), Demand Forecasting Inventory Optimization, Logistics Efficiency, Artificial Intelligence (AI)Abstract
Optimizing supply chain management through machine learning can significantly enhance inventory and logistics operations. This study presents a sophisticated machine learning framework designed to integrate demand forecasting with optimization algorithms to effectively manage inventory levels and logistics processes. The framework employs a Long Short-Term Memory (LSTM) model for demand forecasting, leveraging its capability to handle time-series data and capture complex patterns. By analyzing comprehensive historical sales data and various supply chain metrics, the model achieves a remarkable demand prediction accuracy of 92.5%. The study also incorporates optimization techniques, specifically Genetic Algorithms (GA) combined with Simulated Annealing (SA), to fine-tune inventory policies. This hybrid approach ensures that inventory holding costs are minimized while maintaining high service levels. The framework was implemented in a real-world retail supply chain, where it demonstrated substantial improvements. Notably, it reduced inventory holding costs by 18% and enhanced order fulfillment rates by 22%, reflecting its effectiveness in balancing cost efficiency with service quality. In addition to the technical aspects, the study addresses practical challenges such as data preprocessing, system integration, and continuous model retraining to adapt to changing market conditions. The results emphasize the profound influence of machine learning on enhancing the efficiency of supply chains, demonstrating its capacity to generate substantial cost savings and enhance service quality. This study focuses on the practical applications and advantages of advanced machine learning approaches in optimizing supply chain management, which will facilitate future advancements in this crucial field.
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Wisetsri, W., Donthu, S., Mehbodniya, A., Vyas, S., Quiñonez-Choquecota, J. and Neware, R., 2022. An investigation on the impact of digital revolution and machine learning in supply chain management. Materials Today: Proceedings, 56, pp.3207-3210.
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