Enhancing Supply Chain Forecasting with Machine Learning: A Data-Driven Approach to Demand Prediction, Risk Management, and Demand-Supply Optimization

This paper discusses how ML can be leveraged to enhance supply chain forecasting through demand prediction, risk mitigation and demand-supply match optimization. Even deterministic and time-series supply chain approaches dont have an edge over volatile and challenging data environments, making them imprecise and inflexible. Through the use of ML models, such as recurrent neural networks (RNNs), support vector machines (SVMs), and reinforcement learning (RL) agents, this study shows the accuracy in demand prediction, risk detection, and supply-demand match. The primary findings include: the RNN decreases the mean squared error by 15% over traditional approaches and the RL agent minimizes inventory turnover and lead times to enhance supply chain efficiencies. These results highlight the potential of ML to react rapidly to real-time shifts and drive better decisions. The report provides a comprehensive approach to data-driven predictive models, and useful advice for companies looking to improve supply chain resilience and profitability.