FORECASTING THE FUTURE: A COMPARATIVE ANALYSIS OF ML AND DL MODELS IN SUPPLY CHAIN DEMAND PREDICTION

Abstract

Supply chain demand forecasting is a strategic process aimed at predicting future customer demand for products within the broader framework of a supply chain. This involves forecasting the anticipated quantity of goods or services that customers will purchase and seamlessly integrating this insight into the overall supply chain management. The primary goal is to synchronize production, procurement, and distribution activities with expected demand, thereby optimizing inventory costs, minimizing instances of under stocking or overstocking, reducing waste, and ultimately enhancing overall supply chain efficiency. The emphasis is on leveraging advanced technologies, including deep learning techniques such as CNN, LSTM, CNN+LSTM, GRU, and machine learning techniques like Linear Regression and XGBoost, to achieve accurate predictions. By implementing these algorithms, businesses can construct a robust forecasting system capable of monitoring changes in demand and aligning supply accordingly. This proactive approach empowers retailers to enhance their inventory and planning efficiency, ultimately contributing to increased customer satisfaction. KEY WORDS: Machine Learning, Deep Learning, XG Boost, Linear Regression, CNN, LSTM, CNN+LSTM, GRU