Enhancing Sustainable Supply Chain Forecasting Using Machine Learning for Sales Prediction

Abstract

Managing the supply chain is crucial to success in the competitive business sector. Demand forecasting using sales data is one of the major things in supply chain management because it is directly connected to profit margins, inventory levels, sales, and customer satisfaction. This research tried to provide an innovative approach to sales prediction using advanced machine learning methods to enhance supply chain operations and boost the predictive accuracy of supply chain models after analyzing historical sales data and considering different factors like seasonality, trends, and stock. Various machine learning algorithms were applied, including Linear Regression, Elastic Net Regression, KNN, Random Forest, and the ensemble Voting Regressor. The performance of Random Forest and KNN is very well but the Voting Regressor is better than other models for its strength of multiple algorithms. The Voting Regressor provides the lowest RMSE of 1.54 and the highest R² of 0.9999. This ensemble method improves sales forecasting accuracy by reducing errors and ensuring computational efficiency. It also provides more reliable tools to manage inventory, prevent overstocks, and minimize holding costs. This research presents the importance of machine learning integration in supply chain management. It shows the Voting Regressor as the most effective approach for demand forecast. Future research could explore the model’s application in broader markets, integrating other key factors and deep learning algorithms to refine predictive capabilities later.