Machine Learning for Demand Forecasting in Supply Chain Management: Challenges and Best Practices

Abstract

This paper explores the challenges, opportunities, and best practices associated with using machine learning for demand forecasting in supply chain management. The challenges encompass various aspects such as data quality, model complexity, interpretability, and scalability. Addressing these challenges requires careful consideration of data preprocessing, feature engineering, model selection, and validation techniques. Additionally, ethical considerations such as bias detection and fairness must be taken into account to ensure responsible and equitable forecasting practices. Despite these challenges, machine learning offers numerous opportunities for organizations to enhance their demand forecasting processes. These include the ability to leverage diverse data sources, capture nonlinear relationships, and adapt to changing market dynamics in real time. By embracing machine learning techniques, organizations can improve forecast accuracy, reduce forecasting errors, and optimize inventory levels to meet customer demand more effectively. To capitalize on these opportunities, organizations must adhere to best practices in machine learning for demand forecasting. This includes establishing clear objectives, selecting appropriate algorithms, validating models rigorously, and integrating forecasting insights into decision-making processes.