Automatic demand forecast model selection in supply chains: a forecast value-added analysis of selection strategies, machine learning, and hyperparameter optimisation

Demand forecasting plays a critical role in supply chain management, enabling suppliers, manufacturers, and retailers to synchronise operations and enhance overall efficiency. Despite extensive research on time series forecast model selection, choosing the most appropriate forecasting model for a given time series remains a complex challenge, particularly in volatile and uncertain environments. The increasing availability of data and the emergence of new forecasting methods have introduced greater complexity, making automated model selection essential for improving forecasting accuracy and decision-making in supply chain operations. This study proposes an automated demand forecast model selection framework that integrates a broad range of statistical and machine learning models. A key feature of the framework is the optimisation of hyperparameters across all models, ensuring each method is fine-tuned for optimal performance. The approach is validated on the M3 monthly dataset, where it outperforms all previously submitted methods, demonstrating significant improvements in forecast accuracy. Additionally, the methodology is tested in a real-world supply chain setting, further showcasing its effectiveness in handling complex and dynamic demand patterns. By enhancing forecast accuracy and reducing the reliance on manual model selection, this research provides an efficient decision support system for supply chain demand forecasting in fast-changing supply chain environments.