Hybrid Machine Learning and Time-Series Modelling to Forecast Product Demand: A CRISP-DM Approach

Efficient demand forecasting can yield significant competitive advantage for companies in today’s market, making it a crucial process in supply chain management. By exploring and implementing modern demand forecasting models, encompassing time-series, machine learning, deep learning and hybrid approaches, companies can gain benefits in important business areas such as strategic planning, operational efficiency and customer service. This research is done in collaboration with a case company, and it aims to identify and develop a product demand forecasting model that improves forecasting accuracy compared against a baseline approach and to identify and analyze key demand drivers that influence demand. To meet these goals, an industry standard CRISP-DM framework is utilized to develop and compare multiple time-series, machine learning and their hybrid combination models to find the best performing forecasting solution. Key demand drivers are identified and analyzed by combining SHAP values with statistical analysis. The results of this study indicate that a hybrid demand forecasting model combining Holt-Winters Exponential Smoothing and Elastic Net Regression outperformed the other models in the comparison and improved the forecasting accuracy significantly compared to the baseline approach. The results also suggest that product demand is driven by trend and seasonal factors, short-term sales pricing and momentum as well as macroeconomic leading indicators such as industry sentiment, interest rates and euro strength. Despite the significant improvement in forecasting accuracy and practical insights through the identified demand drivers, research limitations such as limited amount of training data, small test set and lack of true causal analysis of the relationships between features and demand are acknowledged to emphasize the need for future research. The development and evaluation of the HW-Elastic Net model contributes to current research by highlighting the importance of exploring hybrid models in demand forecasting, especially on small and noisy real-world datasets.