Evaluating Machine Learning Techniques for Demand Forecasting in Supply Chains Using MOORA Method

This study introduces a novel approach that combines Combining Machine Learning methods with Multi-Criteria Decision-Making to help supply chain stakeholders identify the best model for delay prediction. Unlike conventional approaches that integrate MCDM and ML into a single system, this paper uses MCDM to evaluate different ML classifiers to improve decision-making. Additionally includes a sensitivity study to assess the method's resilience to other MCDM approaches, providing a thorough solution for accurate delay prediction in dynamic supply chain settings. This study is noteworthy for being the first to combine Supply chain support through machine learning and multi-criteria decision-making stakeholders in predicting delays. By evaluating different ML classifiers through MCDM and performing a sensitivity analysis, it provides a robust and interpretable decision-making framework that improves supply chain management efficiency. The bagging method achieved the highest rank, while the Decision Tree received the lowest rank. According to the MOORA approach, bagging holds the highest value for machine learning in supply chain applications.