Optimizing Supply Chain Efficiency Using Machine Learning with ARAS Methodology

This research examines how focusing on the evaluation of the efficacy of five machine learning models: Maxor Min, AdaBoost, Bagging, Random Forest and Decision Tree. The Adjoint Ratio Assessment (ARAS) method was applied to establish the models are analyzed based on six evaluation parameters: precision, accuracy, recall, log loss, MCC (Matthews’s correlation coefficient), and model complexity. Research Significance: The significance of this research is the systematic assessment of machine learning models intended for supply chain optimization. It provides insights into model selection, ensuring the adoption of algorithms that align with operational goals such as forecasting demand, optimizing inventory, and risk management. Methodology: The ARAS method is used to rank models based on their performance across evaluation parameters. This approach ensures a thorough evaluation of the advantages and disadvantages of each model, aiding supply chain practitioners in making informed decisions. semble learning that merges several ineffective classifiers to produce a robust predictive model. In the field of supply chain management, it is often utilized for activities like demand forecasti