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 forecasting and anomaly detection. the one of AdaBoost ability to focus on misclassified events during training is particularly useful in noisy environments. However, its sensitivity to outliers and high computational demands for large datasets can limit its scalability in some applications. bines their outcomes to. Its ability to handle complex datasets makes Random Forest a reliable choice in a variety of situations. Decision Tree: Decision tree models are simple but powerful tools To make decisions. Their application in supply chain is extensive scenarios such as supplier selection, inventory categorization, and shipment prioritization. Packing: Bootstrap aggregation, or packing, improves model consistency and accuracy by training multiple versions of the same model on different data subsets and combining their predictions. In supply chain applications, packing is particularly useful for reducing variance and improving forecast reliability. Its ability to use multiple data sources ensures robust performance in Jobs like demand forecasting and stock control. ication in supply chain is extensiveIn supply chain situations, accuracy is important for tasks such as demand forecasting and product classification. While high accuracy indicates strong general performance, it does not fully reflect a model’s ability to handle unbalanced datasets. Precision: Accuracy evaluates the trustworthiness of affirmative forecasts, In supply chain applications such as fraud detection or quality control, high accuracy ensures that false positives are minimized, saving time and resources in decision making. Recall (or sensitivity) evaluates how effectively a model can recognize true positives from all true positives. This parameter essential for detecting rare but critical events in supply chain operations such as stockouts or supplier disruptions.Log Loss: Log Lossestimates the uncertainty in probabilistic predictions by imposing a higher penalty for incorrect predictions. In supply chain optimization, it is most useful for models that output probabilities, such as those predicting shipment delays or demand spikes. Lower log loss values indicate higher confidence in the predictions. Matthews Correlation Coefficient (MCC): The MCC offers a measure of parity model performance, one is especially valuable in asymmetric datasets, such as those found in supply chain risk management, where accurate classification across all classes is important. Model Complexity: Model complexity reflects the complexity of the machine learning model, including the number of parameters, features, and computational resources required. In supply chain management, this is essential balance model complexity with performance.. Result: Random Forest emerges as the best performer due to its balance across metrics, followed by Decision Tree and Packing, which offer strengths in recall and consistency. Maxor Min shows competitive performance, while AdaBoost stands out in performance but requires further improvement for broader applications.