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.