Machine Learning for Proactive Supply Chain Risk Management: Predicting Delays and Enhancing Operational Efficiency

Abstract

Abstract Supply chain (SC) efficacy and efficiency can be severely hampered by supplier delays in orders, especially in the fast-paced business environment of today. Effective risk reduction necessitates the identification of suppliers who are prone to delays and the precise prediction of future interruption. Accurately predicting availability dates is therefore a key factor in successfully executing logistics operations. By leveraging machine learning (ML) techniques, organizations can proactively identify high-risk suppliers, anticipate delays, and implement proactive measures to minimize their impact on manufacturing processes and overall SC performance. This study explores and utilizes various regression and classification ML algorithms to predict future delayed delivery, determine the status of order deliveries, and classify suppliers according to their delivery performance. The employed models include K-Nearest Neighbors (KNN) Random Forest (RF) Classifier and Regression, Gradient Boosting (GB) Regression and Classifier, Linear Regression (LR), Decision Trees(DT) Classifier and Regression, Logistic Regression and Support Vector Machine (SVM) Based on real data, our experiments and evaluation metrics including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) demonstrate that the ensemble based regression algorithms (RF Regression and GB Regression) provide the best generalization error and outperforms all other regression models tested. Similarly, Logistic regression and GB Classifier outperforms other classification algorithms according to precision, recall, and F1-score metrics. The knowledge obtained from this study could aid in the proactive identification of high-risk suppliers and the application of proactive actions to increase resilience in the face of unanticipated disruptions, in addition to increasing SC efficiency and decreasing manufacturing disturbances.