PREDICTING LATE DELIVERY RISK IN GLOBAL SUPPLY CHAINS USING BUSINESS ANALYTICS: EVIDENCE FROM TRANSACTIONAL LOGISTICS DATA
Abstract
Global supply chains become more complex, the demand for more information that more reliably ensures delivery and can help with better decision-making has grown. In this study, a machine learning based predictive system for DataCo Smart Supply Chain dataset is presented to detect the late delivery risk. Pre- and analyzed transactional logistics data comprises operational, customer, product, financial and shipping characteristics, which were preprocessed and analysed by various supervised machine learning algorithms including Logistic Regression, Decision Tree, Random Forest, XGBoost and LightGBM. The model was evaluated using the traditional classification metrics and feature importance analysis was conducted to gain insights into the variables which contribute the most to the delivery outcomes. The results highlight that machine learning models have the potential to predict the likelihood of late delivery accurately and offer practical insights into proactive logistics management. The proposed framework allows organizations to detect high-risk transactions, enhance transportation planning, optimize the use of resources and boost the supply chain resilience by timely managerial interventions. The study is a practical application on how to use real-world transactional data to employ predictive analytics for logistics risk management and adds to the body of work in business analytics. The results offer useful insights for managers who want to improve their operational efficiency, customer satisfaction and competitiveness in the highly dynamic and digitally connected supply chain future.
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