A Predictive Analytics of Lead Time Variability in Asia-Pacific Supply Chains Using Machine Learning

Abstract

Lead time reliability is an important performance metric in today's supply chain and logistics system. Differences between requested and actual delivery lead times can cause operational inefficiencies, inventory shortages, and increased logistics costs. This study uses shipment-level logistics data from a private sector dataset to analyze lead time variances in Asia-Pacific supply chains. The study's primary purpose is to analyze the difference between requested and actual lead times across various production sites and product categories. To forecast lead time variances and identify key elements influencing logistics performance, a data-driven methodology combining exploratory analytics and machine learning prediction models is proposed. The study evaluates prediction accuracy using regression-based and ensemble learning models, with performance measures including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination. The study demonstrates how machine learning algorithms may improve supply chain decision-making and logistics planning. The proposed methodology provides logistics managers with proactive risk mitigation and operational optimization predictions.