An Empirical Analysis of Delivery Delays in Supply Chain Management Using Business Intelligence Techniques

Delivery reliability remains a critical determinant of competitiveness in modern supply chains. Despite technological advancements, many organizations continue to experience delays caused by operational and logistics-related inefficiencies. This study investigates the key operational and transportation factors influencing delivery delays using business intelligence and data-driven analytical techniques applied to secondary supply chain data. The research integrates regression modeling, clustering analysis, and mode-based performance evaluation to examine how lead time components and transportation modes jointly affect delivery reliability. The findings demonstrate that delivery delays emerge from systemic interactions among processing, shipment, and coordination phases rather than from isolated bottlenecks. Furthermore, distinct delivery- performance profiles highlight the importance of risk- based order segmentation and mode-specific logistics strategies. The study contributes to the supply chain literature by proposing an integrated analytical framework that combines business intelligence tools with operational modeling to enhance delivery performance management. The results provide actionable insights for managers seeking to improve reliability, optimize lead-time structures, and implement data-driven decision support systems in global supply chain environments.