Improving Delivery Time Predictions Using Real-World Traffic Information and Delivery Performance Data: Enhancing Customer Satisfaction and Enabling Data-Driven Decision-Making

This study quantitatively evaluates how improving delivery time prediction accuracy constructed using real-world traffic information impacts economic efficiency through computational simulation. It proposes a practical framework to optimize delivery operations and support data-driven decision-making. The results show that improving delivery time prediction accuracy led to a 2% increase in revenue and a 21% improvement in customer satisfaction, highlighting the economic and operational benefits of integrating real-world traffic information. This indicates that enhancing delivery time prediction accuracy can lead to economic efficiency gains and improved customer experience, enabling businesses to optimize delivery operations and improve competitive advantage. This research strengthens the foundation of data-driven management and provides a practical framework for establishing a competitive advantage.