TY - GEN
T1 - Improving Delivery Time Predictions Using Real-World Traffic Information and Delivery Performance Data
T2 - 17th International Conference on Computer and Automation Engineering, ICCAE 2025
AU - Yagi, Marina
AU - Woo, Sejun
AU - Higuchi, Akiyo
AU - Shin, Kiyotaka
AU - Takahashi, Hiroshi
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
KW - Customer Satisfaction
KW - Data-Driven Decision-Making
KW - Economic Efficiency
KW - Simulation-Based Modeling
KW - Traffic Data
UR - https://www.scopus.com/pages/publications/105007285586
UR - https://www.scopus.com/pages/publications/105007285586#tab=citedBy
U2 - 10.1109/ICCAE64891.2025.10980569
DO - 10.1109/ICCAE64891.2025.10980569
M3 - Conference contribution
AN - SCOPUS:105007285586
T3 - 2025 17th International Conference on Computer and Automation Engineering, ICCAE 2025
SP - 345
EP - 349
BT - 2025 17th International Conference on Computer and Automation Engineering, ICCAE 2025
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 20 March 2025 through 22 March 2025
ER -