Data Analysis and Optimization Strategies for Delivery Operations in a Shenyang-Based Convenience Store Network

Rosedale Global High School, 7030 Woodbine Avenue #800 Markham, ON, Canada L3R 6G2

^* Corresponding author: yfe080915@163.com

Abstract

This study focuses on optimizing delivery operations in a Shenyang-based convenience store network by analyzing delivery time, cost, order distribution, and external factors such as weather and traffic. Using Python-based data analysis, inefficiencies in delivery times, rising costs due to fuel and labor, and uneven order distribution across routes were identified Visualizations are plotted to highlight the impact of longer routes on time and cost and evaluated external influences like traffic and weather. Solutions include route optimization, balancing order volumes, and adjusting delivery schedules to mitigate delays during peak hours or adverse weather conditions. Recommendations for improving delivery efficiency during holidays and special events, as well as contingency plans for external disruptions, are presented. The study suggests the use of machine learning for future order prediction and alternative delivery methods, such as drones or electric vehicles, for long-term cost reduction.