Modeling and Optimization of JD Logistics Delivery Time Data in Spreadsheets
Introduction
In the era of e-commerce, logistics efficiency significantly impacts customer satisfaction. JD Logistics, as a key player in China's logistics sector, faces challenges in optimizing delivery times across diverse regions. This study explores how to collect regional delivery time data, model it in spreadsheets (e.g., Excel or Google Sheets), identify critical influencing factors (distance, weather, traffic), and propose data-driven optimization strategies for route planning and resource allocation.
Data Collection and Modeling
1. Data Sources
- Historical Delivery Records: Extract timestamps from JD's order system (dispatch→delivery).
- Geospatial Data
- External Factors: Weather conditions (rain/snow), traffic congestion indexes, and road types from third-party platforms.
2. Spreadsheet Structure
A sample dataset in spreadsheets may include:
| Region | Distance (km) | Avg. Delivery Time (hrs) | Weather Condition | Traffic Index |
|---|---|---|---|---|
| Beijing Urban | 15.2 | 2.1 | Clear | 6.7/10 |
| Sichuan Rural | 48.5 | 5.8 | Rainy | 3.2/10 |
Mathematical Modeling
Key Formulas in Spreadsheets:
- Multiple Regression:
=LINEST(delivery_time_range, [distance_range, weather_range, traffic_range])to quantify factor weights. - Scenario Analysis: Use
Data TablesGoal Seek- Clustering: Group regions via
k-means - Clustering: Group regions via
Optimization Strategies
1. Dynamic Routing
Using spreadsheet-based algorithms (e.g., Dijkstra’s shortest path modeled via helper columns) to reroute deliveries in real-time based on traffic updates.
2. Predictive Allocation
- Weather-based staffing: Increase couriers by 15% when rain probability exceeds 60% (per regression results).
- Time-window optimization: Analyze hourly delivery success rates to adjust promised delivery times.
Results and Validation
A pilot test in Jiangsu province showed an 11% reduction in average delivery time (from 3.2hrs to 2.85hrs) and a 7% rise in customer satisfaction scores after implementing spreadsheet-modeled routes.
Conclusion
Spreadsheets provide a flexible, low-cost platform for logistics optimization, especially when integrated with JD's existing data systems. Future work could automate this process using Python scripts while retaining spreadsheet visualization.