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With the rapid development of e-commerce, efficient logistics has become a crucial factor in enhancing customer satisfaction. This study focuses on JD Logistics, collecting delivery time data from various regions to build data models in spreadsheets. By analyzing key influencing factors such as distance, weather conditions, and traffic status, we explore optimization solutions to improve both delivery efficiency and service quality.
A structured dataset is created in spreadsheets, containing:
Key spreadsheet models developed:
| Model Type | Formula Implementation | Purpose |
|---|---|---|
| Linear Regression | =LINEST(delivery_time_range, factors_matrix) | Quantify factor weights |
| Time-series Forecast | =FORECAST.ETS(future_date, history_range, dates) | Predict seasonal patterns |
| Scenario Analysis | Data tables with =CHOOSE() functions | Test optimization scenarios |
Primary influencing factors (weight ranking):
Implement =IFS() functions in spreadsheets to create real-time routing logic based on live weather/traffic data feeds, prioritizing roads with weight scores >0.8.
Using =FORECAST.LINEAR(), pre-allocate warehouse resources for anticipated 72-hour demand spikes with 89% accuracy in test datasets.
Cell-based calculations (=MEDIAN()+2*STDEV) determine optimal time buffers per delivery zone, reducing late deliveries by 22% in simulations.
The spreadsheet-based modeling framework demonstrates significant improvements in JD Logistics' operations:
Future research will integrate the spreadsheet models with real-time API data connections and machine learning enhancements.
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