Analyze delivery durations across regions to forecast parcel arrival windows accurately.
Predicting delivery times is crucial for managing customer expectations and optimizing logistics. At CNFANS Shipping, we leverage structured spreadsheet analysis to transform raw shipping data into reliable forecasts. By analyzing historical delivery durations across regions, you can move from guesswork to data-driven precision.
Step 1: Building Your Master Dataset
Consistent data collection is the foundation. Your spreadsheet should include the following columns for every shipment:
| Column Header | Description |
|---|---|
| Shipment ID | Unique tracking number. |
| Origin & Destination Postal Codes | For calculating regional pairs. |
| Dispatch Date | Date the carrier received the parcel. |
| Actual Delivery Date | Date of final customer receipt. |
| Carrier & Service Level | e.g., Standard, Express. |
Pro Tip:
Step 2: Calculating Core Metrics
Add calculated columns to derive key performance indicators (KPIs):
- Transit Duration:=DATEDIF(Dispatch_Date, Delivery_Date, "d")
- Regional Grouping:
Your spreadsheet will now provide the raw material for analysis.
Step 3: Regional Analysis & Forecasting
Use PivotTables or functions like AVERAGEIFSSTDEV.P
Forecast Range = (Average Duration) ± (Standard Deviation)
For example, if the average is 7 days with a 2-day deviation, the forecast window is 5 to 9 days.
Step 4: Visualizing for Insight
Create charts to communicate findings clearly:
- Bar Charts:
- Line Graphs:
- Scatter Plots:
- Line Graphs:
Visuals help spot patterns that raw numbers might miss.
The CNFANS Advantage
While spreadsheets are powerful for manual analysis, CNFANS Shipping integrates these principles directly into our platform. We automatically analyze millions of data points across our network, providing real-time, dynamically updated forecast windows for every shipping lane, saving you time and increasing accuracy.