RizzitGo: How to Forecast Shipping Costs Using Past Spreadsheet Data
Shipping cost optimization represents one of the most significant opportunities for businesses to improve their bottom line. Many companies possess a treasure trove of historical shipping data in spreadsheets but lack a systematic approach to leverage it. This guide will walk you through the process of transforming your past spreadsheet data into accurate forecasts for future delivery expenses.
Step 1: Collect and Organize Your Historical Data
The foundation of any reliable forecast is comprehensive, clean data. Gather your shipment records, typically including:
- Parcel Details:
- Shipping Dates and Delivery Dates.
- Origin and Destination (ZIP codes, cities, states, or countries).
- Carrier and Service Level (e.g., Ground, 2-Day Air, Overnight).
- Final Shipping Cost (including base charges, fuel surcharges, and accessorial fees).
Consolidate this information into a single, structured spreadsheet. Ensure consistency in formatting—this is a critical first step that prevents errors down the line.
Step 2: Analyze Key Variables: Weight and Routes
Parcel Weight Analysis
Weight is often the primary driver of shipping costs. Start by performing a simple analysis:
- Calculate the average, median, and most frequent (modal) weight of your shipments.
- Identify "dimensional weight" shipments where the size of the package, rather than its actual weight, determined the cost.
- Segment your shipments into weight tiers (e.g., 0-1 lbs, 1-5 lbs, 5-20 lbs) and calculate the average cost for each tier.
Route Performance Analysis
Not all routes cost the same. Analyze your data by origin-destination pairs to identify patterns:
- Group shipments by common routes (e.g., "Warehouse A to Zone 5").
- For each route, calculate the average cost, transit time, and any common surcharges.
- Pinpoint high-cost routes. Are these due to distance, remote locations, or specific carrier inefficiencies?
Step 3: Identify Correlations and Cost Drivers
Move beyond simple averages to uncover the relationships between variables. Use your spreadsheet's built-in tools (like the This analysis helps you understand which factors are the most powerful predictors of your shipping expenses.CORREL
Step 4: Build Your Forecasting Model
Using your analysis, you can now create a model to predict future costs.
- For Simple Models:
- For More Advanced Models:
Example Forecast:
Step 5: Validate and Refine Your Forecast
A model is only as good as its accuracy. Test it:
- Withhold a portion of your historical data (e.g., the last 3 months) from the model-building process.
- Use your model to "forecast" the costs for this withheld data.
- Compare the forecasted costs to the actual costs. The difference is your model's error.
Regularly update your model with new shipping data to account for changing carrier rates and your evolving shipping patterns. This makes your forecasts progressively more accurate.
Conclusion: From Reactive to Proactive Cost Management
By systematically analyzing your historical spreadsheet data, you move from simply recording shipping expenses to actively predicting and managing them. This data-driven approach allows you to:
- Create more accurate budgets.
- Negotiate better rates with carriers using concrete evidence.
- Optimize packaging and warehouse placement to minimize costs.
- Make smarter, more profitable decisions about your fulfillment strategy.
Your past data holds the key to your future savings. Start unlocking it today.