A forecast accuracy template compares what you forecast with what you actually sold, item by item, and turns the gaps into one accuracy figure you can track every month. This workbook does it in volume and value, for three forecast versions at once, and separates error caused by running out of stock from error caused by the forecast itself.
What's inside the workbook
| Sheet | What it does |
|---|---|
| Forecast Vs Actual Dashboard | The monthly summary: accuracy, demand accuracy and supply error for the statistical, demand planner and consensus forecasts, overall and by region, in volume and value, with a chart. |
| SKU Level Accuracy | The main input: one row per SKU and region with opening stock, actual sales and the three forecasts. It calculates the absolute deviation (MAD) of each forecast and decides whether each miss was a demand or a supply problem. |
| Consensus Forecast | The forward consensus forecast for the next six months by SKU, with the three-month average and months on hand (opening stock ÷ average forecast). |
| Observation | A remarks log for the review meeting: region, SKU, demand or supply, and what happened (a lost tender, a stock transfer, a volume shift between two SKUs). |
| Master and Read Me | The review month, region filters, ABC class lists, accuracy bands and the definitions used in the workbook. |
Preview
| Feb 2022 · volume | Statistical | Demand planner | Consensus |
|---|---|---|---|
| Accuracy | 60% | 59% | 57% |
| Demand accuracy | 68% | 66% | 66% |
| Supply error | 7.6% | 7.8% | 8.1% |
| Region-1 accuracy | 41% | 42% | 37% |
| Region-2 accuracy | 84% | 84% | 78% |
The dashboard with the workbook's sample data. Colours follow the template's bands: green at 75% or more, amber at 50–75%, red below 50%. In this example the consensus forecast is less accurate than the statistical one, a common finding worth discussing in the review.
How to use the forecast accuracy template
- Set the review month on the Master sheet and choose the region filter.
- Paste one row per SKU and region into SKU Level Accuracy: opening stock at the start of the month, actual sales, and the statistical, demand planner and consensus forecasts made for that month.
- Read the dashboard. Start with overall accuracy, then compare the three versions. Each step of review should make the forecast better; if a step makes it worse, look at who changed what.
- Check the supply error. If a large part of the error is marked Supply, the forecast wasn't the problem: stock was. Fixing replenishment will do more than re-forecasting.
- Record the reasons for the biggest misses on the Observation sheet, and roll the consensus forecast forward for the next six months.
How the template measures accuracy
Accuracy is weighted, so it equals 100% minus WAPE. The workbook calculates it in units and in value (units × average price), because a forecast can look good in units and poor in value when the misses sit on expensive items.
How a miss is labelled demand or supply
If actual sales were higher than the consensus forecast, the miss is a demand error. If sales were lower, the workbook checks stock: when opening stock was 5% of the forecast or less, or sales used up 90% or more of the opening stock, the shortfall is put down to supply. Otherwise it is a demand error. You can change these thresholds in the formula in column I.
What is a good forecast accuracy?
There is no single benchmark: accuracy depends on the level you measure at, the lag, and how volatile your products are. Measured at SKU level, monthly, one month ahead, stable fast-moving products often reach 70–85%, while intermittent and new products can sit well below 50%. Compare yourself with your own history and track the trend, not an industry average.
Two habits matter more than the number itself: measure the forecast you actually used to make decisions (usually the consensus, at the lead-time lag), and look at forecast bias next to accuracy, because a forecast can be reasonably accurate on average and still be consistently too high.
Planamind measures FA% (100 − WAPE), WAPE, MAPE, bias and MAD every cycle, and WMAPE by forecast horizon. Its forecast value added (FVA) report checks each override against the statistical baseline, with the error before and after, and rule-based recommendations rank the items where accuracy can improve most, validated on a six-month holdout. See how demand planning works in Planamind.
Frequently asked questions
Is the forecast accuracy template free?
Yes. Fill in the short form and the Excel file downloads immediately.
What is the difference between MAPE and WAPE?
MAPE averages the percentage error of each item, so a large percentage miss on a tiny item counts as much as one on your best seller. WAPE divides total absolute error by total actual sales, which weights each item by its size. This template uses the weighted approach.
Why compare statistical, demand planner and consensus forecasts?
It shows whether each step of the process adds value. If the planner's changes or the consensus meeting make the forecast less accurate than the statistical baseline, that step needs a different approach. This is called forecast value added (FVA).
What does 'supply error' mean?
It is the part of the forecast error on items that sold less because stock ran out, not because demand was lower. Counting it as forecast error would push planners to cut forecasts that were actually right.
Can I measure accuracy at a lag?
Yes. Enter the forecast that was made the relevant number of months before (for example, three months earlier for a three-month lead time) in the forecast columns. The formulas don't change.