Free Excel download · Last updated October 2026
A forecast accuracy template compares what you forecast with what you actually sold, combination by combination, and turns the gaps into one accuracy figure you can track every month. This workbook measures it at the level you choose (item, item-location, item-channel or item-location-channel), shows bias next to accuracy, lists the combinations that cause most of the error, and separates error caused by stock shortages from error caused by the forecast itself.
What's inside the workbook
| Sheet | What it does |
|---|---|
| Start Here | The six steps, what to paste in each column, and the question each report sheet answers. |
| Settings | The accuracy level, the target and amber band, the error contributor threshold, ABC cut-offs, the bias and FVA windows, and how month-to-date is phased. |
| Data_Forecast, Data_Actuals, Data_Stock | The three input sheets. Forecast by month, item, location and channel with the product, location and channel hierarchy; actual sales by date; and, optionally, stock available by month, item and location. |
| Dashboard | KPI tiles for accuracy, bias, accuracy without supply issues and month-to-date accuracy, key insights written out in plain sentences, trend charts, an ABC summary and a Product L1 summary. |
| Accuracy_Detail | Accuracy, bias and error share for every combination in one filterable table, with flags, FVA and supply-adjusted numbers. |
| Error_Contributors | The combinations whose error is at least the threshold share of total error (2% by default), each with a likely driver. |
| ABC_Analysis | Accuracy of A, B and C combinations, how many hit the target, and how each class is trending. |
| Bias_Watchlist | Combinations that are consistently over- or under-forecast, with a suggested adjustment for the next forecast. |
| FVA_Analysis | Whether the statistical forecast beats a naive forecast, whether overrides improve it, and where overrides hurt. |
| MTD_Tracker | How the current month is tracking against the phased forecast and where it is likely to land at month end. |
| Monthly_Trend and Hierarchy_Summary | Accuracy and bias month by month, overall and by hierarchy, and every KPI rolled up to each level-1 hierarchy. |
| Supply_Impact | What accuracy would have been without stock shortages, and which combinations were supply-constrained. |
| Guidelines | Definitions, formulas, how to prepare the data, how to use the report in the monthly cycle, and troubleshooting. |
Preview
| Aug 2026 · by Product L1 | Final forecast | Actual | FA % | Bias % | FA ex-supply % | MTD FA % |
|---|---|---|---|---|---|---|
| Oral Care | 28,749 | 28,406 | 87.8% | 1.2% | 89.9% | 91.7% |
| Hair Care | 18,876 | 15,007 | 71.5% | 25.8% | 84.5% | 70.3% |
| Skin Care | 10,693 | 10,222 | 83.0% | 4.6% | 85.2% | 84.4% |
| Total | 58,318 | 53,635 | 82.3% | 8.7% | 87.4% | 83.7% |
How to use the forecast accuracy template
- Unzip the download and enable macros. The report is built by macros, which need Excel for Windows. If Excel shows a yellow bar, click Enable Content. If it shows a red "blocked" bar, close the file, right-click it, choose Properties, tick Unblock and reopen.
- Choose the level on Settings: Item, Item-Location, Item-Channel or Item-Location-Channel, plus the accuracy target and thresholds. Leave the report month and the month-to-date date blank and the file picks them from your data.
- Paste the forecast into Data_Forecast from row 4, one row per month, item, location and channel. Use the forecast as it was frozen at your reporting lag. Add the statistical forecast in column K to switch on forecast value add.
- Paste actual sales into Data_Actuals with the same codes. Use real transaction dates for the current month so month-to-date accuracy works; completed months can be one row per month.
- Optionally paste stock into Data_Stock (opening stock plus receipts by month, item and location) to see accuracy without supply issues.
- Click Refresh Report and read the Dashboard. Start with the KPIs and key insights, then work the Error_Contributors list from the top.
How the template measures accuracy
Errors are calculated per combination at the level you chose and then added up, so an over-forecast in one combination can't cancel an under-forecast in another. That makes accuracy equal to 100% minus WAPE. A positive bias means over-forecasting; a negative bias means under-forecasting. In Settings you can divide by the forecast in place of actual sales.
Accuracy without supply issues
When stock runs out, sales are capped by supply, so part of the forecast error is really a supply failure. Where the forecast was above actual sales and the stock available was below the forecast, the workbook adds the shortfall (forecast minus the larger of stock and actual) back to actual sales before scoring. Example: forecast 100, stock 70, actual 60. The shortfall is 30, the adjusted actual is 90, and the forecast is only blamed for the 10 units that were in stock but didn't sell. Stock is held by item and location, so this works at Item and Item-Location level only.
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 (frozen at the lead-time lag, not the latest revision), 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 file downloads immediately.
Why is it a macro-enabled workbook in a zip file?
The report is rebuilt from your data by macros when you click Refresh Report, so the file is an .xlsm. Unzip it, open it in Excel for Windows and click Enable Content. The macros don't run in Excel for Mac, Excel for the web or Google Sheets.
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.
What is forecast value add (FVA)?
It shows whether each step of the process improves the forecast. The workbook compares a naive forecast (last month's actual), the statistical forecast and the final forecast. If overrides make the final forecast less accurate than the statistical one, that step needs a different approach.
What does 'accuracy without supply issues' mean?
It is the accuracy you would have had if stock had been sufficient. Items that sold less because stock ran out are adjusted before scoring, so planners aren't pushed to cut forecasts that were actually right.
Can I measure accuracy at a lag?
Yes. Paste the forecast as it was frozen the relevant number of months before (for example, three months earlier for a three-month lead time). The formulas don't change.