Forecast Exception Review Template
A forecast exception review focuses the demand planner on the few items that are off, instead of re-reading the whole plan. This template captures the rule, the flagged SKUs, the root cause and the owner, so the follow-up is written down rather than remembered.
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Forecast Exception Review Template
Forecast Exception Review
Company: Acme Precision Ltd Planning cycle: October 2026 Demand planner: R. Mehta Review date: 2026-10-20
Exception rule
Tolerance: Absolute forecast error above 25% on A and B items Measured at: Lag 2 (two months)
Summary
14 SKUs breached tolerance. 9 are linked to two customers who pulled orders forward in September. 3 are new items with no history. 2 are data issues.
Exception log
| SKU | Family | Forecast | Actual | Error % | Root cause | Owner | Due |
|---|---|---|---|---|---|---|---|
Root cause codes
PROMO: promotion or campaign not in forecast PULL: customer pulled orders forward or pushed out NEW: new item, no history DATA: master data, UoM or history error MODEL: statistical model unsuitable EXT: external event such as a shutdown or weather
Agreed follow-up
Sales to confirm the pull-forward volume by Friday. Planner to load a manual adjustment for the new items. Master data to fix two unit of measure errors.
Sign-off
Keep the exception rule stable across cycles so trends are comparable. Review only items that matter to revenue or supply risk, otherwise the log becomes noise.
Automate the exception list
Want this exception list produced from your ERP data each cycle? Ask for an optional expert review.
Optional. The tool and every export work without this. No spam.
How to use the template
Set an exception rule first, for example error above 25% on A and B items, then pull the flagged SKUs from your planning system or Excel. Enter the rule, the lag and the summary in the form, download the document and fill the table by hand or paste it into Excel. Circulate it before the demand review so sales and operations see the same list. Keep the rule fixed for at least a quarter so exception counts can be compared across cycles.
Choosing a tolerance
Tolerance should reflect how costly an error is, not a single global number. Fast moving A items with stable demand deserve a tight band. Lumpy C items will breach any tight band all the time and swamp the list. Many teams combine a percentage band with a minimum unit or value threshold, so a 40% miss on 5 units is ignored while a 15% miss on a large order book is reviewed.
- Use a tighter band for A items
- Add a minimum volume or value floor
- Measure at the lag your supply lead time needs
- Review the rule each quarter
Root cause discipline
The value of the review is in the root cause codes. A short, fixed code list lets you count causes over time and see that, for example, most misses come from promotions that never reached the plan. That points to a process fix, not a better model. Avoid free text only, it cannot be summed. Use one primary code per exception and add detail in the notes column.
Common mistakes
Teams often review every item, which hides the real problems. Others measure error against the latest forecast instead of the frozen forecast that supply used, which flatters accuracy. A third mistake is assigning actions without owners or dates. Keep the log short, measure at the correct lag and make every action traceable to a person.
Frequently Asked Questions
What is a forecast exception?
An exception is an item whose forecast error, bias or volume change breaches a rule you defined, such as absolute error above 25% on A items. Exception based planning means the planner reviews only those items instead of the full forecast.
Which lag should I measure exceptions at?
Measure at the lag that matches your supply lead time. If production is frozen two months out, the two month lag forecast is the one supply acted on, so error against that version is the one that matters.
How many exceptions is too many?
If a planner cannot review the list in the time available, the rule is too loose. Tighten by adding a volume floor or limiting the review to A and B items rather than raising the tolerance for everything.
Can I keep this log in Excel?
Yes. Download the document, paste the table into a spreadsheet and keep one tab per cycle. Counting root cause codes across tabs shows which process failures repeat.
If your exception list is built by hand every cycle, Netray can generate it from your ERP and planning data and draft the root cause suggestions for a planner to confirm.
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