15 Demand Planning KPIs and Metrics That Matter
15 demand planning KPIs that matter for manufacturers — accuracy, bias, FVA, turns, fill rate — with formulas, targets, and what each one tells you.
Most demand planning KPIs dashboards I've inherited measure the wrong things, measure them the wrong way, or measure 40 things so nobody acts on any of them. The point of demand planning KPIs is not a pretty scorecard — it's to tell you where the plan is breaking and what it's costing in cash and service. At a $250M manufacturer I cut our metrics from 30-plus down to the 15 below. The discipline of measuring these well, every month, weighted by revenue, did more for our turns and fill rate than any new software did.
Here are the 15 that earn their place, grouped by what they actually tell you.
Accuracy and error metrics
These tell you how close the number was to reality.
1. Forecast accuracy. 1 − (forecast error / actual), expressed as a percentage. The headline number. Useful only if everyone agrees on the formula and the time bucket.
2. MAPE (Mean Absolute Percentage Error). avg( |actual − forecast| / actual ). The classic, but it blows up on low-volume SKUs (a 2-unit error on a 1-unit forecast is 100%) and ignores SKU value. Don't use it as your primary at the portfolio level.
3. WMAPE (Weighted MAPE). sum|actual − forecast| / sum(actual), weighted by volume or revenue. This is the one to lead with. A 50% miss on a $4 SKU should not register the same as a 5% miss on your flagship. WMAPE fixes that. Target: under 30% on A-items for most discrete manufacturers, under 20% if you're good.
4. MAE (Mean Absolute Error). Average absolute miss in units. Pairs with WMAPE when you need the raw magnitude, not a percentage.
Bias — the metric that quietly drains cash
5. Forecast bias. sum(forecast − actual) / sum(actual). Positive means you chronically over-forecast (building inventory you don't sell); negative means under (stockouts and lost sales). Bias is more dangerous than error because it's systematic — it compounds in the same direction every cycle. Random error averages out. Bias does not. Target: within ±5%. If you fix one metric first, fix this one.
6. Tracking signal. Running sum of bias divided by MAE. A signal that drifts past ±4 means your forecast is structurally off and needs intervention, not tweaking.
Value-add metrics — is the human helping?
7. Forecast Value Add (FVA). Compares your final forecast accuracy to a naive baseline (last period = next period, or seasonal naive). If your analysts and your sales overrides aren't beating the dumb forecast, they're destroying value. I've seen teams add three rounds of "intelligence" that made accuracy worse than copying last month. FVA exposes that. Run it on every override.
8. Override hit rate. Of the manual adjustments made in demand review, what share improved accuracy versus hurt it? This tells you whose judgment to trust and whose to discount.
Inventory and working-capital metrics
The plan exists to deploy cash and service customers. Measure both.
9. Inventory turns. COGS / average inventory. The cleanest read on whether the plan converts to working-capital efficiency. Most mid-market manufacturers run 4-8; pulling one extra turn on a $40M inventory frees real cash.
10. Days of Supply / Days Inventory Outstanding. Inventory on hand expressed in days of forward demand. The operational twin of turns. Watch it by SKU segment — your C/Z tail is usually where the dead stock hides.
11. Inventory accuracy. If your on-hand records are wrong, every downstream KPI is fiction. Cycle-count accuracy under 95% means your other metrics are noise.
12. Excess and obsolete (E&O) inventory. Dollars sitting above the demand that will ever consume it. This is your stranded cash. Track it as a percentage of total inventory and trend it — rising E&O is a chronic over-bias showing up on the balance sheet.
Service and downstream-impact metrics
13. Fill rate. Share of demand met from stock, on the first pass. The direct customer-facing payoff of good planning.
14. OTIF (On-Time In-Full). Did the customer get what they ordered, complete, on the promised date? The metric your biggest accounts actually score you on. A great forecast that doesn't move OTIF isn't translating to the customer.
15. Plan attainment. Did actuals land within tolerance of the committed plan (not the statistical forecast)? This separates a forecasting problem from a decision problem — if the forecast was good but attainment is bad, your S&OP meeting is breaking the number.
The dashboard, prioritized
| Metric | Formula | Target (mid-market) | What it tells you |
|---|---|---|---|
| WMAPE | sum|err| / sum(actual) | <30% A-items | Accuracy, value-weighted |
| Bias | sum(fcst−act) / sum(act) | ±5% | Systematic over/under |
| FVA | final acc − naive acc | >0 | Is human input helping |
| Inventory turns | COGS / avg inv | 6-8+ | Working-capital efficiency |
| E&O % | excess$ / total inv$ | falling | Stranded cash |
| Fill rate | met / demanded | 95-98% | Customer service |
| Plan attainment | act vs committed plan | within tolerance | Decision vs forecast problem |
How to actually use these
Three rules that separate scorecards from action:
- Weight by revenue, always. Unweighted accuracy flatters you and points you at the wrong SKUs.
- Segment by ABC/XYZ. A single portfolio number hides everything. Your A/X items should be tight; your C/Z tail will never be, and chasing it wastes effort.
- Pair an accuracy metric with a money metric. Accuracy alone is vanity. WMAPE next to E&O and turns shows whether better numbers are turning into freed cash.
The trap is measuring accuracy in a vacuum. I've seen teams celebrate a 5-point WMAPE improvement while E&O kept climbing — because they got better at forecasting the wrong, over-biased number. Accuracy plus bias plus cash, together, or you're flying blind.
See where your KPIs are lying to you
We'll take a quarter of your forecast, shipment, and inventory data and rebuild these 15 KPIs the right way — revenue-weighted, segmented, paired with the dollar impact. The free planning-maturity assessment and stranded-inventory teardown shows you exactly where your current metrics are masking trapped cash. Book a 30-minute call and we'll put real numbers on your scorecard.
Let's see what's worth building first.
A 15-minute call: tell me where your AI or planning is stuck, and I'll tell you the one thing worth building first — and whether it's worth doing at all.