What Is a Good Forecast Accuracy by Industry?

What is a good forecast accuracy? The honest answer most consultants won't give you: it depends almost entirely on what you sell and how lumpy demand is, not on how smart your planners are. A good forecast accuracy for a high-volume food manufacturer might be 85% at the item-location level. The same number on a $40,000 capital-equipment SKU would be a miracle. I ran demand planning at a $250M industrial manufacturer, and the single biggest waste of executive energy I saw was leadership demanding "90% accuracy" across the board without knowing whether that was generous or insulting for a given product family.

Let me give you the numbers that actually matter, and the trap built into the question itself.

The number depends on three things, not your team's talent

Before you judge a forecast accuracy figure, you need three pieces of context. Without them, the percentage is noise.

• Aggregation level. Forecast accuracy at the total-company level is always higher than at the SKU-location level, because errors cancel out. A planner reporting "92% accurate" at the national monthly level might be running 60% at the DC-week level where replenishment decisions actually get made.
• Time bucket. Weekly is harder than monthly. Monthly is harder than quarterly. Same forecast, different scoreboard.
• Demand variability. The coefficient of variation (CV = standard deviation / mean demand) tells you how forecastable a product even is. A CV under 0.5 is smooth and forecastable. Above 1.0, you're often better off planning safety stock than chasing a tighter point forecast.

If someone quotes you an accuracy number and can't tell you the aggregation level, time bucket, and CV band, treat it as marketing.

Forecast accuracy benchmarks by industry

Here's a realistic range for SKU-level, monthly forecast accuracy (measured as 1 − MAPE, weighted by volume). These are operator numbers, not vendor brochure numbers.

Notice the spread. A 60% accuracy in industrial B2B can be a genuinely strong result. The same 60% in food would mean the planner is asleep.

Why MAPE lies to you

Most teams report MAPE (Mean Absolute Percentage Error) and call 100 − MAPE their "accuracy." It's the default in every ERP. It also breaks in the exact situations that hurt you most.

• MAPE blows up on low-volume SKUs. Forecast 2 units, sell 1, and your error is 100%. That one item can wreck a category average even though the dollar impact is trivial.
• MAPE is undefined when actuals are zero. Intermittent demand items get dropped or fudged.
• MAPE rewards over-forecasting. Because the error is capped at the actual on the downside but uncapped on the upside, bias creeps in unnoticed.

What I'd hold a team to instead:

• WMAPE (weighted MAPE) — weight by volume or revenue so the metric reflects what actually matters to the P&L.
• Bias (mean error) — separate from accuracy. You can be accurate and biased, or unbiased and inaccurate. Track both. Persistent positive bias is how stranded inventory gets built.
• MASE for intermittent items — compares your forecast against a naive baseline, which is the only fair test on spare parts.

What "good" means for a CFO, not a planner

Here's the reframe I wish I'd had earlier. Forecast accuracy is an input, not an outcome. The CFO doesn't care about MAPE. They care about three things downstream of it:

1. Stranded inventory — cash tied up in stuff that won't move at full margin.
2. Service level / fill rate — revenue you didn't lose to stockouts.
3. Expediting and obsolescence cost — the tax you pay for being wrong.

A 3-point accuracy improvement on a smooth, high-volume A-item can free seven figures in working capital. The same 3 points on a long-tail C-item is rounding error. Chase accuracy where it converts to cash. This is the discipline most planning teams never get taught.

A practical target-setting framework

Don't set one number. Segment, then set a floor per segment:

• A-items, smooth demand (CV < 0.5): target 85%+ and treat anything under 80% as a problem.
• A-items, variable demand (CV 0.5–1.0): target 70–80%, and invest in causal drivers (promo, price, weather).
• B/C-items, intermittent: stop chasing point accuracy. Manage with safety stock and service-level targets instead.

Then track forecast value added (FVA) — does your planner's adjustment actually beat a naive statistical baseline? In a third of the teams I've seen, manual overrides made the forecast worse. That's the most expensive thing in the room and nobody measures it.

The bottom line

A good forecast accuracy isn't a single magic number. It's a segmented set of floors, measured with the right metric, weighted by dollars, and judged by whether it frees working capital and protects service. If your team reports one company-wide accuracy figure and calls it a day, you don't have a forecasting problem yet — you have a measurement problem that's hiding one.

Want to know where your numbers actually sit versus your industry? Get a free planning-maturity and stranded-inventory teardown from PlanForge. We'll benchmark your accuracy by segment, find the cash trapped in over-forecasted SKUs, and show you the two or three moves that convert accuracy into working capital. Book a 30-minute call and we'll walk your numbers together.