How do you calculate service parts forecast accuracy?

Divide correctly forecast service part demand by actual service part demand and multiply by 100. With 1,120 parts forecast correctly against 1,280 actual, accuracy is 1120 / 1280 x 100 = 87.5%.

What is a good service parts forecast accuracy?

For intermittent spare parts, 80-90% at the line-item level is solid; fast-moving consumables can reach 95%+, while slow-movers may sit near 60-70%. The 87.5% in the example is healthy but still 0.5 points short of an 88% target.

What does the forecast accuracy gap tell me?

It is your accuracy minus your target. A negative gap means you missed the target; the example gap of 0.5 percentage points means you fell just short of 88% and likely only need minor model tuning, not a redesign.

Why is service parts forecasting harder than production forecasting?

Demand is intermittent and driven by failure rates, installed-base age, and field-service patterns rather than smooth production schedules, so a few large repair events can swing accuracy sharply period to period.

Forecast accuracy vs fill rate, what is the difference?

Forecast accuracy grades how well you predicted demand; fill rate grades how often you had the part on the shelf when demand arrived. You can have good accuracy and poor fill rate if safety stock is set too low.

How can I improve service parts forecast accuracy?

Segment SKUs by demand pattern, use methods like Croston's for intermittent items, incorporate installed-base and warranty data, and review the largest forecast misses each cycle rather than chasing the aggregate percentage.

Aftermarket, Field Service & Service Parts calculator

Service Parts Forecast Accuracy Calculator

Service parts forecast accuracy measures how closely your demand plan matched what the field actually consumed, expressed as a percentage of correctly forecast parts against real demand. Aftermarket planners and spare-parts inventory teams rely on this number because under-forecasting strands machines waiting on a part, while over-forecasting buries slow-moving SKUs in a regional warehouse. Service parts demand is lumpy, intermittent, and tied to installed-base age, so a 'good' accuracy here looks different from finished-goods planning. Tracking the accuracy and the gap to a stated target tells you whether your planning model, safety-stock policy, and fill-rate promises are defensible.

What this calculator does

Calculate forecast accuracy for service parts from correctly forecast demand, actual demand, and an accuracy target.
a parts inventory analyst needs to evaluate whether service-parts forecasts are reliable enough for stocking decisions
It divides correctly forecast service part demand by actual demand and multiplies by 100, then subtracts your target to show the gap in percentage points.

Formula used

Forecast accuracy = correctly forecast demand ÷ actual service part demand × 100
Forecast accuracy gap = forecast accuracy - target accuracy

Inputs explained

Correctly forecast service part demand:
Actual service part demand:
Target forecast accuracy:

How to use the result

Use it at each planning cycle close to grade last period's spare-parts forecast and decide whether to retune your model or safety stock.
It scores only the parts you forecast correctly against actuals and does not separate over-forecast from under-forecast bias, so a high score can still hide directional planning errors.

Common questions

How do you calculate service parts forecast accuracy? Divide correctly forecast service part demand by actual service part demand and multiply by 100. With 1,120 parts forecast correctly against 1,280 actual, accuracy is 1120 / 1280 x 100 = 87.5%.
What is a good service parts forecast accuracy? For intermittent spare parts, 80-90% at the line-item level is solid; fast-moving consumables can reach 95%+, while slow-movers may sit near 60-70%. The 87.5% in the example is healthy but still 0.5 points short of an 88% target.
What does the forecast accuracy gap tell me? It is your accuracy minus your target. A negative gap means you missed the target; the example gap of 0.5 percentage points means you fell just short of 88% and likely only need minor model tuning, not a redesign.
Why is service parts forecasting harder than production forecasting? Demand is intermittent and driven by failure rates, installed-base age, and field-service patterns rather than smooth production schedules, so a few large repair events can swing accuracy sharply period to period.
Forecast accuracy vs fill rate, what is the difference? Forecast accuracy grades how well you predicted demand; fill rate grades how often you had the part on the shelf when demand arrived. You can have good accuracy and poor fill rate if safety stock is set too low.

Last reviewed 2026-05-12.