ERP & MRP Planning calculator
Forecast Accuracy Calculator
Forecast Accuracy helps planners see whether the forecast is reliable enough for MRP, staffing, and purchasing decisions. It is most useful when the numerator uses the same tolerance rule every review cycle.
What this calculator does
- Calculate forecast accuracy from demand that landed within tolerance versus total actual demand.
- a demand planner needs to quantify forecast quality for a product family
- It measures how much actual demand was covered by an acceptable forecast.
Formula used
- Forecast accuracy = demand within forecast tolerance ÷ total actual demand × 100
- Gap to target = target forecast accuracy - actual forecast accuracy
Inputs explained
- Demand within forecast tolerance: Count units, orders, or SKU-period demand where the forecast was inside the accepted tolerance band.
- Total actual demand: Use actual shipments, consumption, or booked demand for the same SKU set and time bucket.
- Target forecast accuracy: Use the S&OP, customer, or planning target for this demand family.
How to use the result
- Use it during ERP cleanup, MRP review, production scheduling, S&OP prep, purchasing decisions, shortage meetings, capacity planning, or daily shop-floor execution reviews.
- This is a planning estimate. Confirm final commitments against current ERP/MRP records, released BOMs and routings, inventory accuracy, supplier commitments, open work orders, quality holds, and shop-floor constraints.
Common questions
- What is the Forecast Accuracy calculator for? It measures how much actual demand was covered by an acceptable forecast.
- What information do I need before using it? You need in-tolerance demand, total actual demand, and the target forecast accuracy.
- How should I use the result? Use it to tune safety stock, freeze fences, planning parameters, and S&OP accountability.
- When is the result only an estimate? It is only an estimate when demand, inventory, lead time, routing hours, setup time, yield, supplier dates, or work-center capacity comes from forecast assumptions or stale ERP data instead of current orders and recent execution history.
Last reviewed 2026-05-12.