What it measures
Accuracy of physical inventory vs system records
Lagging as it confirms past stock placement errors, but strongly leading as an accuracy below 98% directly predicts future picking errors, FEFO breaches, and short-shipments within the next 24-48 hour dispatch cycle.
Business impact
- Increased 'chaos tax' from unplanned cycle counts, emergency stock movements, and customer service time spent investigating and explaining short shipments.
- Corrupted inventory position data leads to flawed replenishment orders, generating pockets of excess stock on some SKUs while creating stock-outs on others, bloating working capital and damaging service simultaneously.
Formula
(Accurate locations / Total locations) x 100Grain
One row per bin location count event — material × plant × warehouse × storage location × date
Inside the Implementation Pack
A preview of what's actually built for this KPI — extracted from the real Implementation Pack, not a mockup.
This is the primary ratio the pack's other measures build on — trend, rank, and period-comparison variants, all on Fact_Inventory_Cycle_Count.
Fact_Inventory_Cycle_Count— 8 mandatory columns — warehouse key, storage location key, ...Dim_Employee— key + nameDim_Material— material_key + material_descriptionDim_Plant— key + nameDim_Storage_Location— key + nameDim_Warehouse— key + nameDim_Date— Auto-generated calendar table — no customer data required
Full field-by-field data contract, in 4 SQL dialects (SQL Server, Snowflake, Databricks, BigQuery), ships in the pack.
| Target | 0.99 — performance considered compliant at or above this line |
| Warning | 0.96 — amber alert — triggers a review |
Both live in one Excel tab — change them, refresh the report, done. No DAX editing needed.
Everything you need to build Inventory Record Accuracy (IRA) in Power BI — dimensional model, DAX measures, SQL views, and a food-industry demo dataset.