What it measures
% of scheduled PMs completed on time
it measures adherence to the maintenance plan before equipment failures occur. A sustained dip below 90% predicts a rise in unplanned downtime and yield loss within the next 30-60 days.
Business impact
- Increased 'chaos tax' from reactive maintenance: emergency call-outs for contractors, premium freight for non-stocked spares, and production schedule disruption bleeding into subsequent shifts.
- Erosion of effective line capacity as unplanned breakdowns consume more time than scheduled PMs, forcing the business to burn through its finished goods inventory position to protect service.
- Sustained non-compliance on food safety assets (CCPs) is a major finding in any BRC or IFS audit, triggering immediate corrective action demands and placing major customer contracts under formal review.
Formula
(PMs completed on time / Total PMs scheduled) x 100Grain
One row per maintenance order operation — equipment × plant × work centre × 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_Maintenance_Order. It guards against missing dates and values with NOT ISBLANK checks.
Fact_Maintenance_Order— 9 mandatory columns — maintenance order id, equipment key, ...Dim_Employee— key + nameDim_Equipment— key + nameDim_Plant— key + nameDim_Work_Center— 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 | 0.98 — performance considered compliant at or above this line |
| Warning | 0.92 — 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 Preventive Maintenance Compliance in Power BI — dimensional model, DAX measures, SQL views, and a food-industry demo dataset.