Planned maintenance share

How much of our maintenance effort is planned rather than reactive?

Current value (maintenance case data): 65.5% - ! Watch. Open the case.

Field	Value
Definition	Labour hours on planned work orders divided by all maintenance labour hours.
Formula	sum(labor_hours where planned) / sum(labor_hours)
Unit	%
Granularity	Work order; reported monthly by crew.
Direction	Higher is better ↑
Target and thresholds	≥ 80.0% (amber ≥ 65.0%)
Parameters	–
Owner	Maintenance Manager
Source	Work-order generator (synthetic)
Input	table work_orders, columns planned, labor_hours
Recommended chart	bar - Stacked 100% bar per month (two parts only) or a line against target.
Related KPIs	Preventive maintenance compliance, Mean time between failures (MTBF)

How to read it

A maturity indicator: reactive work costs more and disrupts production. It moves slowly; read it quarterly.

Common misreadings

• Counting work orders instead of hours - many small planned inspections can hide a few huge breakdowns.
• Raising it by re-labelling emergency work as "planned" the same day; planned means scheduled ahead.

Worked example (from the test suite)

planned	labor_hours
True	4.0
True	2.0
False	6.0

Expected result: 50.00%. Planned hours 4 + 2 = 6 of 12 total = 0.5 (counting work orders would give 2/3).

This example is asserted in tests/test_formulas.py against the Python function and in tests/test_sql.py against the SQL below.

Implementations

Python function (the single implementation used by the site):

@kpi("planned_work_share")
def planned_work_share(df: pd.DataFrame) -> float:
    planned = df.loc[df["planned"].astype(bool), "labor_hours"].sum()
    return safe_ratio(planned, df["labor_hours"].sum())

Reference SQL (tested on SQLite):

SELECT CAST(SUM(CASE WHEN planned = 1 THEN labor_hours ELSE 0 END) AS REAL) / SUM(labor_hours)
FROM work_orders