KPIs · PMO

PMO: KPIs

Measures for a PMO judged on intervention rather than reporting, including forecast accuracy, warning lead time, and why on-time delivery alone is a misleading number.

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A reporting PMO is measured on whether the report went out. A predicting PMO is measured on whether anyone acted in time. These measure the second thing.

The six that matter#

MeasureDefinitionTargetHow it gets gamed
Warning lead timeDays between a risk being flagged and it materialisingAbove 10Flagging everything, so the warning carries no information
Forecast accuracyPredicted completion against actualWithin 10%Forecasting late and calling every early finish a win
Intervention rateShare of flagged risks that were acted onAbove 70%Counting "noted in the meeting" as an action
On-time deliveryAgainst the original baselineAbove 80%Re-baselining, which makes every project on time forever
Budget varianceActual against approvedWithin 10%Moving cost between projects at period end
Escaped commitmentsClient promises missed without prior warningZeroNot recording commitments made verbally

On-time delivery must be measured against the original baseline. Measured against the current plan it is meaningless: a project re-baselined four times is on time by construction. Track both if you like, but the original is the one that tells you whether the estimate was any good.

Warning lead time is the number that defines this function. A risk flagged the day it materialises is a report. The same risk flagged fifteen days out is a chance.

Two counter-metrics#

Counter-metricCatches
False-alarm rate on HIGH risksA prediction engine tuned to flag everything, which is indistinguishable from no engine once people stop reading it
Rework as a share of delivered workProjects hitting dates by shipping problems into the next phase

Portfolio-level view#

MeasureWhy
Projects with a single-person critical pathThe most common structural risk and the easiest to fix while it is early
Cross-project dependencies with no acknowledged ownerThe dependency that fails is almost always the one the other team did not know about
Utilisation above 90%Sustained, this is not efficiency; it is a system with no capacity to absorb anything

What is deliberately not measured#

  • Reports produced. The output of the old model.
  • Dashboard freshness. Automatic now, and it was never the constraint.
  • Meetings held. A cost, not a result.
  • Tasks closed. Trivially inflated by splitting work into smaller tasks.

Cadence#

DailyPrediction refresh; anything newly HIGH
WeeklyInterventions taken and their effect, commitments at risk
MonthlyForecast accuracy against what actually happened, false-alarm rate
QuarterlyWhether the thresholds are still right

The monthly forecast-accuracy check is the one that keeps the rest honest. A prediction engine nobody scores becomes a confident engine, and a confident wrong engine is worse than a spreadsheet, because people stop bringing their own judgement to it.

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