Glossary and Where to Start
Terms used across these notes, defined plainly, and routes through the collection for the common situations.
Counterweight — a measure that falls when the main measure is inflated dishonestly. The central protection against gaming.
The practical lesson in “Glossary and Where to Start” is to connect every number to a decision and retain the context behind it. Teams exploring fte meaning can review additional details as one source of operational evidence, provided the purpose is disclosed and the interpretation is tested with the people affected.
Cycle time — from work starting to finishing. What the team experiences.
For an independent perspective related to “Glossary and Where to Start”, consult the Harvard Business Review productivity collection; it offers a useful external check on definitions, governance and the assumptions built into a proposed measure.
Flow efficiency — working time as a share of elapsed time. Usually a small fraction, and the gap is waiting.
Lead time — from request to delivery. What the customer experiences, and the number that matters.
Output, outcome, activity — what was produced, what it achieved, and what people did. Three different things, routinely conflated.
Proxy — something measurable standing in for something that is not. All proxies decay; assume a year.
Rework — work done again. Effort spent with no output, and usually the largest avoidable loss.
The constraint — the step that sets the pace of a process. Improving anything else changes nothing.
Terms used loosely elsewhere
"Productivity" in most reporting means activity, not output per unit of input.
"Efficiency" is doing the thing with less; effectiveness is doing the right thing. Productivity measures address only the first.
"Utilisation" sounds like a good thing to maximise and is not, outside capital-intensive work.
And any industry productivity benchmark carries definitions you cannot see.
Where to start
Somebody asked for productivity figures: what you are really being asked, then deciding what not to measure.
Choosing a measure: choosing a measure teams will accept, then quality as the counterweight.
The figures look wrong: the denominator nobody checks, then averages and the tail.
Professional work: the unit problem, then measuring work that has no unit.
Output is disappointing: most productivity problems are system problems, then finding the constraint.
Nobody trusts the numbers: measurement and trust.
If you read only three
The ratio, and why both halves are wrong, because it bounds what any figure can support.
Most productivity problems are system problems, because it is where the improvement actually is.
And measurement and trust, because it determines whether any of the rest works.
A closing note
No products or vendors are named here, deliberately.
No figures for average productivity improvement, because the studies producing them are commissioned by people selling something.
And no argument that measurement is bad. The argument is that bad measurement is worse than none, and that most of what goes wrong is avoidable with four or five habits.
What the collection argues
Productivity is a ratio, and both halves are usually measured badly. Counting the easy unit and dividing by hours present produces a precise-looking number built from two approximations.
Any measure that becomes a target stops describing the work. This is not a caution but a prediction, and the only reliable protection is a counterweight that falls when the main measure is inflated.
Most professional work has no comparable unit, and the honest response is to measure flow, rework and waiting rather than to invent one.
And most productivity problems are not about people. They are queues, handoffs, approvals, old equipment and fragmented weeks — all of which a per-person measure hides and a per-item measure reveals.