David KrauseFew metrics are as widespread in manufacturing — and as often misunderstood — as OEE. It appears on shop-floor boards, in monthly reports and in target agreements. Yet two plants both reporting "85 % OEE" can be economically worlds apart, and a plant can raise its OEE while profit falls. This article places the three key productivity metrics in context and shows how to connect them to cost accounting.
1 · OEE — overall equipment effectiveness
OEE (Overall Equipment Effectiveness) describes the actual use of a machine relative to its planned operating time. It is the product of three factors, each capturing one type of loss:
= 90 % × 90 % × 96.3 % = 78.0 %
| Factor | Captures | Value | Loss |
|---|---|---|---|
| Availability downtime losses | Breakdowns, setup, tool changes, material shortages | 90.0 % | 435 h |
| Performance speed losses | Reduced speed, minor stops, idling | 90.0 % | 392 h |
| Quality quality losses | Scrap, rework, startup parts | 96.3 % | 130 h |
| OEE | Planned time 4,350 h → productive | 78.0 % | 957 h |
No time may be deducted from planned operating time in advance — not breaks, not waiting for material, not scheduled maintenance. Those deductions are precisely the losses OEE is meant to reveal. Whoever nets them out beforehand creates a metric that always looks good and steers nothing. An OEE figure is only comparable when its reference base is documented.
2 · TEEP — what OEE systematically hides
OEE only evaluates the time you planned to run. It can be excellent while the machine sits idle half the year — because the unused time never enters the denominator. TEEP (Total Effective Equipment Productivity) closes that gap by building on calendar time:
TEEP = 49.7 % × 78.0 % = 38.7 %
Same machine, two metrics: 78.0 % OEE — a solid figure that rates the operational flow well. And 38.7 % TEEP — the share of the year the investment actually produced good parts. Both are correct. They answer different questions:
| Metric | Answers | Audience |
|---|---|---|
| OEE | Does the machine run well when it is supposed to run? | Supervisor, maintenance, CIP |
| TEEP | Does utilization justify this investment? | Plant management, controlling, capex planning |
Shift operation is the strongest lever on the machine hour rate — stronger than any OEE improvement. If the same machine ran three shifts (planned time 6,000 h instead of 4,350 h), TEEP would rise from 38.7 % to 53.4 % at unchanged OEE, and the MHR would fall from €46.37 to about €34/MH. OEE would stay exactly the same. Whoever steers OEE alone never sees this lever.
3 · From percentage to euro amount
Here lies the gap that stays open in many metric systems: losses are reported in percent or hours, but not in money. Yet the conversion is trivial once a reliable machine hour rate exists — you multiply the downtime by the MHR.
| Loss type | Hours | Cost/year | Share |
|---|---|---|---|
| Availability losses breakdowns, setup | 435 | €20,171 | 45 % |
| Performance losses speed, minor stops | 392 | €18,154 | 41 % |
| Quality losses scrap, rework | 130 | €6,045 | 14 % |
| Total downtime | 957 | €44,376 | 100 % |
"Our OEE is 78 %" thus becomes: this machine loses €44,376 a year — and 45 % of it sits in availability. That is a statement plant management can set priorities on. One percentage point of OEE equals 43.5 hours or about €2,017 a year here; raising availability by five points brings in a good €10,000.
This calculation values downtime at the full hour rate — the full-cost view, correct for bottleneck machines where every hour gained can actually be sold. If the machine is not fully loaded, only the variable components are freed (on this machine €17.66/MH of €46.37/MH). Which rate applies is decided by the demand situation, not by the metric.
4 · Labor productivity — the second resource
Machines are only one side. Labor productivity relates actual output to the labor capacity deployed — usually via standard times:
= (9,980 pcs × 0.30 h) / 3,800 h
= 2,994 h / 3,800 h = 78.8 %
Two definition questions decide the significance. First: do you count only direct operators or also the area's indirect staff? Both variants are common, but they yield entirely different values — so the scope must be documented. Second: only good parts count. Counting scrap rewards production without quality.
Labor productivity and OEE can move in opposite directions. A supervisor who cuts setup times by running larger lots improves OEE — and simultaneously raises inventory and tied-up capital. A team working three machines at once lifts labor productivity but risks longer breakdown-response times and thus availability. Optimizing metrics one at a time shifts losses around; looking at them together exposes the trade-offs.
5 · A sound metric system
Four rules have proven themselves in practice, regardless of industry and company size:
- 1Document the reference base. Every metric needs a written definition — what counts in numerator and denominator. Without it, no comparison across months, machines or plants is possible.
- 2Check controllability. A metric belongs only where its audience can actually change it. OEE belongs at the machine, TEEP with plant management.
- 3Not every machine is OEE-relevant. Measurement makes sense for bottleneck machines, high value creation, high scrap risk or capital-intensive assets. Blanket recording mostly generates effort.
- 4Translate time into money. Only the machine hour rate turns a loss rate into a basis for decisions. → Chapter 7
Chapter 7, step 4 derives the planned operating time and uses the resulting 3,393 hours as the denominator of the machine hour rate — that is where the loop between time and money closes. Chapter 14 treats improving OEE as a management task.
All figures refer to the fictional reference company "Präzisionsteile Muster GmbH", worked through across the whole book. The metric definitions shown are the general state of the technical literature (incl. VDMA standard sheet 66412, Nakajima).
