---
title: "Which OEE factor is costing you the most time?"
description: "Compare availability, performance and quality in lost minutes, check the inputs behind each factor, then weigh each loss by its cost to see where to look first."
category: "OEE and metrics"
author: "Data Analytics Team"
published: "2024-07-25"
updated: "2026-10-20"
source_url: "https://www.iotflows.com/blog/which-oee-factor-loses-most/"
---
> Question: Where do I look first when OEE is low?

## Key takeaways

- Compare OEE factors in lost minutes, because each factor’s percentage sits on a different base of time.
- A point of availability costs more minutes than a point of performance, and a point of performance more than a point of quality.
- Check the ideal cycle time, the short-stop cutoff and the scrap log before you trust which factor looks weakest.
- Weigh each loss by its cost: scrap wastes material as well as time, and a minute matters most on the bottleneck.
- Split the leading factor into its six big losses and work on the largest bucket first.

When OEE (overall equipment effectiveness) is low, look first at the factor that loses the most minutes of planned production time, not the factor with the lowest percentage. Convert availability, performance and quality into lost minutes, check the inputs behind each one, then weigh the minutes by what they cost on that machine. The largest loss that survives those checks is where you start.

One week on a CNC mill runs through every step below. If you need the formulas first, [How to calculate OEE](/blog/how-to-calculate-oee/) works one shift by hand.

## Why the lowest percentage misleads

OEE multiplies three factors, and each one is a ratio of a different amount of time. *Availability* divides run time by planned production time. *Performance* divides the ideal time for the parts made by run time, and *quality* divides good parts by all parts.[1]

*Ideal cycle time* is the best cycle the operation sustains in good conditions, timed at the machine rather than taken from a quote. Total count × ideal cycle time is the ideal time for the parts made: the least time they could have taken.

Because the base shrinks from one factor to the next, a percentage point is worth less time at each step. A point of availability is 1% of planned production time. A point of performance is 1% of run time, and a point of quality is 1% of the ideal time for the parts made.

> **Definition.** **OEE loss in minutes** is the planned production time a factor took away from good output at the ideal cycle time. The availability, performance and quality losses add up to planned production time minus good count × ideal cycle time.

For example, a mill with 2,250 planned minutes, 1,800 run minutes and 1,400 ideal minutes of output loses 22.5 minutes per point of availability, 18 per point of performance and 14 per point of quality. The glossary entry for [performance](/docs/get-started/glossary/#performance) gives the second factor’s full definition.

## Convert each factor into lost minutes

Each factor’s loss is the planned time it took away from good output. Three subtractions give the minutes.

**Availability loss** = Planned production time − run time

**Performance loss** = Run time − (total count × ideal cycle time)

**Quality loss** = (Total count − good count) × ideal cycle time

The three add up to planned production time minus good count × ideal cycle time. That is the gap the short form of OEE measures: good count × ideal cycle time ÷ planned production time.[1]

#### Worked example: One week on a Haas VF-2 vertical mill

- Five 8-hour day shifts with a 30-minute lunch each give 2,250 minutes of planned production time.
- Stops total 450 minutes, so run time is 1,800 minutes.
- Ideal cycle time is 2.5 minutes. Total count is 560 parts: 532 good and 28 scrapped.

Availability is 1,800 ÷ 2,250 = 80.0%. Performance is (560 × 2.5) ÷ 1,800 = 77.8%. Quality is 532 ÷ 560 = 95.0%.

Availability lost 2,250 − 1,800 = 450 minutes. Performance lost 1,800 − 1,400 = 400 minutes. Quality lost 28 × 2.5 = 70 minutes.

**Result:** OEE = (532 × 2.5) ÷ 2,250 = 59.1%, with 920 minutes lost

| Factor | Percentage | Base | Minutes lost |
|---|---|---|---|
| Availability | 80.0% | 2,250 planned min | 450 |
| Performance | 77.8% | 1,800 run min | 400 |
| Quality | 95.0% | 1,400 ideal min | 70 |
| All three | 59.1% OEE | 2,250 planned min | 920 |

Figure: Three bars of lost minutes for one week on a mill. Availability at 80.0% loses 450 minutes, performance at 77.8% loses 400 and quality at 95.0% loses 70. The lowest percentage is not the largest loss.

*Performance has the lowest percentage, but availability loses 50 more minutes. Sample data.*

A team that chases the lowest percentage starts on the smaller loss here. Minutes put all three factors on one scale, so you can rank them.

## Split the leading factors into their losses

A factor tells you the kind of loss. The *six big losses* tell you the cause, and each one belongs to exactly one factor.[2]

| Factor | Loss | What it looks like on a mill |
|---|---|---|
| Availability | Equipment failure | A broken tap, a spindle alarm |
| Availability | Setup and adjustments | Changeovers, first-part checks |
| Performance | Idling and minor stops | Chip clearing, door opens |
| Performance | Reduced speed | Cycles slower than the ideal |
| Quality | Process defects | Out-of-tolerance bores |
| Quality | Reduced yield | Rejects while a new job settles |

Loss names and factor mapping follow the six big losses of TPM.

Minor stops last a minute or two and need no maintenance visit.[2] Where they land depends on the cutoff your plant sets for logging a stop: shorter stops stay inside run time and lower performance. The post on [micro-stops](/blog/micro-stops/#oee) shows how that cutoff moves minutes between availability and performance while OEE stays the same.

Split the mill’s 920 minutes into buckets and rank them. You need a reason on each stop for availability, and a stop record that keeps short stops for performance.

| Loss | Factor | Minutes | Where the number comes from |
|---|---|---|---|
| Reduced speed | Performance | 250 | 400 − 150 short-stop minutes |
| Setup and adjustments | Availability | 210 | Four changeovers |
| Idling and minor stops | Performance | 150 | 180 stops averaging 50 s |
| Waiting for material | Availability | 120 | Two waits for bar stock |
| Equipment failure | Availability | 80 | A broken tap, a spindle alarm |
| Reduced yield | Quality | 45 | 18 startup rejects × 2.5 min |
| Unclassified stops | Availability | 40 | Stops with no reason |
| Process defects | Quality | 25 | 10 rejects × 2.5 min |

Sample data. The six big losses count any unplanned stop, including material waits and stops with no reason, as equipment failure. Those stay separate here because each needs a different fix. The rows add up to 920 minutes.

Reduced speed leads at 250 minutes, ahead of changeovers at 210. Before you start a speed project, check the number that produced it. To rank stop reasons over a longer window, build a [downtime Pareto](/blog/downtime-pareto/) on minutes, not counts.

## Check the inputs before you trust the result

Every factor rests on a number someone entered or a record someone kept. A wrong input moves minutes into the wrong factor, or invents them.

Performance depends on the [ideal cycle time](/blog/ideal-cycle-time/) more than on anything the machine does. Suppose the mill’s 2.5-minute ideal came from the job quote. Timing 20 consecutive good cycles gives 2.8 minutes as the best the process sustains.

With 2.8 minutes, the 560 parts are worth 1,568 ideal minutes. Performance rises to 1,568 ÷ 1,800 = 87.1%, and its loss falls from 400 to 232 minutes. Of the 250 minutes booked as reduced speed, 168 were a gap between the quote and the machine, and 82 remain.

Figure: Two bars of performance loss on the same week. With a 2.5-minute ideal from the quote, the loss is 400 minutes and performance 77.8%. With a timed 2.8-minute ideal, the loss is 232 minutes and performance 87.1%.

*A quoted ideal cycle time put 168 minutes into performance that the machine never lost. Sample data.*

Changeovers now lead at 210 minutes, so [measure changeover time](/blog/measure-changeover-time/) before you plan that project. OEE becomes (532 × 2.8) ÷ 2,250 = 66.2%. Quality’s loss grows to 28 × 2.8 = 78.4 minutes, because each scrapped part carries more ideal time.

Keep 2.5 minutes only if a different program or tooling sustains it on this machine, since the ideal is the best cycle the operation sustains. Then the 168 minutes are a real performance loss with a known cause. Otherwise set the number from timed cycles, as the guide to [setting the ideal cycle time](/docs/production/cycle-times-and-filters/#ideal) describes.

Measured cycles also test the times you plan with. Jesel, a valvetrain manufacturer, compares measured cycle times against the times in its bills of material every day, as its [customer story](/customers/jesel/) describes.

| Factor | Input that misleads | Check |
|---|---|---|
| Availability | Breaks counted as stops | Remove breaks from planned time |
| Availability | Sensor offline read as stopped | Look for gaps with no data |
| Performance | Ideal cycle time from a quote | Time 20 good cycles |
| Performance | Parts per cycle set wrong | Hand count 10 parts |
| Quality | Rejects never logged | Compare the log with the scrap bin |
| Quality | Rework counted as good | Count first-pass good parts only |

Quality counts only parts that pass the first time, so a reworked part is a reject.[1] The guide to [first pass yield](/blog/first-pass-yield/#rework) explains why. A quality factor at 100% for weeks deserves a check against the scrap bin before anyone reads it as a result. Give each reject a reason when you [log scrap](/docs/production/log-scrap/), so quality minutes appear beside the stops.

## Rank the losses by what they cost

A lost minute isn’t worth the same everywhere. Two things set its price: what the loss consumes besides machine time, and whether the machine limits the plant’s output.

The *bottleneck* is the machine whose capacity limits what the whole line can ship. The Lean Enterprise Institute describes it as the weak link in the system, the one constraint that stops the next unit from being made with current resources.[3]

A minute lost on the bottleneck is a minute of output lost for the line, while a minute lost on another machine is absorbed by its spare time. To prove which machine that is, see [how to find the bottleneck](/blog/find-the-bottleneck/#definition).

#### Worked example: Pricing the mill’s week after the ideal cycle time check

- The mill is the bottleneck of its cell. Each minute of it is worth $2.50 of contribution margin.
- Each scrapped part also wastes a $40 blank of 4140 steel.

Availability costs 450 × $2.50 = $1,125. Performance costs 232 × $2.50 = $580. Quality costs 78.4 × $2.50 + 28 × $40 = $196 + $1,120 = $1,316.

**Result:** Quality costs the most, $1,316, with the fewest lost minutes

Figure: Two panels for the same week. In lost minutes, availability leads with 450, then performance 232 and quality 78.4. In cost, quality leads at $1,316, then availability $1,125 and performance $580, because each reject also wastes a $40 blank.

*Priced in dollars, quality moves from the smallest loss to the largest. Sample data.*

Now suppose the mill has spare capacity and finishes the week’s work by Thursday. Its lost minutes cost little until demand rises, and the $1,120 of scrapped blanks is the only cash that leaves. The first project is the 18 startup rejects, and the stop and speed work moves to whichever machine is the bottleneck.

> **Rank by minutes or by cost?**
>
> Rank by minutes when the machine is the bottleneck and each reject wastes little material. Rank by cost when a reject wastes material worth more than the machine time it took. When the machine has spare capacity, start with quality, and spend the stop and speed effort on the bottleneck.

## Read the pattern across shifts and machines

One week on one machine gives you a ranking. The same ranking across shifts, part numbers and machines points at the cause before you walk to the floor.

| Pattern | Likely cause | First check |
|---|---|---|
| Availability low on one shift | Slow starts and handovers | Stops in the first 30 min of the shift |
| Availability low on changeover days | Setup length | Changeover minutes per job |
| Performance low on one part number | That operation’s ideal cycle time | Time 20 cycles of that part |
| Performance falls through the week | Tool wear or a slowing feed | Cycle time trend by day |
| Performance low, few logged stops | Short stops under the cutoff | Count stops under 2 minutes |
| Quality low after each changeover | Startup rejects | Scrap in the first hour of a job |
| Quality at 100% for weeks | Rejects not logged | Logged scrap against the scrap bin |

Each row compares the same factor across shifts, parts or days on one machine.

Look at the factor before you look at the people. Low performance on one part number points at that operation’s cycle time on every shift that runs it, not at the operator who happened to run it last.

## See each factor on its own

IoTFlows reports [OEE factor by factor](/solutions/oee-tracking/), so the weakest factor is on screen without a calculation. A SenseAi on each machine decides running or stopped every second and counts parts from the machine’s own signal, with no PLC connection.

Each machine’s card shows Uptime, which is availability, and the parts gauge, which is performance against the ideal cycle time set on each operation. Quality reads on Shift Production as good parts over everything counted, from the scrap your team logs with a reason. The [metrics reference](/docs/monitoring/metrics-reference/#performance) defines each number.

For a month or a quarter, sort the [Advanced Report](/docs/monitoring/advanced-report/#rows) by **Worst Performing** to put the machine with the lowest uptime at the top.

Open that machine, and its **Downtime Classifications** card ranks its stop reasons by hours lost.

## Find your biggest loss this week

1. **Pick one machine and one week.** Choose the machine that limits output, or the one with the most scheduled hours.
2. **Convert each factor to minutes.** Subtract run time from planned time and ideal output from run time, then multiply rejects by the ideal cycle time.
3. **Check the inputs.** Compare the ideal cycle time with timed cycles, and the scrap log with the scrap bin.
4. **Split the losses into buckets.** Rank stop reasons, short stops, slow cycles and rejects by minutes.
5. **Price the top three losses.** Multiply minutes by what a minute of the machine earns, and add the material in each reject.
6. **Fix one loss and remeasure.** Work on the largest bucket, then compare next week’s minutes with this week’s.

## Questions

### Should I fix the OEE factor with the lowest percentage first?

Not by default. Each factor is a percentage of a different base, so the lowest percentage can lose fewer minutes than a higher one. Convert each factor into lost minutes of planned production time, then weigh those minutes by what they cost on that machine, before you choose a project.

### Why does my quality factor read 100%?

Quality reads 100% when no rejects were recorded, which is not the same as no rejects made. Compare the scrap logged over a week with what went into the scrap bin and the rework area. Count reworked parts as rejects, since OEE quality counts only parts that are good the first time.

### Do short stops count against availability or performance?

It depends on the cutoff your plant sets for logging a stop. Stops shorter than the cutoff stay inside run time and lower performance, and longer stops lower availability. OEE is the same either way, but the cutoff decides which factor carries the loss, so keep it fixed when you compare weeks.

### Can improving one OEE factor lower another?

Yes. Running a machine faster can raise performance and add scrap, and skipping a warm-up can raise availability and add startup rejects. Check all three factors and the total lost minutes after any change, so a gain in one factor is not paid for by a larger loss in another.

## Sources

1. [OEE Calculation: Definitions, Formulas, and Examples](https://www.oee.com/calculating-oee/), Vorne Industries, accessed Oct 16, 2026
2. [Six Big Losses in Manufacturing](https://www.oee.com/oee-six-big-losses/), Vorne Industries, accessed Oct 16, 2026
3. [What’s Your Company’s Bottleneck? (Brent Wahba, 2014)](https://www.lean.org/the-lean-post/articles/whats-your-companys-bottleneck/), Lean Enterprise Institute, accessed Oct 16, 2026