---
title: "Manual vs automatic OEE tracking in the first week"
description: "Machine-reported stops move lost minutes from performance into availability. OEE itself moves only if part counts, ideal cycle times or planned time were off."
category: "OEE and metrics"
author: "Mahmoud Parto"
published: "2024-08-09"
updated: "2026-10-20"
source_url: "https://www.iotflows.com/blog/manual-vs-automatic-oee-tracking/"
---
> Question: What happens to my OEE when I stop writing stops on paper and let the machine report them?

## Key takeaways

- Machine-reported stops lower availability and raise performance, because short and rounded stops move from one factor to the other.
- OEE equals good count times ideal cycle time divided by planned production time, so run time and stop logs cancel out.
- OEE itself changes in the first week only when part counts, ideal cycle times, planned time or scrap records were wrong.
- Performance above 100% on the new data means the ideal cycle time is set too slow, not that the machine got faster.
- Automatic capture still needs people to set ideal cycle times, log scrap and give unexplained stops a reason.

When you stop writing stops on paper and let the machine report them, your availability drops and your performance rises to match. The short stops, rounded durations and shift-start gaps the paper missed were already in your OEE, hidden as slow running. OEE itself moves only if your part counts, ideal cycle times or planned production time were wrong, and the first week is when you find out.

The sections below work one shift both ways, list the inputs that do change OEE and give a five-day plan for the switch. They’re written for plant managers who need the new numbers believed on the floor.

## Manual and automatic tracking defined

> **Definition.** **Manual OEE tracking** is the practice of recording stops, part counts and scrap by hand, on a shift sheet, whiteboard or spreadsheet, and calculating availability, performance and quality from those records after the shift ends.

> **Definition.** **Automatic OEE tracking** is the practice of recording run state and part counts from a signal on the machine, every second and every cycle, so availability and performance are calculated without anyone writing a stop down; scrap and stop reasons still come from people.

OEE multiplies three factors. Availability is run time over planned production time, performance is ideal cycle time × total count over run time, and quality is good count over total count.[1] The [OEE glossary entry](/docs/get-started/glossary/#oee) gives the same factors in IoTFlows’ terms.

Neither method changes the formula. They change who records each input and when, as the figure shows.

Figure: A map of six OEE inputs. Run state, part count and total count come from the machine, every second or every cycle. Planned production time, ideal cycle time and scrap with its reason are entered by people.

*Run state and counts come from the machine; planned time, ideal cycle time and scrap still come from people.*

## Why the stop log can’t change OEE

Multiply the three factors and run time cancels out. It sits in the numerator of availability and the denominator of performance, so what’s left is a ratio with no stops in it:

**OEE** = (Good count × ideal cycle time) ÷ (Planned production time)

This is the single most useful fact for the first week. Every stop the machine finds that the paper missed takes minutes from run time. Availability falls, performance rises by the same factor, and OEE stays where it was.

#### Worked example: One shift on a 150-ton injection molding machine, recorded two ways

- Shift: 480 minutes with a 30-minute lunch, so planned production time is 450 minutes.
- Ideal cycle time: 30 seconds (0.5 minutes) for a single-cavity mold. Total count: 690 parts, 669 good.
- Paper log: a material wait written as 20 minutes and a nozzle freeze-off written as 15, so 35 minutes stopped.
- Machine record: the same two stops at 27 and 18 minutes, a 12-minute start-of-shift gap and 24 short stops averaging 1.5 minutes, so 93 minutes stopped.

From the paper log, run time is 415 minutes. Availability is 415 ÷ 450 = 92.2% and performance is (690 × 0.5) ÷ 415 = 83.1%.

From the machine record, run time is 357 minutes. Availability is 357 ÷ 450 = 79.3% and performance is 345 ÷ 357 = 96.6%.

Quality is 669 ÷ 690 = 97.0% either way.

**Result:** OEE = (669 × 0.5) ÷ 450 = 74.3% both ways

Availability fell 12.9 points and nothing on the floor changed. If you report availability on its own, warn the team before the first shift report goes out. For each factor worked step by step, see [how to calculate OEE](/blog/how-to-calculate-oee/).

## Where the missing minutes come from

The paper log in the example held 35 of the 93 stopped minutes. The other 58 came from three places, and each one has its own fix.

Figure: A waterfall of stopped minutes. The paper log holds 35 minutes in 2 stops. Rounded times add 10, the shift start adds 12, and 24 short stops add 36, the largest share, for 93 minutes in the sensor record.

*The 58 stopped minutes the paper missed, by source; short stops are the largest share. Sample data.*

- **Short stops.** A *minor stop* is one that lasts a minute or two and needs no maintenance technician, and most companies don’t track them accurately.[2] Here, 24 jams and clears cost 36 minutes nobody wrote down.
- **Rounded durations.** An operator writes the stop when it’s over, from memory, in round numbers that run short. The 27-minute material wait became 20 and the 18-minute freeze-off became 15.
- **Shift edges.** The warm-up and the restart after the handover rarely reach the sheet, because the machine never “broke”. Here they cost 12 minutes.

The split between availability and performance depends on a cutoff. One published example policy gives every stop longer than 2 minutes a reason, which files it under availability; shorter stops stay in performance.[2] Pick a cutoff, write it down and keep it, so the same jam lands in the same factor every week. For how short stops behave and how to cut them, see [micro-stops](/blog/micro-stops/).

## What does change OEE in the first week?

OEE moves only when an input in the formula above changes. The first week of machine data tests each one, and most plants find at least one that was off.

| Input | How paper gets it wrong | What machine data shows |
|---|---|---|
| Part count | Tallied or copied from the traveler | A count every cycle |
| Ideal cycle time | The quoted standard, with allowances | Actual cycles faster than the ideal |
| Planned production time | Warm-up left out of the shift | Stops at both shift edges |
| Good count | Reworked parts counted as good | Nothing: scrap still needs a person |

A part that needs rework is not good in OEE quality, which counts only parts that pass the first time.

Quality is the factor automation helps least. A molding machine runs the same cycle for a short shot as for a good part, so someone at the machine or an inspection step decides which parts were scrap.

The ideal cycle time causes the largest surprises. It should be the [best cycle the operation sustains](/blog/ideal-cycle-time/) in good conditions, so performance above 100% means the ideal is set too slow.[1]

#### Worked example: The same shift with the routing’s 36-second standard

- The paper sheet used the routing’s 36-second standard (0.6 minutes) as the ideal cycle time.
- Paper performance: (690 × 0.6) ÷ 415 = 99.8%, which looks plausible.
- Machine performance: 414 ÷ 357 = 116.0%, which is impossible.
- Correcting the ideal to the 30 seconds the machine sustains at its best brings performance to 96.6%.

**Result:** OEE falls from (669 × 0.6) ÷ 450 = 89.2% to 74.3%, a measurement fix

Figure: Three performance bars against a dashed 100% line. Paper log with a 36-second ideal reads 99.8%. Sensor record with the same ideal reads 116.0%, past the line. Sensor record with a 30-second ideal reads 96.6%.

*Unlogged stops inflate run time and hide a slow ideal cycle time; the machine record exposes it. Sample data.*

The paper record hid the error, because unlogged stops padded run time and pulled performance under 100%. The machine record exposed it on day one.

> **Don’t pad the ideal cycle time**
>
> Set the ideal cycle time from the best cycle the machine sustains, timed over 10 to 20 consecutive cycles, not from the quoted standard. A padded ideal raises performance and OEE, and it hides the speed losses OEE exists to show. Fix it once, per operation, in the first week.

## Manual and automatic capture compared

Each method records the same inputs with different precision. The difference is widest for stops and counts, and nil for scrap.

| | Paper or spreadsheet | Machine-reported |
|---|---|---|
| Stop start and end | Written from memory | Recorded to the second |
| Stops under 2 minutes | Rarely written down | Every one, with its duration |
| Stop reason | Written by the operator | Picked by the operator or a rule |
| Part count | Tallied at shift end | Counted every cycle |
| Ideal cycle time | Set by an engineer | Set by an engineer |
| Scrap and rework | Logged by a person | Logged by a person |
| Numbers arrive | After the shift | While the shift runs |

Use paper when you’re proving the habit on one machine for two or three weeks, before you spend anything. Use machine-reported data once you need short stops, more than one machine or numbers during the shift.

## Automatic capture still needs your definitions

Switching the capture method doesn’t fix the inputs people set. A study from Chalmers University of Technology shows what happens when nobody does.

The researchers analyzed six months of data from 23 Swedish manufacturers and 884 machines, all running the same automatic OEE system.[3] Median OEE was 70% and the average 65%.[3]

- **Performance at the default.** 702 of the 884 machines reported 100% performance, the system’s default value, which suggests no cycle times were measured.[3]
- **Quality at the default.** 796 of the 884 machines reported 100% quality, also the default.[3]
- **Losses with no reason.** Unclassified losses took about 19% of scheduled production time, more than half of all recorded loss time.[3]

With performance and quality left at their defaults, the authors question whether those companies measured OEE at all, or only availability.[3] Their conclusion: when measurement is automated, managing the details of each process matters more, not less.[3]

Three tasks in your first week prevent each of these failures: set an ideal cycle time per operation, log scrap at the machine, and give every unexplained stop a reason. A short reason list gets used; see [which downtime reason codes to log](/blog/downtime-reason-codes/).

## A five-day plan for the switch

Run the paper log and the machine record side by side for one week on the machines you switch first. Paper stays the reference for reasons and scrap; the machine becomes the reference for minutes and counts.

1. **Define shifts and breaks before day one.** Enter the plant’s timezone, shifts and scheduled breaks so planned production time matches the floor. Shifts sort data as it arrives, so set them before the first machine reports; see [shifts and timezone](/docs/admin/shifts-and-timezone/).
2. **Check the count against ten parts.** On day one, count ten parts by hand as they come off and compare them with the machine’s count. A four-cavity mold set to one part per cycle reads a quarter of its real output; the [ten-part check](/docs/production/quickstart/#validate) says how close is close enough.
3. **Correct each ideal cycle time.** On day two, check performance for each operation. Performance above 100% means the ideal cycle time is set too slow, so enter a faster one; see [set the ideal cycle time](/docs/production/cycle-times-and-filters/#ideal).
4. **Classify routine stops by rule.** On day three, set rules that classify short stops and stops at the shift edges, so operators explain only the stops in the middle of a shift. [Automatic downtime rules](/docs/monitoring/auto-downtime-rules/) covers each one.
5. **Log scrap at the machine.** From day three, operators log each bad part when it happens, with a reason, instead of tallying the bin at shift end. The [scrap logging steps](/docs/production/log-scrap/#log) show the entry.
6. **Reconcile the week factor by factor.** On day five, put the paper and machine figures side by side for each shift. Explain the stopped-minute gap by source: short stops, rounding, shift edges and offline time. [Reconcile with a manual calculation](/docs/monitoring/troubleshoot-monitoring/#manual-check) lists the differences that remain.

After day five, retire the paper log and mark the switch date on every trend chart. Set the first goal a few points above the new baseline, never the old paper figure; [choose an OEE goal](/docs/monitoring/oee-goals/#choosing) explains why.

## Track OEE from the machine

A SenseAi sensor mounts magnetically on the outside of each machine and detects every stop in seconds, with no PLC connection and no operator input. IoTFlows reports [OEE factor by factor](/solutions/oee-tracking/): availability as uptime, performance as parts counted against the ideal cycle time, and quality from the scrap operators log with a reason. Data lands in the shifts you define as it arrives.

The Short Downtimes rule classifies stops under a per-machine cutoff, 5 minutes by default, and the shift-start and shift-end rules cover the edges. Operators put a reason on what’s left in a tap.

[Sorinex](/customers/sorinex-exercise-equipment/) installed SenseAi sensors across its critical production equipment in under 5 days, and its story reports an 87% improvement in root cause visibility within the first month.

## When manual tracking is enough

Choose manual tracking when:

- **You track one machine** for a few weeks to prove the habit before spending anything.
- **Your stops are long and rare,** such as a machine that runs a 6-hour cycle and stops twice a shift.
- **You haven’t set ideal cycle times yet,** since performance means nothing without them on either method.

Choose automatic tracking when:

- **Short stops are frequent,** because no operator can write down 24 jams in a shift.
- **You run more than one machine or shift,** and the numbers need to match from one to the next.
- **You need the numbers during the shift,** while there is still time to recover the loss.

Whichever you choose, fix the inputs first. A correct count, an honest ideal cycle time and a trusted shift schedule matter more to your OEE than the method that records the stops.

## Questions

### How long should I keep the paper log after switching to automatic tracking?

Keep it for one week on the machines you switch first. The paper log is your reference for stop reasons and for the scrap count while operators learn the new entry, and the week gives you enough shifts to reconcile both records. Retire it once the reasons and scrap appear in the new system every shift.

### Do operators still enter anything once OEE tracking is automatic?

Yes, two things. They give a reason to each stop no rule has classified, and they log scrap at the machine with its cause. Run state and part counts come from the machine, but no signal can tell a good part from a bad one or say why a machine was waiting.

### Should I restate last year’s OEE after switching to automatic tracking?

No. Mark the date the capture method changed on every trend chart and treat the first full week of machine data as a new baseline. Old paper figures and new machine figures measure different things, so a drop at the switch date reflects the measurement, not the machine.

### Why did availability fall while output stayed the same?

The machine record found stops the paper log never held: short stops, the minutes lost to rounding and the gaps at the start and end of the shift. Those minutes were already lost, so output does not change. They move from performance, where they hid as slow running, into availability.

## Sources

1. [OEE Calculation: Definitions, Formulas, and Examples](https://www.oee.com/calculating-oee/), Vorne Industries, accessed Oct 16, 2026
2. [OEE Six Big Losses](https://www.oee.com/oee-six-big-losses/), Vorne Industries, accessed Oct 16, 2026
3. [Analysis of Critical Factors for Automatic Measurement of OEE (Procedia CIRP 57, 2016)](https://research.chalmers.se/en/publication/246913), Chalmers University of Technology, accessed Oct 16, 2026