The Interruption Whitepaper
We studied production interruptions across 12 manufacturing operations and 62 lines. What emerged is that the biggest loss on most floors isn't breakdowns. It's the frantic 74-second pulse of interruptions between them, and the reporting habits that keep it invisible.
- Across 62 lines the average time between interruptions (MTBI) is just 1m 14s.
- Sites with the most frequent interruptions averaged 12.54 points lower OEE performance: the fragmentation gap.
- Lines sort into three bands by interruption ratio: Optimised, Critical, Volatile.
- Lenient stop thresholds mask chronic interruptions, corrupting downtime reports.
- With interruption time taken out, those sites were not running slower.
Measure this on your own line. One sensor per line, live within 48 hours of the kit arriving.
The loss nobody codes
Ask a plant manager where they lose time and you'll hear about breakdowns: the failed motor, the jammed infeed, the changeover that ran long. Those are real, and they get reason codes, reports and attention. But when we measured 62 production lines at high resolution, the largest loss on most of them wasn't the breakdowns at all. It was the relentless stutter of short interruptions (what standard OEE calls minor stops or micro-stops) between them, each one too brief to code, and therefore invisible.
We call the rhythm of those stops the pulse of production. Across the studied footprint, the weighted-average Mean Time Between Interruptions (MTBI) was 1 minute and 14 seconds. Read that again: on average, a line ran for about seventy-four seconds before something stopped it. Each stop is too short to code, so the time adds up unrecorded.
The fragmentation gap
It's tempting to treat a slow line as a machine problem and reach for faster equipment. The data says to look at the stops first. When we grouped sites by how often they were interrupted, the steadiest (MTBI around 3m 27s) averaged an OEE performance of 81.21%. The most frequently interrupted (MTBI around 1m 13s) averaged 68.67%.
That 12.54-percentage-point performance gap is the fragmentation gap. In OEE, interruption time sits inside performance, so a line that stops often looks slow. With interruption time taken out, these sites were not running slower: the gap is time lost to the stops themselves, and a faster machine does not close it.
We first called this gap a speed tax. That name suggested the machines slow down between stops, which the data does not show, so we no longer use it. The comparison uses three-month site averages for August to October 2025: four frequent-stop sites against three steady ones, with sites above 100% performance left out.
Three bands of operational health
Tracking the portfolio over time produced a natural, data-driven way to grade a line. A steady-state cohort settled at a 14.28% interruption-ratio baseline; a peak-stress cohort defined a 19.82% upper control limit. Every line falls into one of three bands:
- Optimised: interruption ratio below 14.28%. Stable flow, high performance, clear reporting.
- Critical: between 14.28% and 19.82%. Drifting, and worth watching closely.
- Volatile: above 19.82%. Performance is at its lowest and the fragmentation gap at its widest.
The value of the bands is diagnostic. Knowing which one a line sits in tells you immediately whether it is losing capacity to fragmentation, or genuinely constrained elsewhere.
The buffer illusion
Here is where reporting quietly lies. Different lines use very different stop thresholds, the delay before a pause is logged as downtime, ranging from an aggressive 30 seconds to a lenient 5 minutes. Judge a line on its interruption percentage alone and you'll be fooled.
One line in the study reported a comfortable-looking 12.86% interruption ratio, apparently Optimised. But its actual time between stops was a catastrophic 15 seconds. Operators were using the generous 5-minute buffer to clear faults and manually reset the line before any breakdown code could trigger. The threshold acted as an administrative mask, hiding chronic stutter and mechanical stress from every management report.
To cut through it we use a Threshold Exposure Index (TEI = MTBI ÷ stop threshold × 100). That 12.86% line scored a TEI of just 5.06%, exposing the illusion instantly. A low TEI means the reported numbers are corrupted by the buffer, and the real losses are worse than they look.
| Illustrative line | Reported interruption % | MTBI | Threshold | TEI | Reality |
|---|---|---|---|---|---|
| Line A | 12.86% | 0m 15s | 5m 0s | 5.06% | Masked: chronic stutter hidden by a lenient buffer |
| Line B | 9.53% | 4m 8s | 0m 30s | 826% | Genuinely optimised: aggressive threshold, clear reporting |
| Line C | 3.52% | 3m 48s | 2m 40s | 142% | Optimised and honestly reported |
Cleaner data, sharper truth
Micro-friction doesn't just cost capacity, it corrupts analysis. Across the raw portfolio, the correlation between MTBI and reported unplanned downtime was a weak, confusing r = 0.41, because low-TEI lines had their breakdown data masked by extended thresholds. Filter to the clean, high-TEI data points and the correlation surges to r = 0.73.
That is the deeper lesson of the whitepaper: eliminating micro-friction is not only a capacity play, it is a measurement play. Remove the noise and true breakdown severity finally becomes visible, which is the precondition for fixing it. You cannot improve what your reporting is hiding.
What to do with this
The practical sequence is short. Measure interruptions at high resolution, not just breakdowns. Check each line's TEI before trusting its downtime report. Place every line in its band. Then attack the pulse, the highest-frequency stop causes, before spending a cent on faster equipment.
The named frameworks here (the pulse of production, the fragmentation gap, the three bands and the Threshold Exposure Index) are the tools we use to find where the time goes. The case studies show each one at work on a real floor.
What is an 'interruption' versus a breakdown?
An interruption is a short stop, often seconds, where the line halts and restarts, frequently under operator control. A breakdown is a longer stoppage that triggers a downtime reason code. The whitepaper's central finding is that interruptions, not breakdowns, are the larger hidden loss on most lines.
How was the study conducted?
It draws on site-level data from 12 manufacturing operations with 62 production lines, chosen because their 2025 data was complete. The baseline uses August to October 2025, and the study measures interruption ratios, Mean Time Between Interruptions (MTBI) and OEE performance for each site.
Is the interruption ratio the same as unplanned downtime?
No. The interruption ratio isolates high-frequency interruptions. Treated separately from breakdown downtime, it becomes a leading indicator: filter the micro-friction out and the true correlation between MTBI and unplanned downtime rises from a weak r=0.41 to a strong r=0.73.
Is the fragmentation gap a speed tax?
No. In OEE, interruption time sits inside performance, so a line that stops often looks slow. With interruption time taken out, the frequent-stop sites were not running slower. The gap is time lost to the stops themselves.
See it on your own line.
A 30-minute call with a manufacturing specialist. We look at one of your lines together and show you what the first week of data would say about it.
- Live within 48 hours of your kit arriving
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