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 micro-stops between them, and the reporting habits that keep it invisible.
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 recoverable loss on most of them wasn't the breakdowns at all. It was the relentless stutter of short interruptions 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. Assets in that state never reach kinetic equilibrium. They spend their lives decelerating and ramping back up.
It's tempting to treat a slow line as a machine problem and reach for faster equipment. The data says otherwise. When we grouped lines by how fragmented their running was, a clear penalty appeared. The most stable cohort (MTBI around 3m 27s) posted an average Performance score of 81.21%. The most volatile cohort (MTBI around 73s) collapsed to 68.67%.
That 12.54% performance gap is a Speed Tax: a penalty paid purely for process fragmentation. It cannot be recovered by running the machine faster, because the machine is rarely the constraint. The constant stopping is.
Translated into the metric that pays the bills, that 12.54% of Performance corresponds to roughly 4.89% of total OEE, capacity that is sitting on the floor, unbought and unbuilt. On a single small operation that reclaimed capacity was worth around R9.6 million per quarter; scaled across a large enterprise portfolio losing in the order of R724,000 per production hour, the same mechanism is worth up to R96.6 million per quarter.
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:
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.
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 |
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-stops 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.
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 tier. Then attack the pulse, the highest-frequency stop causes, before spending a cent on faster equipment. On the lines we studied, that order of operations is where the 4.89% of OEE actually lives.
The named frameworks here, the pulse of production, the Speed Tax, the three tiers, the Threshold Exposure Index, are the tools we use to find it. The case studies show each one at work on a real floor.
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.
It draws on 30 validated monthly data points captured across 12 manufacturing operations and 62 production lines over a rolling historical horizon, measuring interruption ratios, mean time between interruptions (MTBI) and OEE performance for each.
No. The interruption ratio isolates high-frequency micro-stops. 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.
Moving a line from the 1m 14s micro-stutter state toward the 3m 27s optimised benchmark reclaimed 5.64% of total plant capacity on one studied site. Across the portfolio the performance-to-OEE relationship implies up to 4.89% of OEE is recoverable this way.