What monitoring reveals when you leave it running
A single line, watched continuously over four months. The first finding was that planned downtime took a third of the line's time. The more useful one was what the trend kept surfacing: slow drifts a one-off audit would miss.
- At the start, planned downtime took 33% of the line's time.
- Much of that "planned" time was habit rather than requirement.
- Continuous data showed slow drifts in cycle time, rejects and stop causes.
- A one-off audit gives a snapshot; monitoring gives the trend.
Your plant, the same method. One sensor per line, live within 48 hours of the kit arriving.
The value isn't in the snapshot
Plenty of sites run a one-off study, get a number, act on it and move on. This line was left under continuous monitoring for four months instead. A snapshot shows where you stand today. A trend shows where you are drifting, and keeps showing it after the first round of fixes.
Where the time went
The first finding was the size of planned downtime: 33% of the line's time. The record showed how much of that "planned" time was habit rather than requirement. Changeovers ran long, buffers covered problems that no longer happened, and some stoppages had never been revisited.
Unplanned stops get attention because they hurt. Planned downtime is treated as fixed and rarely challenged, which is why it can hide the largest block of time on a line.
What kept surfacing
The more interesting story was what the data caught week to week. A cycle time creeping up on one station. A reject rate ticking higher on one product. One interruption cause (what standard OEE calls minor stops or micro-stops) appearing more and more often. None of these showed on any single day. On a four-month trend, each stood out clearly.
That is the case for leaving monitoring running rather than sampling. Every change resets the baseline, and the next issue is already on the chart before it costs a shift.
More results like this.
The filler was not the problem
A beverage bottler blamed its filler for a line that kept stopping. The first month of monitoring showed 9,848 stops, nearly a quarter of paid time spent waiting, and an air-supply loss of over 34 hours.
Read the case study → Packaging and bags · Bag manufacturerThree shifts, one live board
A three-shift bag maker tracked output on paper, one number per shift. Demand was rising and nobody could say whether the machines had headroom. A live record of every stop answered it with data.
Read the case study → Food and beverage · FMCG packing site218,000 tiny stops, hidden in one bucket
No single breakdown, yet OEE sat at 26.5%. The losses were 218,000 interruptions lumped into one uncategorised bucket, until a change in data resolution made them visible.
Read the case study →Why does planned downtime matter as much as breakdowns?
Planned downtime is treated as fixed, so it rarely gets questioned. On this line it took a third of the time, and much of it was habit rather than necessity.
What did continuous monitoring catch that an audit wouldn't?
Slow drifts. A creeping cycle time, a reject rate ticking up, a stop cause appearing more often week on week. These only show up when you compare this week with the last three months.
What does this case leave out?
The plant's results after the changes. Those are the plant's own to publish, so this case reports what the record showed.
Find losses like these 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
- One sensor, one terminal, no PLC and nothing to integrate
- R4,950 per line per month, ex VAT. 90 days, then a month's notice