9,848 stops in a month, and 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.
- A high-volume filling line logged 9,848 stops in its first monitored month.
- Nearly a quarter of paid time (24.6%) was waiting: bottles arriving slowly, or not at all.
- An intermittent 'No Air' utility loss took over 34 hours in one month.
- A second line showed a labelling fault worth over 93 hours in a month, hidden in vague categories.
Your plant, the same method. One sensor per line, live within 48 hours of the kit arriving.
Starting blind
A high-volume filling line was stopping constantly, and nobody could say exactly why. The filler took the blame. The first month of monitoring put a number on it: 9,848 individual stops.
The count mattered less than its shape. Nearly a quarter of paid time (24.6%) was simply waiting: bottles arriving slowly, or not arriving at all. A major external dependency surfaced too: a "No Air" supply loss of over 34 hours in a single month.
The data pointed away from the filling machine everyone had been blaming. The largest losses were upstream supply and utility stability, each with a different owner.
A second line, a hidden drain
Monitoring a second line exposed a labelling fault costing over 93 hours in a single month. It had been buried in vague categories such as "unknown". Once each stop carried a reason, it stood out on its own.
Putting the record on the wall
The plant then put a live public display in a common area. The data moved out of the back office and in front of everyone on the floor. Operators, supervisors and maintenance now read the same stops, in the same words, while the line runs.
This case reports what the first month of monitoring found. Results after that belong to the plant.
More results like this.
What monitoring reveals when you leave it running
One line, watched continuously for four months. Planned downtime took a third of its time, and the trend kept surfacing slow drifts a one-off audit would miss.
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 →What did the data show first?
That mechanical failure was not the main problem. Nearly a quarter of paid time was waiting for bottles, and a large loss came from an external 'No Air' utility dependency.
Why put the data on a public screen?
A live screen in a common area gives operators and supervisors one shared view of the line as it runs. Everyone sees the same stops, in the same words, at the same time.
What does this case leave out?
Results after the first month. The plant's later figures are its own to publish, so this case reports what the first month of monitoring found.
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