Three shifts, one live board
A bag manufacturer running three shifts 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 the question with data.
- Manual tallies gave one number per shift and no record of where the hours went.
- Live downtime data separated genuine losses from planned changeovers.
- Short stops nobody logged added up across three shifts.
- Supervisors moved from end-of-week paperwork to a live board.
Your plant, the same method. One sensor per line, live within 48 hours of the kit arriving.
Growing faster than the paperwork
The site ran three shifts a day making bags. Output was tallied by hand at the end of each shift: a single number on a sheet. There was no record of the stops, slow-downs and changeovers that shaped it.
Then demand climbed. The team was asked whether the machines had the headroom to produce more. Nobody could say, because nobody had measured what the machines did between the start and end of a shift.
Making the lost hours visible
Augos put live production and downtime monitoring on the lines. Every stop was timestamped and, over the first weeks, given a reason: planned changeover, material wait, mechanical fault, or an operator pause. For the first time, a short shift could be told apart from a shift that stood still for two hours.
The question stopped being "how many did we make?" and became "where did the hours go, and which of them can we get back?"
Much of the time was hiding in plain sight: repeated short stops that no one logged one by one, because each felt too small to matter. Added together across three shifts, they were a large block of time.
Managing against a live board
With the losses named, the work was unglamorous and specific: the slowest changeovers, the highest-loss stop causes, and what each shift had shown it could do. Supervisors managed against a live board instead of rebuilding the day from a sheet the next morning. Whether a line "could do more" became a question the record could answer.
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 → Food and beverage · Beverage bottlerThe 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 → 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 →How was output tracked before?
On paper, tallied at the end of each shift. That gave a total but no breakdown of where the hours went, so there was nothing to act on.
What did the live data change first?
It made downtime visible as it happened, so supervisors could respond to a stopped machine in minutes instead of finding out the next day.
Where was the hidden time?
In repeated short stops that no one logged one by one, because each felt too small to matter. Added together across three shifts, they were a large block of time.
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