08. July 2025
We’re all facing the same challenges.
Walking through the production floor and seeing less than half of the lines running, confusion and frustration aren’t far behind. Especially younger, sharp colleagues start asking themselves: how can this happen?
I just finished a call about a new project aimed at increasing OEE by 2 %. And now I see it live and in color: 50 % of the potential—or more—is going unused. And this is during the day shift, with all technical expertise present.
Those who start looking for the causes quickly discover a whole bouquet of reasons:
Line A’s setup change is already two hours behind schedule.
On Line B, a part wasn’t cleaned and is now being caught up.
The only technician qualified for Line C is out sick.
Line D’s team is in training for a new tool that’s supposed to increase efficiency by 3 %.
A quality issue was detected on Line E, which seven QA colleagues will inspect in detail next Monday.
Two hours later: one more line is running now that the Line D team is back.
But when will the new tool be ready for Line C? Delays abound. The project request is still stuck in the third of seven approval stages, waiting on the results of a test with another piece of software before anyone wants to commit.
Idea: post a notice at Line C—just in case someone else is asking the same question.
On the way back to the office, I wonder why the Line E team can’t just jump in on Line C. The machines can’t be that different. I note the question for Tuesday’s shift meeting—it might help with the next QA topic. Hopefully.
Now it’s shift handover. Seven critical pieces of information are passed on. Two are missing.
Twenty minutes later, the first machine at Line A stops. Twenty seconds later, the rest of the line comes to a halt.
The service technician from the machine supplier can only come four days later.
Twenty minutes after that, the line runs again.
Even if everything runs perfectly from that point on, the line will no longer hit its monthly target—a classic case of avoidable production downtime.
With the introduction of MADDOX, the way disruptions are handled in Rotkäppchen-Mumm’s final packaging has been fundamentally transformed—fully in line with sustainable efficiency improvements. As soon as a disruption occurs, it’s not only signaled by a warning light but also displayed visually on a large touch panel. Employees can click on the disruption and immediately receive suggestions for possible causes and the affected part of the equipment.
What may seem like a rare exception to younger colleagues has long been accepted as the way things run by colleagues with years of experience. Through numerous exchanges with other production facilities over the years, they have learned that the reality in their own plant is no exception—colleagues at other companies face the same challenges.
“You can’t force people to share their knowledge.”
Many discussions about how similar the challenges are across industries took place most recently at our network meeting on knowledge management in manufacturing. Even before confirming their participation, the 19 involved companies already understood: many everyday production problems arise because the right colleagues don’t have the right knowledge at the right moment.
A prominent topic during the panel discussion was the question:
How can people be motivated to share their knowledge with others?
This represents the first major hurdle in knowledge management. The reasons why people keep their knowledge to themselves are diverse:
There are also subtler aspects to consider. In many conversations with technicians, the main issue is that they are constantly called to handle disruptions. Less visible, however, is the psychological need to be needed—the desire to be the hero who solves the problem.
The key question is:
How can such behavioral patterns be managed so that knowledge management still works?
In many discussions, “leadership” was identified as a central factor—even if the specific recommendations varied. There was agreement: forcing doesn’t work. Anyone forced to document their knowledge can package it in a way that is hardly useful to others.
Instead:
By the end of the network meeting, it was clear to everyone: Good knowledge management is not a nice-to-have, but a must—if you want to sustainably prevent production downtime. Only a central, structured access to relevant knowledge makes it possible to act quickly at the crucial moment.
Maybe next time, Line A will only be down for four minutes—and the monthly target will be met.
Want to learn how a self-learning assistant system like Maddox can support your team in everyday operations and make knowledge available exactly when it’s needed? Then it’s worth taking a look at our solution for AI-powered knowledge management in production.
Industry 4.0 Requires Proven Knowledge Management – How Leading Manufacturers Are Already Successfully Implementing It.
