It’s the early shift at a manufacturing plant.
The line is running smoothly—until suddenly machine 4 stops. An unplanned shutdown. The display shows a cryptic error message. Machine operator Susan, who has been with the company for three weeks, looks at the screen, perplexed.
She knows that this problem has occurred somewhere before. But where is it documented?
The experienced colleague from the night shift has already left. In her locker lies a tattered notebook with handwritten sketches and abbreviations that only she understands. In the maintenance office, there is a “malfunctions” folder, but the last entry is two years old. Some solutions were sent by email, others clarified by phone. Still others exist only in the minds of individual employees.
While the machine is idle, the search begins.
Susan scrolls through old PDFs on the network drive. File names such as “Final_v3_new” or “Fault_Old” are not helpful. She calls a colleague from another shift— voicemail. So she tries to fix the fault herself. A reset. The machine runs again briefly, then the same error occurs again. The actual cause remains unclear.
Time is passing. Production is falling behind schedule. The shift supervisor is becoming anxious. Finally, Susan remembers that Mo, the maintenance technician, helped her out last time. She calls him, and he arrives.
Mo systematically analyzes the cause and fixes the problem. Machine 4 is running again.
But just ten minutes later, machine 3 reports a similar problem. Mo is called again. He assists his colleague at the plant— and this machine is also up and running again soon.
What remains is frustration.
Mo is annoyed. Once again, he spends most of his shift troubleshooting problems that don’t actually fall within his core technical area. He isn’t supposed to solve every minor and simple problem himself.
Susan feels overwhelmed and tense. She is worried that she won’t be able to meet the demands placed on her. She would love to be able to help herself—quickly, confidently, and independently.
Meanwhile, the reject rate and pressure are increasing. The shift supervisor goes home dissatisfied.
And the next morning, it all starts over again.
This scenario is not an isolated case.
In many manufacturing companies, production knowledge has grown organically over many years – spread across notebooks, Excel lists, personal experience, and individual workarounds. Knowledge is available, but it is not transparent. It is implicit, fragmented, and often undocumented. When an experienced employee leaves the company, some of this knowledge is irretrievably lost.
For new or less experienced employees like Susan, this means that when a problem arises, they are faced with an information vacuum. Instead of being able to take targeted action, they search through folder structures, old emails, and the minds of others. Troubleshooting takes longer than necessary. Causes are not analyzed in a sustainable manner; instead, symptoms are eliminated in the short term. The same problems occur again.
This results in hidden costs:
extended downtimes
inefficient troubleshooting
recurring faults
high dependence on individual “islands of knowledge”
long training periods for new staff
The shortage of skilled workers further exacerbates this situation. When experience is lacking and there is no structured, accessible production knowledge, every unplanned disruption becomes a stress test for the shop floor.
And this is precisely where the crucial question arises:
With MADDOX, production knowledge is no longer scattered across notebooks, network drives, or individual minds. It is bundled in one place—structured, digital, and accessible at any time.
All relevant information about the plant, recurring malfunctions, best practices, and sustainable root cause analyses is stored centrally. Nothing is lost. New knowledge complements existing knowledge. Experience becomes scalable.
This creates stability on the shop floor, especially in an environment characterized by a shortage of skilled workers and high turnover.
The crucial difference:
The machine operator does not need to know what to look for. MADDOX provides active, context-related suggestions. Based on machine data and a trained algorithm, MADDOX automatically suggests the knowledge entries that match the fault situation.
A hopeless search becomes a targeted action.
The machine operator is new to the company and English is not her native language.
No problem.
All knowledge entries for troubleshooting and the entire MADDOX interface are available in any language required. Content can be translated directly. This means that every employee understands exactly what to do, regardless of their background or level of experience.
This reduces misunderstandings, increases safety, and promotes intercultural cooperation.
Theory and lots of text are of little help when things need to be done quickly.
That is why the content in MADDOX can be processed using multimedia:
The machine operator not only sees what needs to be done. She sees how it needs to be done.
In the past, only the symptom was often eliminated. In everyday life with MADDOX, the focus is primarily on the cause, which is systematically documented.
Every problem solved expands the knowledge base. MADDOX grows with every piece of feedback, with every experience in operation. Recurring errors are reduced. Downtime is reduced.
Dependence on individual knowledge carriers is decreasing.
How does it feel? Click through MADDOX yourself. With our Storylane demo, you can experience step by step how easily the machine operator is guided from the machine’s error message to the solution—without detours or searching.
The result:
Machine 4 is running again.
This time, it’s not by chance.
It’s not by improvisation.
It’s by structured, accessible knowledge — right on the line.
Susan works more confidently with MADDOX.
Mo gets to his planned tasks.
The shift stays on schedule.
The entire maintenance team is relieved.
And the company no longer loses knowledge.
In order for our AI-based assistance system to make the right suggestions in the event of an unplanned malfunction, MADDOX needs to know the status of the machine. Accordingly, we have read access to the machine data—e.g., via the OPC UA machine data interface.
The algorithm is trained by experienced personnel during a training phase at your company. The better the training, the better and more targeted the suggestions MADDOX will make in fault situations.
Every knowledge entry is available in all required languages. By integrating DeepL, we reduce communication barriers in intercultural teams and lower barriers to knowledge transfer.
To support the texts and enable important information to be captured more quickly, MADDOX offers the option of capturing scenarios in photos and videos.
Once MADDOX has been sufficiently trained by experienced personnel and can make appropriate suggestions for the fault situation, the automatic assignment option can be activated. MADDOX then assigns the appropriate knowledge entries without the need for manual intervention by the user. We would be happy to advise you personally on this.
To give the data collected by MADDOX greater visibility and usability, we provide visual processing via Grafana free of charge.
Let’s examine together how MADDOX can improve your everyday production.
