Predictive Maintenance Service Business Instead of Repair Mode—Which Mistakes Machinery Manufacturers Should Avoid

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In many companies, service is still reactive. Instead, problems should be prevented before they occur. The solution: predictive maintenance and service as a business model.

Mechanical engineers sell machines. That was the deal for a long time. But today, no customer buys just a machine anymore—they buy performance, availability, smooth processes. And that's where the problem lies: the service is lagging behind. Companies that still act reactively and only take action when something breaks down lose out. The rules of the game have changed: those who do not automate now will remain stuck in repair mode while the competition turns service into a business model.

The problem is not the insight—it is there. Most machinery manufacturers know that service is the next growth lever. Yet in implementation, many stumble over the same mistakes. They optimize the existing instead of questioning the basic logic. They digitize the old instead of thinking of new processes. And in the end, service looks the same as always: cumbersome, slow, expensive.

1. Mistake: Service Remains Reactive Instead of Proactive

Service in many companies follows the old pattern: the customer reports a problem, a ticket is opened, a technician comes. Sounds logical, but it is inefficient. The true lever of efficiency does not lie in quick reaction, but in preventing problems before they occur. Predictive maintenance is not a buzzword but the basic prerequisite for modern service. Instead, mechanical engineers dutifully collect sensor data without real consequences.

An example: A machine reports via IoT data that a wear part will fail in three weeks. And what happens? Nothing. The information is in the system, but it doesn't trigger a process. Only when the machine stops does the search for spare parts begin. The technology is there, but the process is missing.

What should happen instead: automated spare parts ordering, automatic maintenance planning, and proactive customer communication. A technician should not only be dispatched when equipment fails, but before it happens.

Modern service companies think in terms of "Mean Time to Repair" (MTTR)—how long does it take to resolve the problem? The best not only reduce repair time, they eliminate downtime entirely. Predictive maintenance has long been standard in safety-critical industries—no airline operator waits to service an engine only after a failure. The automotive industry shows that it also works on an industrial scale.

2. Mistake: There's Plenty of Data—But No One is Using It Correctly

Data is the new gold? Not quite correct. Data is only valuable when it is used. And here lies the next problem: mechanical engineers have all the data they need—but in practice, it remains unused. This is because their IT systems do not communicate with each other. SAP knows a spare part is available, but the service tool does not know the inventory. The CRM has customer data, but no real-time information on the machine. Thus, the service remains "fragmented" and far slower than it could be.

A practical example: A mechanical engineer wants to provide customers with real-time machine condition data. The problem: The sensor data is there, but it resides in different systems—one in the ERP, another on an IoT platform, a third in a service tool. Three departments, three data sources, zero automation. This issue could be easily solved with a seamless infrastructure that connects all systems and triggers processes. The customer immediately sees on their platform if a spare part is running low—and the system initiates an order in the background.

A similar case is seen with the United Grinding Group, which faced precisely this challenge. The company manages over 100,000 machines worldwide with a spare parts inventory of several thousand components. The machines provided valuable sensor data, but the information remained siloed in separate systems—spread across ERP, CRM, and service platforms. Technicians had no real-time access to the data, spare parts orders were processed with delays, and maintenance schedules were organized manually.

The solution: a centralized service infrastructure that connects data flows and automates processes. Today, the company works like this:

• Plan service operations early before a machine failure threatens.

• Automatically reorder spare parts when critical values are reached.

• Integrate customer systems seamlessly into the digital service—without media disruptions, without Excel.

That is the difference between data management and true service automation. Simply collecting data yields nothing. Those who automate processes that directly derive decisions from this data win. It's not a matter of IT, but of the business model.

3. Mistake: Patchwork Processes Instead of Continuous Automation

Many machinery manufacturers have settled for a fragmented system landscape. An ERP here, a ticket system there, an Excel list here. Everything somehow works, but nothing connects. Manual handovers, emails with information that no one reads, orders that are stalled because no one feels responsible—this is still the order of the day in many companies.

The solution is simple: an infrastructure that connects all processes. Not a new isolated solution, but a platform that intelligently integrates existing systems. Service processes must be able to manage themselves—if a machine reports that a component will soon fail, this should not be a static piece of information. It must trigger a spare parts order, schedule a technician, and offer the customer maintenance. Automatically. Without Excel, without waiting time.

Machinery manufacturers who view service as a business model don't need prettier dashboards; they need processes that run. Automatically, seamlessly, intelligently. Those who achieve this don't just offer machines —they offer availability, performance, and real efficiency. And that's what customers really buy today.

The technology for this exists. The only question is: who will use it correctly first?

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