Predictive maintenance is also a key enabler for new, service-based business models, such as pay per use. Our IoT Consultant, Simon Nogglersupports companies in the introduction of predictive maintenance and talks in an interview about the added value that proactive maintenance creates for manufacturers and customers and how it can be used to improve the quality of their products. Earning money.
1 Why is predictive maintenance such a hot topic right now?
Simon Noggler: One of the reasons is that there are more and more networked machines that collect and transmit data. In addition, the costs for performance and computing power are falling. This means that larger volumes of data can be processed than was originally the case. With the growing number of standard solutions that are now available on the market, it is possible to Predictive maintenance use cases easier to implement than before.
2 What added value does predictive maintenance offer companies and their customers?
Simon Noggler: Thanks to the data-based information gained, plant and machine manufacturers can make decisions at an early stage, manage after-sales activities better and plan resources more accurately in advance. The added value: they can offer their customers more suitable services that go beyond simply selling the machine. By analysing the data, any potential for optimizing the machines is identified at an early stage and can be passed on to research and development in short feedback cycles.
On the customer side, productivity increases because unplanned downtime can be avoided. This results in more reliable planning of production processes and therefore more punctual deliveries. During operation, predictive maintenance is used to continuously determine the system or device status. This monitoring makes it possible to efficiently predict when and whether maintenance is actually necessary. Ideally before any complications arise.
The use of predictive maintenance is therefore a classic win-win situation - for the manufacturer and its customers.
3. what are classic use cases for predictive maintenance?
Simon Noggler: A classic application is the condition monitoring of wind turbines. Above a certain temperature, the rotor blades freeze, which inevitably leads to failure. By incorporating third-party data, in this case weather data, forecasts can be made and the turbines can be switched off proactively. In this case, a shutdown is a better alternative than expensive repair costs.
Predictive maintenance is also used in aircraft construction: By generating sensor data and expected values, spare parts can be ordered and replaced before wear even occurs. This avoids waiting times due to a possible failure and reduces the likelihood of dangerous situations on the flight route.[1]
However, these are just two of many examples that show that predictive maintenance is an efficient solution for many problems.
More about predictive maintenance4 What are the biggest challenges in a predictive maintenance project?
Simon Noggler: The basic prerequisite for a predictive maintenance project is, of course, that the necessary quantity and quality of data is available in order to derive meaningful business decisions from it. This requires many years of specialist knowledge and the willingness to learn quickly from any errors that occur. Another challenge is that more expertise is needed in the area of software development. This expertise needs to be built up independently and/or purchased.
5. what opportunities are there for companies to earn money with proactive maintenance?
Simon Noggler: Predictive maintenance can initially serve purely to optimize processes and thus save costs. In addition, new business models based on predictive maintenance can generate new sources of income. As a result, the manufacturer is evolving from a pure seller to a service provider. This enables them to address new target groups and areas of demand.
The questions were asked by: Hanna Pfaff
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Sources:
[1] https://www.zuehlke.com/blog/predictive-maintenance-faktenbasiert-entscheiden-am-beispiel-produktion/
