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Valuable data for predictive maintenance

Big Data Digitalization Agility DPA
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Andreas Balsiger
by Andreas Balsiger

As required by the paradigm shift in digitalization, the automation and optimization of business processes are already being intensively pursued in numerous companies. However, many of the measures taken are primarily concerned with internal processes, but the customer focus has been mostly neglected.


Use case: Worldwide machine maintenance

Imagine an industrial company that sells machines to its customers globally. Occasionally, it occurs that a machine breaks due to an acute failure. This may result in the machine being out of commission and unusable for the customer for a considerable amount of time. Even if the customer immediately calls in a service technician, a lot of time could elapse before the machine can be fixed and operational again. Costly spare parts may even have to be ordered from the manufacturer. However, with the correct use of existing data, this situation and many others can be avoided, and failure may be averted.


The correct data for precise planning of maintenance slots

An accurate assessment could be made about when the next maintenance should occur to prevent a complete breakdown based on the data collected. Different local conditions, such as humidity, climate, etc., can – and must – be considered for such an assessment.

Instead of waiting for failure, which often forces companies to act in the most adverse situations, proactive maintenance planning – based on experience and factual data – can save the customer lot of effort and costs while increasing customer satisfaction for our company providing the service.


Forecasts in real time

The collected customer-specific data allows a precise and, above all, real-time forecast of recommended maintenance slots. Based on this, an accurate assumption can be made about when and for which machine the next maintenance should take place. Suppliers can now proactively inform their customers about this service and offer them the opportunity to book a time window for online maintenance with just one click.

For a company reliant on the machines, this has the advantage of being able to plan maintenance work and reducing emergency calls to service technicians. This also has a significant impact on a company's cost structure.


Focus on customer orientation

The fact that the customer perceives this proactive information as a service, thereby strengthening all involved parties' relationships, is a side effect that should not be underestimated. A study by inContact showed that consumers react to a proactive service in a very positive manner. Specifically, 87% of respondents said they would even be happy to contact them for customer service requirements proactively. According to inContact, this shows that customer expectations are not only fulfilled but exceeded. Customer relationships can be sustainably strengthened, and new ones created. Furthermore, customers can become potential advocates of a business if proactive services inspire them.


Digitally learned

The first step for an industrial enterprise in the digital transformation sphere is to analyze which existing and potentially future data can be used to create a new, non-disruptive service. This service must be designed to be consistently supported digitally end-to-end.

According to a World Economic Forum and consulting firm Accenture study, predictive maintenance can reduce unplanned machine failures by up to 70%. The US Department of Energy (DEO) even estimates up to 75% fewer outages.