The 3 Most Important Arguments for Using AI in Your Service
Machinery and plant engineering in Germany has been distinguished by its high innovation power for many decades. Our engineering expertise is valued worldwide. The "Made in Germany" label stands for the highest quality and reliability everywhere in the world, which generates good sales figures internationally.
In the growing international competition, the innovation power of traditional German industry is needed more than ever today. After many years of incremental improvements to hardware, electronics, and software components, the industry has reached a point of highly optimized manufacturing processes. This is good, but it also means that the leverage for outstanding mechanical innovation continues to get smaller.
To continue to retain customers with new innovations and differentiate from competitors with similarly good technology and lower prices, outstanding service is becoming increasingly important as a differentiator. In particular, connecting and analyzing machine data can significantly improve service quality.
2 Application Scenarios for AI in Service
Machine and plant builders typically use a combination of preventive and reactive service, meaning machines are serviced regularly and when a serious malfunction occurs, the customer contacts service, which identifies the problem and, if necessary, sends a service technician to the customer.
In reactive remote service, the service technician accesses the machine and searches the log files for clues about the cause of the malfunction. AI can accelerate the search for the problem cause by automatically examining sensor and control data for irregularities and providing the service technician with targeted information about unusual signal patterns when accessing the machine.
The goal of preventive service is to identify and fix problems before they become a malfunction. Traditional maintenance at fixed intervals is based on empirical values for the wear of individual machine components. With AI, on the other hand, it is possible to intervene only when necessary. This can be achieved through Predictive Maintenance. Here, the AI determines the necessary time interval for the next maintenance based on wear predictions. A second option is for the AI to automatically recognize signal patterns that have caused problems in the past and proactively send a warning to the service technicians. They can analyze the case, assess the risk, and, if necessary, coordinate a service visit with the customer.
Predictive maintenance is technically more demanding because the AI needs labeled data about past failures during its training phase. The second option requires no or significantly less labeled data and can also effectively prevent failures. It is important that both preventive approaches can only be implemented if the machine operator consents to data access. If operators exclude transferring data to the cloud, a compromise could be to allow data access only when necessary, as is already common with remote maintenance.
3 Good Reasons for Using AI in Service
With the application scenarios described above, you as a machinery or plant builder can offer your customers improved service quality. From our perspective, there are three key business reasons to invest time, money, and energy in this innovation:
- Strengthen Service as a Differentiator
When purchasing a machine or plant, customers expect the highest quality and reliability at the lowest possible price. Because this is market standard, it is difficult to differentiate significantly from established competitors based on process performance. Service, however, is different. With value propositions such as "100% machine availability" or "50% reduced time to identify the cause of a failure," you can clearly differentiate yourself in the market and thus have another strong argument on your side when acquiring new customers. - Increase Revenue in the Service Business
Your new AI-powered services can also be offered as an additional digital service that your customers can optionally add. The price your customer is willing to pay for this depends largely on how expensive downtime is for the customer. - Reduce Costs in Service
With hundreds or thousands of machines in the field, every efficiency improvement in your service processes brings immediate cost savings. Specifically, AI can help you reduce both the personnel hours for diagnosing problems and the number of on-site service visits.
Your company's priorities determine which of these arguments will be most compelling.
Conclusion
The use of AI in service offers machinery and plant builders valuable potential for improving service quality and customer experience. AI can be used in both reactive and preventive service. From a business perspective, the investment in AI can pay off for you if you pursue one of the following three goals:
- You want to strengthen your service as a differentiator.
- You are looking for ways to increase the revenue of your service business.
- You want to reduce service costs.
If that applies to you and you are still looking for a suitable software solution, please feel free to contact us about aiXbrain Dataray.
