The vision of a completely autonomous factory still looks like it is a long way off. In recent years, the focus has shifted to subsets of the Industry 4.0 concept, such as predictive maintenance for machines and production lines. But even scenarios like these generate huge amounts of data that needs to be processed by the corresponding IT systems. To find out which ERP systems are particularly well suited to the performance requirements of the smart factory, the Center for Enterprise Research at the University of Potsdam took a closer look at them. This year, its annual “ERP System of the Year” award included the category “ERP for Industry 4.0/Internet of Things”. The winners were announced on 17 October – and the accolade went to Asseco Solutions for the seventh time. The view of the panel of experts was that APplus was the best of all the nominated systems at meeting the requirements of the smart factory.
“Whether the smart factory is a success depends on the data to be collected from it and analysed,” explains Ralf Bachthaler, board member at Asseco Solutions. “But that immediately produces one of the central challenges of these scenarios: the mass of data they generate can’t be managed properly with traditional technology. That’s why we decided to implement artificial intelligence to master this problem – and it’s delivered impressive results in the first few real-life projects. Our goal, as at the beginning of the Industry 4.0 movement, is to actively help shape emerging trends by the rapid adoption of new technologies. We’re delighted that our solution has won the ERP System of the Year award in what is a key strategic area for the German economy and it shows major recognition for the creative and innovative capabilities of our staff.”
The new developments in the Asseco solution were one of the most positive aspects highlighted by the panel of experts at the Center for Enterprise Research, who awarded Asseco 114 out of a possible 120 points for the Research and Development criterion. The specialists particularly praised the involvement of customers and the scientific community in the innovation process. APplus also did well in aspects specific to the smart factory, where the solution scored 166.50 out of 185 possible points. The panel highlighted the fact that the solution covered a wide range of Industry 4.0 requirements and that it offers tight integration between the ERP and the machine level.
Intelligent technology for the smart factory
APplus acts as a centralised information hub in the enterprise and offers multiple functions for connected Industry 4.0 scenarios. Using smart Asseco technology, customers can connect production lines and machines to the cloud, even if they were not originally smart systems. They can also collect and analyse their data. For machine builders, this offers the opportunity to extend the offerings around their products with new services. These could include predictive maintenance processes, smart information services for end customers, and configuration recommendations based on big data analyses. By offering functions like these, machine builders can extend their own business model and optimise it for the digital future.
Asseco Solutions is already using artificial intelligence in its Industry 4.0 solutions today, such as to detect anomalies in the production process and reduce rejects and outages. It feeds operational and error-related data into a neural network to train it to detect critical parameter constellations. If the machine data looks like it is approaching one of the critical patterns, the AI sends out an alert so that staff can intervene before the machine malfunctions or produces rejects. Unlike traditional predictive maintenance scenarios that are usually based on certain threshold values, AI-based anomaly detection can also be used by companies that do not yet have a large reservoir of historical machine operation data. In these cases, the AI learns parallel to the production process – once it detects a certain malfunction for the first time, it can predict future occurences of that malfuction before they arise.