nextLAP says Production 4.0 requires a holistic approach to the manufacturing process (Photo: Audi)
Predictive maintenance is one of the best know applications that fall under the header ofIndustry 4.0, but nextLAP considers it only a first step toward a fully new approach to manufacturing. The startup, which has offices in Munich and in Silicon Valley, aims to predict the entire production process and manage it in real time. Only then will Production 4.0 become a reality.
Predictive maintenance, which analyzes data from production machinery with the help of big data analytics, is only a part of the new approach. “Here you’re, for example, looking at a machine as if it were a drummer in an orchestra,” said nextLAP Managing Director Andre Ziemke. “But what’s important is to have all instruments on the screen.” That information needs to then be visualized as a process.
Ziemke, who used to be in charge of production IT at premium car brand Audi, said digital production is more about the ability to predict the entire production process than about the state of individual machines or equipment. “Predictive process control” is how nextLAP calls this approach, which facilitates the early detection of process anomalies on the basis of data. Early detection allows quick proactive countermeasures.
In nextLAP’s approach, a continuous real-time digital picture of the production process, which includes both manufacturing and logistics, helps plan, shape, steer and supervise the entire chain. “This kind of holistic digitalization can only be realized with the help of internet-of-things (IoT) technologies,” Ziemke said. nextLAP uses mini PCs and intelligent devices for that purpose.
To make the system work, everything that’s involved in the production process has to be connected to one platform. That includes machinery, pick-by-light racks, tools, robots, autonomous transport systems, containers, drones and people. In assembly, for example, the monitor shows which part is next. The worker takes the step digitally. The “smart rack” then registers which part has been retrieved and it makes sure a new part is procured. The electronic power screwdriver knows exactly how many revolutions are needed for a certain part. It also checks whether the screw joint is correct and it reports the result back to the platform.
Everything in the plant has an IP address and is connected to the platform. If something goes wrong somewhere in the process ”“ regardless of whether it is in assembly or logistics ”“ the cloud-based IoT platform knows right away and can offer possible scenarios for solving the problem before it has a negative impact on the production process.
The nextLAP system has already been tested in a pilot project at Audi and it has won several awards for its approach and the efficiency gains achieved. The company is also testing the system with a US-based maker of electric premium vehicles it doesn’t want to name.
Production and logistics don’t exist in separate worlds for nextLAP. Its platform monitors and manages all parts of the manufacturing process. The software gathers process parameters generated at different points in the process. This could be when a part needs to be assembled, a machine’s throughput, or the location of a truck that brings parts to the factory. Suppliers should also be part of the process.
With car manufacturing becoming more complex, Thomas Stoeckel, another nextLAP managing director, says it is crucial to have an overall picture of the process. “Experienced production and logistics people know by instinct when something will cause problems and how much room to move remains,” he said. “That’s tough for the younger generation as complexity in manufacturing continues to grow.”
The nextLAP platform forms the basis for a series of algorithms that absorb knowledge and can learn. These algorithms can, for example, chieck whether, in the case of an anomaly, it is OK to wait or whether immediate action is required.
Just as in other business areas, Ziemke said, data are rapidly gaining in importance in manufacturing. “No intelligent manufacturing without data,” he said. And he added that full digitalization of the manufacturing process can only be realized “if all data are available in real time and can be controlled from one location.”
-By Daniela Hoffmann Â Â