Many places in the supply chain hold latent information with a great deal of potential for making processes more efficient. This pertains to the performance of an entire system as well as individual elements. Using machine learning enables companies to benefit from more efficient workflows. For example, orders can be picked and sent to customers without errors and with maximum speed.
TGW considers machine learning from three perspectives: the object level, the material flow level and the machine level. Objects can, for example, be picked by the self-learning order picking robot Rovolution. It reacts completely on its own to unexpected events such as an article falling down during the gripping operation. This ensures interruption-free work around the clock. A highly complex algorithm looks at data to develop an understanding of the scenario, making it possible to assess and classify the condition. On this basis, the Rovolution robot can make autonomous decisions about how to handle an article being picked.
On the material flow level, machine learning can help to control the system so that there are no bottlenecks and all workstations are evenly occupied. TGW is currently developing prediction models that enable precise adaptation of a system to seasonal fluctuations or changes in customer order behaviour. The models recognise patterns that are not immediately apparent to a human brain. This makes the process of drawing conclusions and making decisions in everyday business much faster.
At the machine level, the main task is to analyse and understand the condition of individual components, as part of condition monitoring for example. This makes it possible to reduce downtime by scheduling dates and times for maintenance at an early stage.