A few years ago, SBB prescribed three optimization programs that will cost around €1 billion by 2027. In terms of traffic management, the aim is to make better use of routes, in particular by reducing the distances between trains. Production planning wants to get more kilometers out of people and materials, ensuring that trains stand still as little as possible, and that train drivers spend as much of their working time as possible driving rather than on other things. The third part of the program, asset management, is intended to reduce material wear and tear, and make better use of the workshops.
Of the €1 billion allocated to the three programs, only €20 million is allocated to AI, however. “Nevertheless, this opens up opportunities for us that we didn’t have before,” says Decker, who’s been working in this space at SBB for five years.
AI enabling predictive maintenance
What fascinates Decker about AI is not only its possibilities, but its low costs, like in wheelset and track management. With the help of constant monitoring of wheel wear by cameras and sensors, and the evaluation of the results obtained in the process, data allows him to predict very precisely when a wheel needs to be replaced. If this forecast is then matched with the utilization data from the repair shop, it becomes real predictive maintenance since the wheel is replaced neither too early nor too late, and the repair shop has the time and capacity to make the change immediately. “The prerequisite for this is high-quality data,” said Decker, yet it doesn’t take a lot of money — at least for the AI. In this example, its use accounted for less than €300,000.
