2:00 PM – 2:25 PM
Machine builders have access to an increasing amount of data from engineering, production, and the installed base. But how do you convert that information into concrete improvements to machines, parts, and service processes?
In this breakout, Chris Thomassen from IFS shows how AI enables the closed learning loop between engineering, operations, and service.
Using an end-to-end scenario, we track a component experiencing recurring failures. AI helps identify failure patterns and determine the potential cause. Subsequently, we examine how this can lead to an engineering change, how the impact on existing machines and processes can be analyzed, and how AI agents can assist in carrying out follow-up actions.
In addition, we are also looking at the next step: digital workers. Which tasks will engineers, planners, and service employees no longer need to perform themselves in the future, but can they delegate to AI in a controlled manner?
The session combines approximately 15 minutes of vision and practice with a 10-minute demo, in which you discover what such a closed AI loop can look like in practice.
What are you bringing?
- How AI can bring insights from service and asset data back to engineering;
- How to move from a failure pattern to an engineering change and impact analysis;
- What role AI agents play in executing and coordinating follow-up actions;
- How digital workers can take over repetitive and time-consuming tasks;
- How AI thereby strengthens the connection between the machine you design, the machine you build, and the machine you maintain.
Speaker: 12Guide