NTT DATA and Hyster-Yale bring physical AI to assembly checks

NTT DATA and Hyster-Yale Materials Handling have deployed physical AI for quality assurance at the lift truck manufacturer’s facility in Berea, Kentucky. The jointly developed system embeds checks into a critical assembly workflow, helping production teams identify potential problems before products leave the factory floor.

NTT DATA integrated vision sensors, edge AI and advanced analytics into the process. Working with Archetype AI and Hyster-Yale, it adapted a physical AI model to compare assembly activity with expected production steps. The system validates that parts are installed and assembly stages are completed, flagging deviations before a product moves to the next stage.

Processing runs locally on-site through edge computing. Early results showed deployment timelines falling from months to weeks compared with legacy techniques, supporting faster adoption and iteration across manufacturing operations. The companies are exploring how to scale the approach to support consistent production quality across Hyster-Yale’s operations.