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SEAT/CUPRA

Sector Digitalization

In automotive plants, efficiency is measured in seconds: any improvement in cycle time directly impacts thousands of vehicles produced. Added to this challenge is the constant need to incorporate new operators in an environment with high turnover.

Traditionally, training depended on expert workers who had to leave the production line to pass on their knowledge, causing productivity losses and the risk of inconsistencies. Aware of this reality, SEAT/CUPRA and Aritex decided to explore how augmented reality could be integrated into the plant’s routine.

The goal was to transform training into an autonomous, intuitive, and repeatable process, capable of preserving critical knowledge and ensuring that each new operator quickly reached the required quality level.

Challenge to be solved

The main challenge was to digitize the knowledge of experienced operators and turn it into clear, accessible instructions for newcomers.

Experts often perform complex tasks automatically, combining steps and using their own language, which is difficult to translate into traditional manuals. This situation created a bottleneck: every time an experienced worker had to stop their work to train a newcomer, production slowed down.

Moreover, staff turnover meant constantly repeating this process, with the added risk of losing valuable know-how when a worker changed positions or left the company. The challenge was twofold: ensuring knowledge continuity and providing an effective learning experience that did not interfere with the plant’s pace.

Proposed solution

The answer was Digital Mentor, a training tool based on augmented reality that guides operators step by step through assembly processes. Using a tablet, the trainee overlays 3D models onto physical materials, receiving visual and textual instructions in real time.

This solution is more intuitive to use and avoids isolating the worker, as happens with AR glasses, allowing them to remain connected to their work environment. The content was designed after observing and breaking down each expert’s movements, transforming their experience into easy-to-follow sequences.

The system allows exercises to be repeated as many times as necessary, accelerating the learning curve and reducing errors. In this way, training becomes an autonomous, safe, and standardized process, freeing experts from repetitive tasks while maintaining their productivity.

Impact

·       Significant reduction in training times

·       Freeing expert profiles for higher-value tasks

·       Preservation of the plant’s critical knowledge

·       Greater autonomy and motivation for new operators

·       Reduction of errors and improved safety

·       Scalability to other processes and industrial plants

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