Upskilling in medium-sized manufacturing companies - technological approaches from Industry 4.0 and 5.0

Explanation: Digital twins are virtual images of machines or complete production lines. They are based on real-time data and enable realistic training in a controlled, safe environment. This allows complex processes to be practiced, sources of error to be simulated and process improvements to be tested without disrupting ongoing operations.

Implementation steps:

1. Identify relevant processes or machines.
2. Integrate design and operating data (e.g. CAD, IoT sensors).
3. Program interactive scenarios, e.g. for troubleshooting or process optimization.
4. Conduct training with feedback and analysis functions.

Best practice: A packaging machine manufacturer implemented a digital twin for its main machine series. New employees were able to simulate the production process in 3D and repeat it until they got all the steps right. Result: 25% shorter training times, 18% fewer errors in the first three months of work.

Explanation: Virtual reality (VR) immerses learners in a completely digital world in which they can train work steps realistically. Augmented reality (AR), on the other hand, expands the real field of vision with additional information such as step-by-step instructions or safety warnings.

Implementation steps:

1. Select critical activities (e.g. machine maintenance, assembly).
2. Develop or adapt suitable AR/VR content.
3. Provide hardware such as VR headsets or AR glasses.
4. Conduct training sessions with real-time feedback.

Best practice: An automotive supplier trained employees in maintenance technology with AR glasses that displayed live assembly instructions. This led to a reduction in training time from 14 to 3 days and a significant increase in occupational safety.

Explanation: AI analyzes individual learning progress, error rates and production data to create tailored learning content. This gives employees exactly the modules they need - in the right order and difficulty level.

Implementation steps:

1. Define role and competence profiles.
2. Link production and training data.
3. Train AI algorithms to generate individual learning paths.
4. Continuously monitor progress and adapt content.

Best practice: An electronics manufacturer used AI to adapt training content in real time based on recorded production errors. Within 6 months, training time was reduced by 30% and product quality improved measurably.

Explanation: Adaptive platforms adjust the pace, content and difficulty to the user's prior knowledge and learning speed. This increases the efficiency of knowledge absorption and ensures that neither under- nor overload occurs.

Implementation steps:

1. Carry out an initial test (assessment) to determine the level of knowledge.
2. Divide content into modular units.
3. Dynamically adapt learning paths based on test results.
4. Offer regular knowledge checks and certificates.

Best practice: A medical technology company met stricter regulatory requirements twice as fast by using adaptive platforms to close specific knowledge gaps.

Explanation: All employees can access up-to-date work instructions, best practices and troubleshooting guides at any time via cloud platforms. In conjunction with IoT data, problems can be identified and resolved more quickly.

Implementation steps:

1. Create a central database with structured content.
2. Standardize content and tag it with keywords.
3. Enable access via PC, tablet or smartphone.
4. Integrate feedback functions to keep content up to date.

Best practice: A machine manufacturer linked its service database with machine sensors. Employees in the technical department automatically received appropriate repair instructions in the event of faults. This reduced downtimes by 20 %.