Artificial intelligence in production

The integration of artificial intelligence (AI) in production is no longer a vision of the future, but a change that has already been realised and is significantly revolutionising the way we produce. Companies such as Bosch, Mercedes-Benz and Siemens rely on generative AI to optimise processes, increase efficiency and create new automation options.

Generative AI in practice

Bosch uses generative AI to create synthetic images for optical inspection. This technology makes it possible to train AI models without having to rely on large amounts of real data. In electric motor production, quality control is optimised through AI-based inspection of welds, resulting in a significant reduction in project time and an increase in productivity.

Mercedes-Benz uses ChatGPT to analyse production data. The AI supports employees in error analysis and process optimisation by intelligently linking large volumes of quality data. This technology makes it possible to identify and analyse potential malfunctions at an early stage, resulting in higher product quality and efficiency.

AI co-pilots and automation

Siemens has developed the Industrial Co-Pilot, an AI assistant that supports employees in automation engineering. The Co-Pilot makes it possible to generate and optimise complex programming code in natural language. This innovation significantly reduces simulation times and increases the efficiency of production processes. The automotive supplier Schaeffler uses this co-pilot to generate PLC code for programming industrial automation systems using natural language input, which increases the efficiency and creativity of engineers.

Challenges and solutions

A key issue in the use of AI is so-called "hallucination", where AI systems deliver incorrect or irrelevant results. Automated code checks can minimise this problem, although human expertise still plays a crucial role. AI relieves engineers of routine tasks, but still requires their experience and contextual knowledge to develop meaningful and error-free solutions, as human expertise remains essential to use AI tools effectively.

Further fields of application and advantages of AI in production

In addition to error analysis and quality control, AI also offers potential in maintenance and servicing. Predictive maintenance uses AI algorithms to minimise downtimes and extend the service life of machines. Sensors continuously collect data that is analysed by AI to predict the optimum maintenance time. This prevents unplanned downtime and saves costs.

Another important field of application is the optimisation of supply chains. AI can analyse large amounts of data from various sources in order to maximise the efficiency of the supply chain. Bottlenecks can be recognised at an early stage and measures can be taken to avoid delays. The automation of logistics processes through AI leads to faster and more efficient production.

Human-machine collaboration

The future of production will be strongly characterised by collaboration between humans and machines. AI and robotics are increasingly being used as co-workers in production environments. This collaboration makes it possible for machines to take on dangerous and repetitive tasks, while humans can concentrate on more complex and creative activities.

Cobots (collaborative robots) are an example of this development. They work directly with humans and can be used flexibly in different production areas. Cobots are designed to interact safely with human colleagues, which improves efficiency and safety in the workplace. However, it is still important to note that there are risks associated with cobots, such as programming errors or malfunctions. Therefore, a comprehensive risk assessment and training of employees is particularly important.

Further education and training

With the introduction of AI into production, the need for skilled workers who understand and can operate these technologies is also increasing. Companies are increasingly investing in employee training to strengthen skills in the use of AI and automation technologies. Retraining programmes are crucial to prepare the workforce for the new requirements and to ensure that the introduction of AI does not lead to job losses, but rather opens up new opportunities.

Interactive employee training on new technologies and trends such as Artificial Intelligence is crucial to keep the workforce up to date. Our inspiring sense. Workshop offers customised training sessions that promote hands-on learning and the direct application of new technologies in a collaborative environment. This workshop supports employees to effectively utilise new technologies and trends and to continuously upskill.

Reference Lenovo

Outlook: The future of production

The role of AI in production will continue to grow in the coming years. Advances in technology and the continuous expansion of 5G networks will create the basis for even smarter and more connected production environments. This development will not only improve the efficiency and quality of production, but also contribute to more sustainable and resource-efficient manufacturing.

Conclusion

The use of AI in production opens up enormous potential. From data analysis and troubleshooting to process optimisation: AI systems such as generative AI and AI co-pilots are revolutionising industrial production. Companies that implement these technologies at an early stage can achieve significant competitive advantages by increasing their efficiency, reducing costs and improving the quality of their products. The implementation of AI in production is a complex process that is associated with challenges such as data security, employee acceptance and ethical issues. It is important to take these challenges seriously and work together to find solutions to realise the full potential of AI in production, combining human creativity and machine precision to make production not only more efficient but also more accurate.