Unplanned plant shutdowns are one of the most costly problems in industry. According to ABB’s “Value of Reliability” study, German industrial companies incur an average cost of €147,000 per hour. The situation is particularly critical in the automotive industry, where costs can even reach peak values of up to 1.85 million euros per hour. The study also shows that 67% of companies are affected by such downtimes at least monthly, with a frightening 21% still acting purely reactively – in the so-called “Fire Fighting Mode”. (Source: ABB study)
RAG systems: Increasing efficiency through intelligent information processing
Retrieval-augmented generation (RAG) systems combine semantic search technologies with generative artificial intelligence. In concrete terms, this means that technicians and engineers receive precise answers to complex technical questions within a few seconds. RAG systems search through hundreds of manuals, maintenance protocols and technical documentation, identify relevant points and provide the user with concrete recommendations for action, including references.
Practical example: troubleshooting on a production line
To clarify a concrete scenario: An alarm “E42 (Servo axis B overload)” occurs on a packaging line. On average, technicians spend about 45 minutes alone finding the relevant information in 12 manuals with a total of about 900 pages. The total repair time is about four hours, which at the average downtime cost of 147.000 €/h causes a total burden of 5888.000 €.
With an RAG system, however, the information gathering is reduced to less than two minutes. The repair can be shortened to about one hour – a saving of three hours or € 441,000 per incident.
Predictive maintenance: predictive maintenance with measurable success
Predictive maintenance, supported by RAG technology, increases plant availability by 10-20 % while reducing maintenance costs by 5-10 %. One example illustrates the potential: a press plant with 300 hours of unplanned outages per year (cost: around €44.1 million) can halve the outages with RAG-supported predictive maintenance. This corresponds to annual savings of around 22 million euros.
Intelligent spare parts management reduces capital commitment
An often underestimated cost driver is inefficient spare parts management. In fact, 41% of all spare parts are never used in many companies and thus unnecessarily increase storage costs. Through the use of RAG systems, a leading automotive supplier (Tier-1) was able to reduce the storage value of its spare parts from the original EUR 42 million to EUR 32 million – a saving of 24 %.
Make information search in the service desk efficient
Technicians spend an average of 6.5 hours a week simply searching for technical information. In a company with 50 technicians, this results in enormous productivity losses. The implementation of an RAG system reduces the daily information search by up to two hours per technician. The annual savings potential through time alone thus amounts to approximately 1.76 million euros.
Conclusion: Competitiveness through rapid knowledge availability
The integration of RAG systems into industrial processes offers enormous opportunities for a sustainable increase in efficiency and competitiveness. Rapid access to relevant information not only reduces downtime costs and inventories, but also optimizes maintenance processes and sustainably improves overall productivity. RAG systems are thus a decisive step towards Industry 4.0 and ensure long-term success for the company.
Find out more: How we implement RAG systems from idea to operation can be read on our services page AI solutions: RAG systems & AI agents.
Would you like to implement this in your company? We support you pragmatically – from the idea to the operation.