AI Support for Downtime Recovery in a Manufacturing Company

Unplanned downtime in the factory incurred high costs and disrupted delivery commitments. When a machine unexpectedly breaks down, the production line comes to a halt – something that can cost an average of €260,000 per hour in the manufacturing industry. Every minute counts. Repairing breakdowns often took unnecessarily long because technicians first had to search for the right instructions or consult colleagues. Knowledge about breakdowns was scattered in paper manuals, various IT systems, or only in the minds of a few experienced technicians. Additionally, technicians lost time searching for parts and tools: is the required part in stock and where is it located in the warehouse? The company leadership saw that these inefficiencies led to an average of 5% productivity loss per year. They wanted to reduce downtime and standardize the breakdown resolution process.

The company gathered all relevant information for breakdown diagnosis and recovery in a central AI knowledge base. Digital manuals, maintenance schedules, troubleshooting checklists, and even instructional videos were included. Sensor and control data from the machines were linked via APIs, allowing the AI to "see" in real-time the status of a machine. The maintenance management system and warehouse management system were also connected to track inventory and history. PrudAI implemented INDY (Industrial Digital Yield) to unlock this knowledge. Technicians accessed INDY via a tablet and headset. During a breakdown, they can type or even speak a question ("Why is line 3 down?") and INDY interprets the question, searches all data, and provides immediate advice. Uniquely, a technician can also take a photo of a broken part: the AI recognizes the part, searches for the relevant instruction ("replace this part according to plan X"), and immediately checks in the warehouse system if the part is in stock and where it is located.

Added value of AI (INDY): INDY combines computer vision, NLP, and integrations with company data to provide technicians with useful answers as quickly as possible. Instead of time-consuming browsing through manuals or consulting multiple systems, technicians receive one interactive answer via the AI chat. This answer includes, for example, a repair plan, illustrations from the correct manual, and states "take part 123 from warehouse location B-12 (in stock)". The AI is trained on thousands of breakdown scenarios and continuously learns, allowing it to even provide predictive advice ("This breakdown resembles a previous pattern, likely cause Y – check component Z first"). This enables less experienced technicians to tackle complex breakdowns as if they have a senior expert by their side. Research shows that AI-driven maintenance systems can significantly reduce unplanned downtime – Deloitte reports that predictive maintenance can reduce downtime by 30–50%. This aligns with the results INDY demonstrated in practice. Moreover, INDY is used to continuously train the team: the AI periodically presents quiz questions and scenarios to technicians to test their knowledge. These scores are linked to the HR system, allowing for targeted additional training. Thus, INDY functions not only as a troubleshooter but also as a digital coach for the maintenance team.

The deployment of INDY has significantly improved the company's maintenance organization. Breakdowns are resolved 25–40% faster on average than before, reducing downtime by about a third. This means production outages have been greatly reduced – a huge cost saving given the high downtime costs per hour. Technicians report experiencing less stress during breakdowns: they now follow a clear step-by-step plan and know they are not overlooking anything. The number of recurring breakdowns also decreased because the AI consistently recommends the right preventive actions (e.g., "also clean sensor X to prevent recurrence"). As INDY elevates all employees to a higher knowledge level, the dependency on one or two "breakdown gurus" has disappeared – the entire team performs more consistently. Finally, the company noticed an increase in productivity: operators and planners have to wait less time for the line to restart, and production targets are now met more reliably. With INDY, the organization has taken a step towards a smart factory where humans and AI work seamlessly together to minimize downtime.