47th FVI Expert Breakfast

Topic: Digitalization in Maintenance: What can be implemented immediately and easily? – From manageable pilot projects to long-term sustainable system architecture.

The session made it clear: Successful digitalization neither begins with a 1,000-page specification nor with the search for the perfect platform. What matters are a specific pain point, a comprehensible target image, and a solution that is actually used in the shop floor's everyday work.

• Start small, but not aimlessly: Several participants advocated not discussing the perfect system for months, but starting with a clearly defined use case. Examples included a particularly failure-prone machine, a simple sensor "with duct tape," or even a smartphone as a temporary sensor solution. The pilot does not have to be built for eternity – it should quickly show what data is available and what benefits can arise from it.
• Bad processes do not improve with software: Before digitalization, it must be understood how order processing, fault reporting, or documentation actually work today. A practical example showed the risks of unclear master data particularly drastically: Instead of the calculated four data imports, around 186 corrections were ultimately necessary. The central insight was therefore: Digitalization often has a lot to do with "tidying up the children's room" – at the same time, the necessary cleanup should not become a years-long entry barrier.
• Acceptance is created on the shop floor: The technical solution was described in the discussion as the easier part. More difficult is the transition from old working methods to the digital world. Projects are particularly successful when users, critics, and experienced employees are involved early on. A visible benefit, such as the elimination of routing slips, manual operating hour recordings, or multiple documentation, creates acceptance faster than abstract digitalization promises.
• The benefit must be concrete and measurable: A dashboard with "500 KPIs" brings no added value if no operational decision results from it. Large programs, in particular, come under pressure if their ROI is only supposed to become visible after five or six years. Therefore, small use cases with clear acceptance criteria, short implementation periods, and an 80/20 prioritization were recommended. At the same time, an overarching target image for interfaces, cybersecurity, and integration is needed so that individual pilots do not create a new shadow IT.

Classification: With ADAM from the specific bottleneck to Operational Intelligence

This episode fits perfectly with our strategy because ADAM does not start as a monolithic IT mega-project but pragmatically solves an acute problem and gradually develops a reliable digital knowledge and process base.

• Analysis paralysis and risky mega-projects: Companies often plan a complete platform before the first user has even tested a real process. This increases complexity, costs, and project risk. We deliberately start smaller with ADAM: In a clearly defined PoC, we solve a specific bottleneck within a few months. As a SaaS platform and with no-code components, ADAM can be gradually expanded and integrated into existing systems through open interfaces. This creates measurable benefits early on without losing sight of the long-term target image.
• Master data chaos and brownfield reality: Historically grown data, different designations, and incomplete plant records make classic migrations expensive and slow. ADAM is "ADAM-First" and Brownfield-Native. With hybrid search and vector search, employees can find documents, spare parts, and previous solutions even if the master data is not yet perfectly cleaned up. At the same time, each use creates a more structured digital life record of the machine – including faults, measures, documents, modifications, and retrofits.
• Low acceptance and lost experiential knowledge: If technicians have to fill out additional forms or operate complicated interfaces, the documentation remains incomplete. The knowledge stays in heads, notebooks, and chat logs. We therefore rely on the most natural interface: language. With the message, solution, and feedback assistant, employees can document faults and work performed directly from the work process. ADAM structures the information, transfers it into existing systems if necessary, and makes it usable as Operational Intelligence for the entire team – like a "YouTube for maintenance" that learns from every solved fault.

Conclusion: The digitalization of maintenance does not begin with the perfect data base or the largest software project, but with a relevant problem, an accepted workflow, and a quick success – ADAM combines exactly this pragmatic start with a long-term scalable platform for Operational Intelligence.