Quality is not created by documentation alone, but by understanding.
I am Johann Fischer. I focus on how quality management, technical problem solving and modern digital tools can work together more effectively. My topics sit at the intersection of FMEA, 8D, Six Sigma, process analysis, production, electronics, data analysis and AI-supported automation.
What interests me most is how complex professional processes can be made clearer, more robust and more effective. It is not about applying methods only formally, but using them in ways that create real value in everyday work: recognising risks earlier, analysing causes more cleanly, making knowledge more accessible and preparing decisions more transparently.
Professional background
My background combines technical training, quality work, business administration and adult education:
- vocational training as an electronics technician
- certified industrial supervisor in electrical engineering (Industriemeister Elektrotechnik)
- technical business administrator (Technischer Betriebswirt)
- quality manager specialising in FMEA, 8D, Six Sigma, process optimisation and technical problem solving
- experience in electronics, production, logistics and process management
- part-time business administration studies focused on logistics and production
- adult education lecturer
Increasingly, my work is best described as AI engineering: I develop and test AI-supported applications, connect models with data and workflows, and make sure their results remain verifiable and useful in practice. As an AI engineer, I work at the intersection of AI, machine learning, data analysis, software and process automation—without treating these technologies as ends in themselves.
FQI — personal knowledge space
FQI is my personal knowledge and experimentation space around modern quality work. Here I collect, condense and reflect on topics relevant to quality, production, processes and AI — practically, methodically sound and with a critical view of value, limits and risks.
I do not publish ready-made patent recipes here. I am more interested in the path there:
- How can quality work become more intelligent?
- How can methods, data and AI be meaningfully connected?
- How do solutions emerge that are not only technically interesting, but actually help in day-to-day work?
Working method and independence
FQI visibly separates sources, professional assessment and open questions. AI can support research, structuring and wording; professional responsibility remains human. Content remains freely accessible and is not personalised through advertising tracking.
Read more about how FQI works, the AI and editorial notice and the FQI Insights.