Manufacturers today face two critical challenges: the rapid loss of expert knowledge as experienced workers retire, and the limited accessibility of production data in environments where full system integration is not yet possible. These challenges make traditional approaches to root cause analysis increasingly unsustainable.
A new study from Arch Systems explores an innovative solution: AI Dashboard Vision. This method introduces AI root cause analysis in manufacturing by enabling artificial intelligence to interpret screenshots of factory dashboards and deliver expert-level diagnostics and prescriptive guidance—even when modern API access is not available.
Inside the Experiment
The study compared four approaches to diagnosing a component scrap issue on FUJI pick-and-place machines:
- Human subject matter experts with more than 20 years of experience
- PhD-level engineers without specific domain knowledge
- ChatGPT using a basic prompt
- Arch’s Expert AI Agent using structured data and expert-guided reasoning
Each approach was evaluated for accuracy, clarity, and time to resolution.
What the Results Showed
Arch’s Expert AI Agent consistently delivered results on par with or better than human experts. It was able to identify root causes and recommend next steps in a matter of seconds, whereas human experts typically took around 10 minutes.
The general-purpose tools, including ChatGPT and the PhD engineers, were directionally correct but struggled with precision. Their responses lacked the structure and clarity needed for practical use on the factory floor.
Perhaps most importantly, the study showed that dashboard screenshots alone can serve as a reliable data source for AI, as long as the AI is guided by an expert thought process. This removes a key barrier for many manufacturers that still rely on legacy systems.
Implications for the Industry
This research points to a significant opportunity for manufacturers looking to:
- Reduce their reliance on hard-to-replace expert labor
- Deploy AI without overhauling their infrastructure
- Improve response time to production issues
- Scale consistent best practices across sites and teams
AI root cause analysis in manufacturing is now capable of delivering expert-level accuracy using the same dashboards factory teams already depend on. With the right data and structure, AI can become a real-time advisor to front-line teams, improving decision-making and response times on the production floor.
Conclusion
AI Expert Guidance, powered by dashboard vision and expert reasoning patterns, is no longer a concept on the horizon. It is being used today to solve real problems in real factories. As AI root cause analysis in manufacturing continues to prove its speed and accuracy, it presents a compelling alternative to traditional troubleshooting methods.
For manufacturers seeking to increase uptime, improve quality, and adapt to a shrinking labor pool, this capability offers a faster, more scalable path forward.
Read the full study Comparing AI Dashboard Viewing and Human Diagnostic on FUJI Systems