A better-formatted 8D doesn't protect your customer — the problem-solving behind it does. General AI polishes language. MAD-Ai validates containment against AIAG standards, crosswalks root cause to your PFMEA, and flags what's missing before submission.

When quality engineers talk about AI, they don't ask for dashboards. They ask for supplier 8D review, PFMEA crosswalks, and MRB workflow automation — the execution work that drains engineering capacity every week.

When quality engineers talk about AI, they don't ask for dashboards. They ask for supplier 8D review, PFMEA crosswalks, and MRB workflow automation — the execution work that drains engineering capacity every week.

There's a critical difference between AI that helps your team work faster and AI that does the work. Discover why execution AI transforms quality capacity — not just productivity — delivering 29x faster supplier quality management.

Quality teams need AI that executes, not AI that generates. Learn why chatbots fall short of what manufacturing quality work actually demands — and what execution AI does differently across 8D, PFMEA, and corrective action workflows.

We’re excited to announce that Rick Heilshorn has been selected to speak at the AIAG Quality Summit this September. This opportunity reflects years of hands-on experience in engineering, manufacturing, quality, and AI-driven innovation and we’re proud to see MAD-Ai contributing to the future of smarter manufacturing and quality systems. If you’re attending the summit, make sure to put Rick’s session on your to-do list.

Corrective actions are often treated as a single step: define the fix and implement it. In reality, corrective action is the output of a much larger process. Before a corrective action is defined, quality engineers must: Validate the problem statement Confirm containment effectiveness Analyze potential root causes Evaluate contributing factors across process, material, and logistics Each of these steps involves gathering, interpreting, and structuring information from multipl

8D is a standardized methodology. The steps are well-defined. Templates exist. Expectations are clear. And yet, it consistently takes longer than expected. The reason is not the framework—it is the work required to complete it. An 8D does not start with clean, structured data. It starts with: Incomplete issue descriptions Inconsistent defect information Multiple stakeholders providing input at different times Pressure from customers for rapid response Before an engineer can e

There is a gap between how AI is marketed and how it is actually used on the plant floor. In practice, quality engineers are not using AI for abstract analytics or high-level insights. They are using it for very specific, repeatable tasks tied to daily responsibilities. Common use cases include: Building structured 8D reports from issue descriptions Developing 3-Legged 5-Why analyses and corrective actions Drafting supplier responses and customer dispute documentation Reviewi