What happens when you measure an AI quality workflow like a manufacturing measurement system? MAD-Ai evaluated 2,370 criterion judgments and found 93.2% repeatability and 99% reproducibility—while also uncovering ambiguous criteria and an input-format failure that normal use did not reveal.

It's tempting to blame declining quality outcomes on the people doing the work, but the data points elsewhere: 2.8 million manufacturing job openings are projected through 2033 from retirements alone, roughly a third of adults score at the lowest measured levels of numeracy, and 79% of manufacturing leaders call the labor shortage their top challenge, while only 29% expect quality headcount to grow. Institutional knowledge has to live somewhere other than the next senior hire

After two days at Booth #240 running the Quality Suite against real 8Ds and PPAPs, one question came up over and over, phrased a dozen ways: how do we know an AI system is actually right, not just confident? This post covers why that's exactly the point of Reasoning R&R™: reasoning first, then repeatability, then reproducibility, because a system can be perfectly consistent and still be consistently wrong. The offer for anyone who missed the booth still stands: bring your las

That's the line Rick Heilshorn opened with on stage at the AIAG Quality Summit, and this post lays out the argument: it's easy to build an AI system that gives the same answer every time, but perfect repeatability means nothing if the answer is wrong. Reasoning R&R™ checks whether an evaluation agrees with a known standard first, then whether it repeats, then whether it reproduces across reviewers. On MAD-Ai's own workflow, that order held up at 93.2% repeatability and 99% re

Ahead of his AIAG Quality Summit session, "Digitizing Quality Engineering Execution with AI," Rick Heilshorn previews the argument: general AI doesn't give a quality engineer a second opinion, it gives them their own opinion back with better formatting. This post walks through the Reasoning R&R™ study behind the talk (2,370 grading decisions, 93.2% repeatability, 99% reproducibility) and what it caught that years of normal use hadn't, plus where to find the team at Booth #240

Manufacturing doesn't trust a gage until it's proven repeatable and reproducible. This post introduces Reasoning R&R™, which applies that discipline to AI: does an evaluation match a known correct answer, does it repeat under the same conditions, and do different reviewers land in the same place. Running the framework on MAD-Ai's own 8D grading workflow surfaced 2,370 grading decisions, 93.2% repeatability, and 99% reproducibility between reviewers, plus twelve rubric criteri

A general-purpose AI and a purpose-built quality workflow were tested on the same automotive 8D scenario, then pushed with four escalating follow-up questions. By the fourth, the general AI called an unchanged closure "auditor-proof," though no new evidence had been introduced. This post looks at why that happens, what AI sycophancy means for a quality department, and why "is the AI good" is the wrong question. The real one: has the system been qualified, the way you'd qualif