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.

General AI is powerful for drafting, research, and exploration, but an open chat is not automatically a controlled quality process. Learn how purpose-built AI workflows can define inputs, evidence, criteria, and repeatable methods without replacing engineering judgment.

Quality & AI | Wednesday, October 7, 2026 Manufacturing quality engineers are used to dealing with variation. When a new gage, inspection method, measurement system, or process is introduced, we don't simply ask whether it works. We ask how much variation it introduces. That discipline exists for a reason. If three trained inspectors evaluate the same 30 parts using the same standard and agree only 36.67% of the time, that is not something we'd shrug off. We'd investigate t

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

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

Instead of a slideshow at the AIAG Quality Summit booth, MAD-Ai is running its Quality Suite against real documents attendees bring in: a rejected supplier 8D, a stuck PPAP package, a PFMEA that hasn't been cross-walked against the control plan. This post explains why a generic demo only proves a tool can do impressive things, not whether it works on your standards, and points to Rick's Reasoning R&R™ session, Wednesday at 1:30PM in the Platinum Ballroom, as the framework beh

Give two quality engineers the same general AI assistant and the same 8D to review, and you won't reliably get the same depth of analysis or conclusion, and neither one used the tool wrong. This post explains why that's a chatbot's design, not a flaw, and how MAD-Ai starts with the standard instead of the prompt: structured inputs, required evidence fields, embedded rules, and escalation triggers that enforce the same rigor regardless of who's asking. The engineer still makes

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

Every "AI is X times faster" claim hides a real question: faster at what? This post breaks down why document translation runs about 60x faster and training plan development close to 90x, mostly mechanical assembly work, while problem solving and root cause analysis lands closer to 15x, because that work is genuinely open-ended and the engineer's judgment stays the last step. Speed varies by how much of each task is standardizable assembly versus real judgment. That's by desig

A Tier 1 automotive supplier quality manager describes what changed after MAD-Ai went into their 8D and PFMEA review process: work that used to take days now takes hours, and newer engineers have guidance that used to live only in a few people's heads. This post looks at why the real story isn't the speed but what happens to a department's newest hire once expert-level review is embedded in the workflow, plus the ROI: 10 to 15 times the speed of a manual process and $3 to $7

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

Manufacturing spent decades making sure a new hire could run a stamping press at the same rate and scrap rate as a senior operator. Grading an 8D or evaluating a corrective action never got that same discipline. This post makes the case for applying the standardization manufacturing already trusts for physical work to analytical quality work: grading 8Ds, cross-walking PFMEAs, and reviewing PPAP submissions to the same standard regardless of who's available. The engineer stil

In a 2019 study, three trained automotive inspectors evaluated the same 30 daytime running lights twice against a known standard, agreeing with each other only 36.67% of the time. This post explains why that's not a training failure but a measurement problem, and why manufacturing runs attribute agreement analysis on physical inspection but never on judgment work like grading an 8D. If unaided human judgment tops out near 37% agreement, that's the bar any AI grading quality w

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

The most preventable failure pattern in automotive quality: the corrective action closes, the PFMEA stays frozen, and the failure recurs. MAD-Ai automatically crosswalks completed 8Ds to the PFMEA and flags every failure mode that needs updating.

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