My work in manufacturing and supply chain over the past 15 years has provided me with a practical education in the benefits — and limitations — of using artificial intelligence (AI) and machine learning (ML) technologies to help solve business problems. Not long after earning a degree in mechanical and aerospace engineering, I became a management consultant, focusing primarily on improving supply chain and manufacturing operations at both the networkwide and shop floor levels. Before becoming vice president of product management for, I also spent years building expertise in software and AI/ML SaaS application development.  

From my time spent on the shop floor and with plant management, I understand what’s on your mind as a quality management professional. I know what types of perplexing questions you’re seeking answers for as you try to get at the root of yield and defective production issues that just won’t go away.  

I know the inability to solve the toughest issues is frustrating. It also leads to a lot of unnecessary waste. You and your team aren’t to blame here, of course — you’re constrained by the limitations of the tools you currently use for quality management. Even with modern quality management systems (QMS), improved physics models and theoretical understanding, and support from the most seasoned engineers, manufacturers still grapple with major (and often baffling) issues with yield and defects. 

AI is the key to surfacing precisely what is causing your production snafus, and where and when. However, you also need the right solution — one made for the manufacturing industry — to let you not only harness the power of AI for everyday use in your operations but also evolve to a state of “predictive quality.” That solution is Quality Flow from Let me explain why. 


Addressing low-hanging fruit is great, but what about all those hidden issues? 

Sometimes it seems like the stars must be aligned for your plant to consistently deliver high-quality products and hit target yields. Modern manufacturing operations are extremely complex and dynamic. And meanwhile, the context your assets are working in are constantly changing — ambient conditions, raw material variability, production crew differences, and on and on.   The interdependencies that exist among control inputs and across production stages can be staggering.  

Meanwhile, against this backdrop of … well … chaos, you and your team are often trying to play whack-a-mole with quality issues by analyzing data manually, and often reactively, one project at a time. The deployment of Six Sigma, Lean, and SPC techniques are extremely useful for helping you identify the causes of errors and reduce variance in your manufacturing processes. But often, they might only eliminate the low-hanging fruit, leaving many more issues hidden and unsolved higher up in the tree. 

That brings us back to tools, and their limitations. We often think of quality analysis tools and methods in manufacturing as lying on a spectrum that goes from “basic” to “advanced” in terms of their ability to provide answers to help you solve, and ideally, avoid, yield and defective production issues: 



QMS are superb tools for descriptive analytics (What happened?). You can log defects and quickly develop a sense of what’s going on. Getting to causation for the more difficult cases, however, can be far more difficult, often relying on manual methods to conduct diagnostic analytics (Why did it happen?). Sometimes, the root cause of a problem is obvious, but often, it’s not.  And creating true predictions and recommendations is typically beyond the scope of the capabilities of most industrial quality organizations. 


Where AI fits in 

So can AI take you to the next level?  Yes.  But, be careful.  AI technology has been hyped for years.  How many times over the years have you heard that AI is going to solve all your problems? And how many times has it come up short? Too many to count, I’m sure. 

 Most “AI” solutions are toolkits or platforms.  You still have to do the manual work – frame the problem, figure out what data you need, find and check all the data, map it all together, build a model . . . what model?  More than one model?  And then what?  You’re doing it from scratch, so what are the odds you’re going to get a good result?  How do you even know you got a good result?  And when it doesn’t turn out, who’s to blame? 

And further, do you have the bandwidth for that? 

Software companies love building platforms for other people to use.  If things go sideways, it’s your fault, not theirs. 

We don’t think that’s right.  AI is hard as it is.  Most AI projects fail because the output doesn’t meet the needs of the business.  In the case of manufacturing quality, your experts need accuracy, transparency, and proof in order to take action. 

That’s why we created our Quality Flow application. It isn’t a platform or a toolkit. It’s a prebuilt solution. We give Quality Flow data, and it gives you actionable answers. 

Quality Flow is powered by’s Enterprise AI® engines. The app works in conjunction with your QMS, and pulls in data from all over your plant. 

 Quality Flow understands context, and it provides you with answers and recommendations for the most difficult questions, like: 

  • Sometimes defect X shows up seemingly randomly, no matter how we look at it. Is there any pattern we can’t see?
  • We have a consistent level of defect X and can’t seem to get it any lower. We’ve been studying it for years and have some ideas. Could AI help us understand what may be missing?
  • Is it possible to predict when the chance of a defect has increased?
  • What should we tell the operators to do if something happened upstream that caused an increased chance of defects?”

Quality Flow can help you answer these questions — and many more. So, if you’re struggling with persistent defects that you and your team can’t solve, or if your yield or scrap seem to be stuck no matter how you try to improve it,’s Quality Flow application may be the app for you. 


Learn more about Quality Flow 

See this page to find out more about our Quality Flow quality management app, which has been proven in production to help solve previously “unsolvable” issues in today’s highly complex and dynamic manufacturing environments and reduce the Total Cost of Quality (TOQ) by up to 35%. 

Also, stay tuned for the upcoming release of our Quality Flow “cookbook,” a guide to common use cases and business questions that we know many quality management leaders today are eager to address, along with specific steps for how you can do it using Quality Flow.