An Interview with Edward Deming- On his Approach to Quality and Innovation Applying AI, AR, and ML

By Craig D. Nelson

Many of you reading this article may not know who Ed Deming was: ?He is the father of continuous quality improvement, his work in Japan after World War II, is largely responsible for the country's economic recovery and its rise as a global manufacturing powerhouse.? Made in Japan has become synonymous with quality-driven iconic companies like Honda, Toyota, Nissan, Sony, Toshiba, Fujitsu and many more.

So, if you don’t know who Ed Deming was, you should and all of us would be interested in hearing what Ed would have to say about digital transformation and the application of AI, AR, and ML to his 14 core principles of quality management.? In this article, I will attempt a hypothetical dialog with Ed Deming on the application of AI, AR, and ML to continuous quality improvement if we could interview him today.

Disclaimer: While Edward Deming did not live to see the full-blown era of AI, AR, ML, and modern digital technologies, we can make educated inferences about his likely perspective based on his fundamental principles.

Craig:? Ed thanks for taking the time to speak with us about your thoughts on the application of AI, AR, and ML in business in 2024.? Back in the 50s you espoused 14 principles for quality management, how do you think those core principles would be applied in today’s digital world, specifically in the context of AI, AR, and ML?

Ed: ?It is my pleasure to be here.? While I introduced 14 principles back in the 50s, I’d like to resize that number for the digital age. As I have considered digital transformation and my principles, I think five core principles are most applicable in today’s environment.

My first principle is to Think Systematically, let me emphasize that quality issues are most often systemic rather than individual.? Hence the analysis of data can be useful in evaluating a system.? Certainly, analytics in the context of AI, AR, and ML, can help business leaders focus on the entire system, including data quality, algorithm design, and implementation strategies, to ensure optimal outcomes.? That said, data analytics cannot take the place of people-fed operational intelligence.? So, temper your dependence on data scientists and your focus on analytical outcomes.? Listen to what people are telling you about system outcomes.

Perhaps you have heard of Plan-Do-Check-Act (PDCA) my Continuous Improvement cycle which is the second cornerstone of my philosophy.? Quality cannot be achieved through inspection, and it is not an after-the-fact assessment of a system’s outcomes.? I advocate for the continual refinement of AI, AR, and ML models to improve accuracy and efficiency, at the point of need in every step of process performance.? Putting AR-enabled tools in the hands of people at the point of performance enables workers to complete tasks correctly the first time and to verify their work.? Inspection methods are changed with the use of AI, AR, and ML-captured data.? The inspector on the floor is no longer needed when task performance is digitally captured and validated at the point of need inside the system.? This impacts pride and worker behavior as everyone is now accountable for quality.

You can tell that I am an advocate for people.? My third principle is Respect for People. ?I believe that people are the most important asset in any organization.? I cannot emphasize the human element in AI development and deployment, ensuring that people are empowered to use these technologies effectively and ethically and that is all I have to say about respecting people.?

I am a strong proponent of AI as a tool for Eliminating Variability in a system.? This is my fourth principle and in the context of AI, this requires developing robust algorithms that can handle a wide range of inputs and produce consistent outputs.? Artificial Intelligence (AI) can significantly reduce variability in systems by analyzing vast amounts of data, to identify patterns in a system, and detect signs of wear and tear or impending failures. This enables preventive maintenance, reducing unplanned downtime and ensuring consistent product quality. ? I believe that systems can be stabilized by analyzing historical data and current process parameters, with the help of AI to identify the root causes of quality issues.

My fifth and final principle is Long-term Commitment.? It seems to me that today changes in technology are moving much faster than back in the 50s.? The constant barrage of disruptive technologies and the hype that seems to be prevalent in your society today is concerning to me.? I stress and urge every business leader to subscribe to long-term commitments.? The importance of long-term thinking over short-term gains is more important in today’s business environment than it has ever been. I caution against treating AI, AR, and ML as quick fixes and instead advocate for a sustained commitment to their development and integration.

A Hypothetical Quote from Deming

"In your digital age, AI, AR, and ML offer unprecedented opportunities for improvement. However, these technologies must be harnessed with a deep understanding of systems thinking, a commitment to continuous improvement, and a profound respect for people. By focusing on the long-term, eliminating variation, and empowering our workforce, we can leverage these tools to create a future of greater quality, efficiency, and human potential."

Craig: Ed thank you for your candid views of AI, ML, and AR.? I would conclude that you advocate the application of these technologies to help organizations implement your principles and achieve higher levels of quality. I believe that by leveraging these technologies, companies can create a culture of continuous improvement, drive out fear, and deliver exceptional products and services to their customers.

Ed:? Thank you for bringing me back to discuss these important ideas.

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Craig Nelson oversees Professional and POV services for CareAR, an AI/AR/ML solutions software company.? In his role, he is responsible for enabling customers to achieve the operating results envisioned by deploying CareAR solutions enterprise-wide.? Mr. Nelson is a thought leader in AR/AI/ML, process optimization, and operational transformation and is recognized for his applied AR/AI/ML deployments in leading organizations that have embraced disruptive digital transformation technologies.