Today marks the fourteenth anniversary of Supply Chain Insights.

Let me start this blog with a heartfelt thanks to all who have taken the time to complete my surveys, participate in my open training, attend my events, and join my network calls. More than 150 readers sent personal notes via LinkedIn on this anniversary.

I never wanted to start my own company. Being a small business owner is a hassle. One that I will willingly relinquish.

History

Over the last 14 years, I have written 605 blog posts, 13 books, hosted five share groups, and penned more than 100 reports. The posts are based on interviews, qualitative and quantitative benchmarking, client work, case studies, and correlations between a company’s choices and its public corporate reporting. I pride myself on speaking truth to power and advocating for the business innovator who is trying to drive a difference in outcomes.

But, I Failed

Recently, Jeff Metersky, now at Gains Systems, wrote:

“Some industry analysts, pundits, and gurus have been making largely the same observations for years. Similar diagnoses. Similar recommendations. New terminology, some times.
And yet, many of our underlying challenges remain. That raises a fair question. If the guidance is sound, why does so little change at scale? And if there is success from their recommendations and guidance where is it communicated?

I do not begrudge anyone for making a living, and some advice is genuinely useful. Yet, too often the true understanding of available solutions is stale, with limited effort to validate current capabilities or outcomes beyond what vendors claim. So when the same conclusions surface year after year, it is reasonable to ask whether we are learning anything new from this group.

I am closer to nearing the end of a 40-year career in this space. I have had the privilege of helping move the needle inside companies, and for my customers and clients, not just commenting on them.
If everything is truly as broken as it is often described, perhaps more voices should move beyond observation and into execution. Build something. Put capital at risk. Test the ideas in the real world.
Because progress is not proven by commentary. It is proven by outcomes. I hope this is read in the spirit it was intended. To find meaningful ways to make tangible actual progress. Please share your thoughts. Open constructive discussion is good.”

I read the post and winced. Yes, I took it personally. I tried, and yes, Jeff is right. I failed. I, and every other influencer, failed. However, I would add, so have technologists and consultants.

My Backstory

I left the world of software twenty years ago, and I don’t personally write code, but I have collaborated with Elemica/BASF and Evonik to test blockchain, and Kinaxis/OMP, and o9 to test outside-in planning processes. (The most significant adoption of this work is the current definition of o9 Solutions demand planning.) I have written about the testing openly and shared the results through this blog and reports.

I am dissapointed that more of the pilot work stalled and was not adopted.

I wrote the Supply Chains to Admire and collaborated with Georgia Tech to build regression models on value. I am dissapointed that this work never found its way into academic journals.

I shared the learning openly through monthly networking calls, sharing of the testing data through open content research, and focused presentations at events, but I agree, I did not make a difference.

So, let’s answer Jeff’s question of how do we make actual progress? I have thought long and hard on this topic. I accept the challenge. I think that it centers on education, value-based content creation, and first-principle thinking. Today, I am going to focus on education and first-principle thinking.

Educate the Buyer to ask Tough Questions

Currently, the market is wrangling through the hype cycle of Artifical Intelligence (AI) everywhere, but meaningful AI case studies are nowhere. In my effort to educate supply chain leaders, I volunteer to speak.

At the end of May, I am speaking to the ASCM Rochester Chapter event on the future of AI and the redefinition of work. My goal is to help.

In the prep, the head of the chapter shared that prior to my speech, the chapter is hosting a speaker on AI. So, I asked a simple question, “How does the prior speaker define AI?” He could not answer the question. So, I probed, “Will he share case studies of the use of Large Language Models (LLMs), machine learning with deep or reinforcement learning, or agent-based workflow or Agentic governance?”

He stammered, “I don’t know.” An uncomfortable silence filled the space.

I often find this to be the case. We are speaking about AI without clear definitions, applicability to use cases, and the right fit of technology. But, more importantly, without a focus on first-principle thinking. Technology for technology sake is a losing proposition. The autonomous supply chain is a fairy tale.

This is all happening as AI applications like ChatGPT and Microsoft CoPilot are radically changing our personal lives. Grammarly has improved my writing and the AI applications in Shutterstock yield impressive images.

However, I firmly believe that we are stalled when it comes to improving supply chain processes.

Just as digital technologies radically improved our personal lives, digital transformation drove little improvement in the core processes of supply chain planning. Here are some questions that I would ask the technologists that show up with shiny object syndrome of AI everywhere, but nowhere. Fight back when the promise is localized efficiency improvements on outdated processes.

So, what to do?

Ask technologists questions on AI to have more honest discussions:

• Why do you think having the dots makes you capable of connecting the dots?
• How do you define AI, and for you, what defines supply chain excellence?
• What supply chain first principles does your software support?
• If they cannot answer, reframe the question as, What makes you believe you can deliver truth when you cannot even name the first principles of truth?
• Why do you think the Internet is the source of trustworthy data? How do you trust, but verify?
• Why do you believe the regurgitation of history breaks the norms to define unprecedented foresight?
• Why do you call your AI artificial when it purports to tell the truth?
• How does your AI solution provide emotional intelligence, and sentient comprehension?

Next week, mis-guided technologists will share their visions on continuous and real-time planning and autonomous work systems at Manifest, but the audience will ignore the over-arching problem. The issue? Companies are not clear on what defines excellence. How can we automate without clarity?

The journey for supply chain excellence has to start with clear definitions. Excellence is not rooted in the efficient delivery of functional metrics. A focus on functional metrics throws the supply chain out of balance and increases waste.

Which leads me to a question Jeff, “Is our current failure a platform for future success?”

Need for First-Principle Thinking

First-principle thinking reverse-engineers complicated problems and unleashes creative possibilities. The approach breaks down complex problems into basic elements and then reassembles the components from the ground up. Having knowledgable team members with deep systems and design thinking, ignites meaningful discussions. People that think they know the answers drag down innovation.

Elon Musk’s First Principle Reasoning Framework

• What is the problem?
• What do we know to be true?
• What are the obstacles?
• What can I do differently?

So, I asked ChatGPT for the first principles of effective supply chain planning. Here I share a bulleted list from the query:

• Demand drives everything → Pull beats push.
• Flow beats efficiency → OEE ≠ throughput.
• Variability is the enemy → Stability before speed.
• Time is the real cost → Lead time hides waste.
• Inventory is a buffer, not a solution → WIP hides problems.
• Constraints govern the system → The bottleneck rules.
• Information moves faster than product → Signals > schedules.
• Trade-offs are unavoidable → Choose your operating point.
• Reliability beats optimization → Predictable wins.
• People and incentives shape outcomes → Metrics drive behavior.

What do you believe are the first principles of supply chain planning? Are you aligned as a group before you evaluate technology?

As you consider the topic, ask yourself: why do we have supply chain planning systems that prioritize push-based flow to maximize Overall Equipment Effectiveness (OEE) and Efficiency, yet provide little insight into managing lead times and cycles? I think the answer lies in the first principle: people and incentives shape outcomes. Companies do not understand the value proposition of waste and cost resulting from misalignment with first principles.

As supply chain planning evolved, flows between functions were not automated, and processes/bonuses were introduced to optimize functional performance, often at the expense of the balance sheet. Technology was not sufficient to model the supply chain as a complex, non-linear system, so we did the best we could. Unfortunately, we have never returned to address the problems of limited, expensive memory and lengthy batch processing in the early definition of supply chain planning technologies.

Ironically, today, the platform definitions are similar, but faster. Few question the basics and try to align with first principles.

Demonstrating these first principles to traditional leaders in finance, information technology, and manufacturing requires modeling that most supply chain leaders lack the capability to build, but it can be built with consultants and newer technologies like Lyric and Optilogic.

So, Why Are We Failing?

Jeff, I think that the answer lies in the fact that technologists are talking about technology for technology’s sake, while business leaders are rooted in traditional process thinking. Both need to change. The momentum and force for traditional processes are just too strong. No independent voice can lift us past this cacophony.

Technologists are biased toward selling software, and their views shape information feeds and events. We need to side-step this tradition.

To evolve, both groups have to step away from hype cycles and shiny-object syndrome to focus on first principles. This evolution takes a village. Hopefully, you will be willing to help.

Note: Some of the questions about what to ask a technologist about AI were shared with permission from Georges van Hoegaerden, georges@ivanhoeinstitute.com blog (georges@ivanhoeinstitute.com).