Reflection: Advice for Supply Chain Technology Leaders Making Today’s Technology Pivot

September 20, 2023
6:46 pm

Based on the calls I am doing with the line of business buyer; I believe the buyer of supply chain planning is confused. Not just a little confused, but more so than I have seen in my forty years as an analyst. One of the reasons is technology inflection. The second reason is the discontinued software maintenance support on software from Blue Yonder, Oracle, and SAP. The third issue is the number of failed implementations. The fourth, and an opportunity, is the excitement around the generative Artificial Intelligence (AI) hype cycle.

In addition, supply chain planners are retiring. Yesterday, fifteen people in my LinkedIn network left. Each announcement makes me sad. The passing of the generational torch is real.

I am more excited by the Web 2.0 technologies of Graph, NoSQL Unified Data Models, Machine Learning, Unstructured Text Mining/Pattern Recognition, and Narrow AI than Generative AI, but I find a low level of understanding by the average buyer on the promise of these platform elements. The buyer is very confused and struggles to speak the language of supply chain planning. Most RFPs in the market are trainwrecks. The discussions are in the weeds, and few see the need to see the sky. Many are the wrong discussions. So what should you do? In my upcoming strategy days, I recommend seven steps:

Step 1. Shift From Inputs and Outputs to Drive Outcomes. Most technology discussions are focused on feature-function diatribes versus driving business outcomes. Now is a great time to shift your programs to improve value. Redefine work. Why don’t we build self-learning supply chains to enable business leaders self-service to gain their insights and drive peer collaboration? We have self-service HR systems. Why not self-service demand and supply planning? Organizations have grown the number of planners exponentially without driving improved value. Redefine the planner’s role to be an orchestrator and move decision support to be a self-service application by business leaders.
Step 2. Measure and Drive Learning Systems. Measure. Learn. Adapt. Just don’t say Net Zero measure the impact. Don’t talk about the bullwhip effect. Instead, measure and harness it. Redefine demand management by measuring Forecast Value Added (FVA), demand latency, bias, and error. Improve manufacturing by measuring schedule adherence and first-pass yield. Build a scorecard for executives to enable visibility of plan effectiveness and recommendations for future improvement. Drive learning systems to improve value through measurement.
Step 3. Design Using a Balanced Scorecard. Only 9% of supply chains are designed, and network design technologies focus primarily on asset utilization. Shift the use of design technologies and what-if analysis platforms to manage supply chain flows. Each supply chain has four to five flows. Each needs to be managed. Provide visualization and learning systems to improve decision support.
Step 4. Shift from Feature/Function Focus to Value. Most company demonstrations start and end with feature function. (These discussions are so boring. I want to engage technologists on how technology can improve work. Move past feature function in today’s processes to unleash new value. Don’t paint in the box of existing technology taxonomies. Instead, observe and push for technology to harness new value through learning systems.
Step 5. Shift from Transactional Views to Flow. Harness and Visualize Flow. Transactional paradigms are shifting to flow. While order-to-cash and procure-to-pay are clearly transactional solutions, decision-support processes are moving to flow. Visualize and manage demand and supply flows and improve reliability.
Step 6. Create Safe Zone Areas for Business Leaders and Technologists to Think Deeply and Solve Problems. Build open networks to enable innovation between technologists and business leaders. Ask innovators to send you their biggest hairiest problems through open design networks. Side-step the RFP process.
Step 7. Use Generative AI for Content Development. Shipping instructions. User guides. Training materials. These are all good use cases for generative AI. I don’t think we are ready to use generative AI as an engine in supply chain planning. The reason? The organization is not aligned on the definition of supply chain excellence. Alignment issues grew three-fold over the last decade with a rise in politics in large organizations.

The goal is to build methodologies to drive success. Recently, I audited a large technology company’s multiple implementations. The results were dismal. Analyze the quality of data before starting, and test engines. Ensure plan feasibility and train the team. Analyze the effectiveness of the software post implementation by remote views of the balanced scorecard and learning systems.

Test to ensure that the planning process is driving better outcomes. Put the data on the desk of the Chief Supply Chain Officer, the Chief Operating Officer and the Chief Financial Officer’s desk and help drive awareness and organizational alignment.

I hope this helps. What am I missing? What would you do?

Warning: Sidestep the Narrative of the Misguided Goldiggers

August 10, 2026 4:58 pm

Ten years ago, I started writing a book titled Stories of the Misguided Gold Diggers. The book was a collection of stories from two decades of stories of technology leaders perpetuating the myth of integrated end-to-end supply chain planning.

I dusted off the manuscript on Saturday. I think that we have a new chapter. Companies focused on putting Artificial Intelligence (AI) on top of existing architectures are putting AI Stupid on steroids.

The Myth of End-to-End Planning

July 31, 2026 9:14 pm

Supply chain planning, supply management, supply chain execution, network design, and transportation/logistics management operate in silos. Not much has changed over four decades. The connections flow back through transactional systems: order-to-cash and procure-to-pay. There is a myth that companies can buy an end-to-end supply chain management solution. This is largely a myth. Here we explain.

Lead Time: A Broken Gossamer

July 16, 2026 6:38 pm

If you are struggling with supply chain planning, dancing in the light of shiny objects, and scratching your head, please read on. My goal is to help you.

Please do not AI Stupid. What do I mean? AI Stupid is putting agents and agentics on top of existing architectures believing that making them faster and hands free add value. To me, this is fools play.

I love AI. I am excited about new technologies. To this end, I want to shine a light on how new technologies can help address the black holes and inconsistencies in today’s supply chain, which largely stem from the limitations of the first generation of supply chain planning and execution technologies. In this blog, I give you three places to start.

Do You Need a Supply Chain Coach?

July 9, 2026 5:27 pm

Supply chain is where the rubber hits the road. For a public company, over 40% of market capitalization is tied to the trade-offs between growth, operating margin, inventory management, and Return on Capital Employed.

The road for supply chain improvement is fraught with issues. Here we share some and offer some advice.

Is your Supply Chain AI Ready?

June 11, 2026 2:39 pm

A simple quiz to assess an organization’s AI readiness.

The pace of change is fast and furious. Every day, technology advances faster than we can digest. A great challenge to have.

Determining whether a supply chain is “AI-ready” is less about technology and more about the gray matter between the ears of supply chain leaders. Leadership, alignment, and clarity of goals matter.

Too few companies are clear on the definition of supply chain excellence. Measuring and rewarding functional metrics reduces the firm’s value. Putting agentics on top of today’s processes can make bad practices run faster, reducing value.

The toughest job for the supply chain leader is challenging existing supply chain paradigms that were defined by the limitations of decades of supply chain technologies. As the curtain lifts on the potential of new forms of technology, process redefinition is our opportunity, but only if we are clear on what drives value. (Here, I link to the Supply Chains to Admire reports to help you define value. The next report will be published on June 23rd, along with my Dynamic Benchmarking Product, to help you define value in the face of your AI readiness. More information about the launch is at the bottom of this blog.)

Case Study: A Scrappy Demand Management Approach

June 9, 2026 5:37 pm

This study of Franklin Sports shines a light on the work that needs to be done at the sales account level to challenge a retail forecast, and also highlights the importance of a new technique for a forecast engine — reinforcement learning.

Artificial intelligence comes in many forms — large language models, generative AI, machine learning, unstructured text mining, deep learning, neural networks, reinforcement learning, agents, and agentics. While the industry is wigging out about agentics, I think reinforcement learning is a great step forward in the journey of Artificial Intelligence.

Supply Chain Shaman