The Big Supply Chain Analytics Failure

November 3, 2021
3:04 pm

New forms of analytics abound. Embracing them is a different story. Data abounds while insights are low. Supply chain leaders struggle with the adoption and use of new forms of analytics. Only one in two are satisfied with their success in using analytics. Here we postulate why based on recent research.

Reason #1. Knowledge

While organizations hired data scientists in the past decade, business leaders struggle to speak the language of new forms of analytics. Terms like ontology, sentiment analysis, python and nosql are unknown to 80% of business leaders. Companies lock their data into traditional relational database structures and limit their possibilities to use disparate data or understand semantics.

What to do? Spend time educating teams based on lunch and learn and open sharing between technologists and operating groups. Do not make the mistake of asking traditional consultants and technologists for insights. (They want to sell you yesterday’s solutions.) Elevate the discussions.

Reason #2. Lack of a Good Framework for Process Innovation

Companies are investing in traditional applications like supply chain planning, but the focus is usually on improving the “engines” or optimization on top of existing systems. Few are questioning the fact that the workflows and architectures are largely outdated.

While companies speak of digital transformation most are working on the digitization of existing processes. The focus is faster and hands free. Companies struggle to rethink the use of technology based on the Art of the Possible to drive process innovation

What to do? Change the dialogue. Don’t focus on making current practices faster. Push for process innovation.

Reason #3. Lack of Awareness of the Need to Unlearn

Change management is fraught with issues. Few feel that they do it well. One of the unspoken challenges is the need to unlearn or ask participants to free themselves from conventional thinking to rethink the possibilities.

What to do? Focus on helping your group to ask the right questions. Start with a focus on supply chain failure. Ask the group to brainstorm on the data available in the organization that can inform and drive new outcomes. Explore the possibilities of unstructured and streaming data.

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