The “it” that needs to be in IT

October 11, 2011
10:43 pm

In the 1990s, it was all about transactions. In the early 2000s, it was about catalogs and shopping carts. Later as teams tried to integrate the end-to-end supply chain, the focus was document management. Today, with the introduction of F8 by Facebook it is about effective story telling and harnessing the Open Graph. What is the “it”? It is the ability to reach the customer: the heart of the supply chain. The story is the same, but reversed in supplier interaction. I firmly believe that the evolution of different data types can unlock new insights for supply chain leaders.
Today’s supply chain needs to be able to sense and respond across ALL of these data types. For supply chain management it is the “it” in IT (information technology). Yes, we have entered the era of BIG DATA supply chains. It is a world where the volume, type and the velocity of data has exploded. While companies have successfully scaled to handle transactional volumes, the other data types mentioned above combined with enrichment data –content, weather, location, maps, voice, video, digital images–can enable new capabilities to sense and respond more intelligently. Let me give you three examples:

Why is my Customer not Buying my Product? Or Buying a lot of my Product? Point of Sale (POS) data can be successfully harmonized and integrated into Demand Signal Repositories (DSR) to answer the question of what sold when. However, it cannot answer the question of “why”. Only through the integration of structured data with unstructured text –ratings and reviews, blogs, social sharing and and listening posts– can a company answer the “why”. The new work by IBM on the Netezza appliance and the work by Teradata on Asterdata signals a technology shift for the DSR market.
What did my Customer really Want? In extended supply chains, product flows through distributor relationships. Frequently what is sold is not what the customer REALLY wanted. This is especially true in automotive supply chains where the color of the car is determined in the first step of production. Again, semi-structured data is the answer. The combination of click through data on the web (how many times did a buyer request a quote of a certain car color) combined with social news feeds and distributor data can give insight on what the customer really wanted. Today, we only know what was bought.
When is the Supply Chain at Risk? To improve buying power, companies have aggregated procurement into commodity buying groups. This consolidation in large, global companies (greater than 1 billion) is a maze of supplier relationships. The use of unstructured text mining on suppliers can signal early warning of potential risks. The use of risk sensing alerts when the supply chain is at risk, but must be combined with transactional data (e.g purchase orders, quality data, shipments, receipts) to answer the magnitude of the risk. Supplier risk sensing was pioneered by Open Ratings, now a Dun & Bradstreet company.

The case studies abound. It is exciting. We can answer old questions in new ways. And, yes, I think that we can teach old dogs –like me– new tricks.
What are your favorite use cases for Big Data Supply Chains? I welcome your thoughts.

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.

Can We Side-Step the AI Spin Cycle?

June 7, 2026 3:44 pm

When it comes to combining tech, 1+1+1 should equal more than 1. The impact should be exponential. Unfortunately, today, the answer is 0.

What do I mean? Let me explain.

I find that the supply chain technology market moves slowly along traditional technology lines. Conferences are usually focused on the use of technology, not on redefining work. This bothers me. I want it to bother you as well.

Here I share some insights to drive change.

Supply Chain Shaman