Welcome in the Big Data Opportunity

July 9, 2013
5:35 pm

The term big data is moving up the charts as a “hot topic.” This week, we published our second quantitative study on the evolution of big data concepts, it gave us the opportunity to talk to supply chain leaders on the evolution of technologies and the use of analytics in the Race for Supply Chain 2020. We wanted to understand where companies are at in the adoption of big data concepts and we had a good time writing the report on the research.
The study was completed by over 120 respondents in the period of June-July, 2013 and the complete study can be accessed on the Supply Chain Insights website or through slideshare.

What Did We Learn?

We believe that the adoption of new concepts for big data is a step change for supply chain teams. It is not about force-fitting new forms of data into applications based on relational databases. Is cannot be treated as an evolution.
It requires change management. It is about small and iterative projects using new forms of analytics. The projects have to be based on a business problem and the focus needs to be on continuous learning. This is quite different from the traditional waterfall project approach of mapping “as is” and “to be” states and managing a large project against a goal. Companies have to be open to the outcome and invest in innovation through analytics.
To drive success companies have to sidestep the hype. While the powerpoints on big data concepts abound, very few technology companies and consulting partners have built solutions to harness this opportunity, and even fewer supply chain leaders are ready to have the discussion. We find the work by Aster Data (now a division of Teradata) and the work by IBM and Enterra Solutions on cognitive learning engines to be promising. We are also encouraged by SAS’s work on unstructured text mining, Bazaarvoice on the translaltion of blog and sentiment data, and the work by APT on test-and-learn strategies is exciting. We are also encouraged by the work on cold chain and serialization by a number of consultants working on sensor data and counterfeiting.

The Findings

In the study, big data was defined as volume that is at least a petabyte, working with a variety of data that goes beyond traditional structured data, and building processes that are based on an increased velocity of data that is associated with real-time flows. In the results, we see several trends:
-The biggest opportunity is not with the volume or velocity of data. Instead, it is with the management of opportunity associated with new forms of data. In the study, 76% of companies see big data as an opportunity and 12% see it as a big data problem.
-Databases are growing, but they can be managed. 15% of companies have a database today that is at least at a petabyte. The largest databases are not Enterprise Resource Planning (ERP). Instead they are in the areas of product management or channel data.
-The work is starting: 28% of companies have a Big Data Initiative today with 37% planning to implement a big data team.
-Companies that have worked on a data-driven culture have a leg-up. Organizations that have active teams on Master Data Management are more likely to have a big data cross-functional team. 54% of companies with Big Data initiatives believe that big data techniques help with MDM.
In the words of a supply chain leader, the path forward requires a disruption. It is for this reason that we have asked experts from the Department of Defense to join us to discuss these findings on our webinar this Thursday at 1:00 PM EDT. We hope to see you there.
We will also be continuing the discussion at our Supply Chain Insights Global Summit in Scottsdale, AZ on September 11th and 12th. There will be several panel discussions on the future of analytics, the evolution of new technologies for healthcare, and the evolution of digital manufacturing with real-time data. The event will sell out at 150 participants and the cutoff date for the discounted room block at the Phoenician is August 8th. So register soon to reserve your seat. (The event is now sold-out for technology providers.)

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 Health Check: The Power of an Orbit Chart

May 27, 2026 9:49 pm

An orbit chart is a powerful tool for understanding the “health” of a supply chain and its potential for improvement. The supply chain is a complex, non-linear system with limited trade-offs. The relationship between trade-offs varies by industry, region, and size. The orbit chart is a diagnostic we use in the Supply Chains to Admire work. Here I explain the use case.

Supply Chain Shaman