Category: Big data supply chains

analytics

Is your Supply Chain AI Ready?

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.)

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analytics

Case Study: A Scrappy Demand Management Approach

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.

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Big data supply chains

The Sad Demise of the Food Industry

For the period of 2016-2025, the industry average was 11% operating margin and 7.82 inventory turns, with a 35% decline in industry inventory performance. Few companies were aware of or adapted to the shift in industry potential.
Today, the shifts are faster as consumers trade down to cheaper brands and retail private label gains market share. Major inflationary spikes in protein, especially beef and eggs, due to supply shortages and disease-related disruptions in 2025, continue the never-ending ride in commodity volatility. Yet, companies are insular to adapt their supply chain practices. Putting AI on top of traditional supply chain processes is a recipe for disaster.

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analytics

Meet the New Dr. No.

The buyer today for supply chain planning is more conservative. The leaders — Chief Supply Chain Officers —are hardened and conservative, with many becoming “Dr. Nos” during sales cycles while pushing traditional definitions of technology.

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analytics

Move Your Own Mountain

The journey of AI automation is a path of carrying small stones starting with the redefinition of architectures with a focus on semantics.

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Big data supply chains

The Beat Goes On

A reflection of how we need to unlearn to rethink supply chain planning processes.

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analytics

The Orchestration Shuffle

As approaches in Artificial Intelligence mature, we have the opportunity to orchestrate the supply chain response. Accomplishing this goal, requires the rethinking of work holistically.

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Big data supply chains

Mistakes and Opportunities

Discussion of the barriers in moving from traditional planning platforms to build new processes on native-AI supply chain planning platforms.

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