Losing Focus on Serving the Global Multinational

October 29, 2024
3:46 am

Supply chain leaders readily agree that industry differences exist in planning. Similarly, debate rages on the differences between regions. Industry differences trump regional definitions, but there is little recognition of the supply chain planning requirements by company size in the discussions. When it comes to defining solution success, mapping the requirements based on company size is more important than industry or region.

Today’s planning solutions serve small regional teams well, but the answer for the global multinational manufacturer requires bespoke wiring of solutions from multiple technology solutions. There is no good answer.

Figure 1. Analysis of Quantitative Surveys From the Last Decade

Getting To An Answer

The first step is to recognize the gap. In my interviews with global multinationals, I hear the need for an architecture that scales for hundreds and thousands of users. There is a need for discovery and what-if analysis, enabling insights not held hostage by laborious, time-consuming processes to run multiple batch jobs. Companies have not been successful in building planning solutions themselves. At the same time, academics focus on building better mousetraps (improving models and engines) in existing taxonomies versus redesigning work to ensure the security of the system of record and proactivity in systems of insights.

Figure 2. Rethinking Planning

As we think about the promise of new forms of data analytics, the talk of “AI” without definition drives me crazy. There are so many techniques under the “AI” umbrella. The tragedy, I find, is when technologists slap the term “AI” on their marketing without sharing the specifics of how specific tactics drive outcomes. AI for AI sakes drives nothing other than the hype cycle. Here are some application possibilities:

Architectural Element	Technique
Planning Master Data	Pattern Recognition and Machine Learning
Channel Sensing	Large language models, Sentiment mining, Machine learning, and Graphical flow modeling
Supply Sensing	Large language models, Retrieval Augmented Generation (RAG), Vector DB, Machine Learning, Pattern Recognition
Rules and Policy	Rules-based Ontological Frameworks
System of Record	NoSQL No/Low Code, Unified Data Model
System of Insights	Agent-based Optimization, Probabilistic Demand Translation, Cognitive Learning Models
Systems of Discovery	Digital Twin Simulation and Optimization
Visualization	Workflow Automation and Role-based Views

Wrap-up

I look forward to hearing your thoughts. Why do you think we missed the mark in serving large, global multinational corporations?

And how do we stop the false AI rhetoric that has the industry spinning its wheels instead of addressing possibilities?

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