Traditional Advanced Planning Systems (APS) are unlikely to become true enterprise platforms because organizations remain functionally siloed, metrics are misaligned, and current APS architectures were designed for an environment that has fundamentally changed. Here I share how I think that APS systems can adapt to drive more value.
Remember the story of the frog in the pot of water? If the water temperature rises slowly, the frog doesn’t recognize the danger until it is too late. Put the frog directly into boiling water, and it jumps.
I think the analogy applies to today’s supply chains.
Companies are absorbing increasing volatility and complexity without fundamentally redefining how work gets done. Companies have confused historic practices with best practices. They are now taking those same processes and attempting to automate them with agents and agentic AI—without first stopping to ask a more fundamental question: Is it time to jump? Here I give a five-step action plan.
In 2007, the average company took six months to sense market shifts and adapt its supply chain. My estimate, based on work with clients, is that if a downturn happened today, the average company would take 20-30% more time to adjust than in 2007. Here I give insights on preparedness.
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.
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.
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.
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.
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.)
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.
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.
