The 2022 Gartner Hype Cycle for Supply Chain Planning Technologies stressed the significance of AI as a game-changing trend in supply chain planning. AI was projected to become a mainstream technology in supply chain planning by 2025. Navigating the Xpo at the Gartner Supply Chain Symposium this week provided much evidence that this will end up a reality. AI was referenced across most technology vendor booths and consistently mentioned in presentations. 

However, the enthusiastic response to’s use of AI stood out above the crowd. My colleague Jeff Alpert’s session, “Probabilistic Planning: Making ‘Better Bets’ in your Supply Chain with AI,” was standing room only and attended by over 300 people. As our team engaged in conversations following the presentation at our booth, I overheard one attendee say, “You’re the only AI that makes any sense.”  

Every attendee I engaged appreciated the level of detail in which we explained how to apply AI to solve specific supply chain problems. Here are three observations from the conference that are helpful as you explore new possibilities with AI. 

1. The Problem You Are Solving 

Get specific about your problem and how AI is uniquely suited to contribute to the solution. Test the vendor you are speaking to about the details: what is their application of AI to your specific supply chain problem, and what level of supply chain subject matter expertise do they possess? 

The problem is solving is the fatal flaw of current Advanced Planning Systems: deterministic math, fixed inputs, and simplified assumptions that fuel never-ending firefighting inside the near-term execution window. As a result, you make bad bets every day based on a “theoretical” plan that doesn’t understand variability or probabilities. The future of supply chain planning will be incorporating the power of probabilities into your decision-making processes. We call this evolution “Probabilistic Planning.” Gartner presented many of the same concepts when using the term Supply Chain Resilience. 

We have tuned machine learning models to understand the variability across time, duration, and quantity in your supply chain. With an understanding of probability curves, predicts demand and supply for a more accurate representation of any supply chain imbalances. We’ve established “Value @ Risk” as an objective way to do a health check on the financial implications of the difference between the “theoretical plan” your legacy systems are producing and what’s “most likely” to occur. Finally, we utilize Generative AI, Graphical Neural Networks, and Reinforcement learning to “generate” a supply chain plan that best mitigates your risk.  

If you missed our Gartner Supply Chain Symposium presentation on these concepts, click this link to watch the presentation.

2. Techniques, Process, Platform: 

Once you have defined a specific supply chain problem you are looking to solve, be clear about the scope of the partnership you are looking for. For example, are you looking for an AI forecasting model, an AI toolkit your own Data Scientists can work with, or are you looking for a SaaS platform with purpose-built AI supply chain applications? 

The pace of innovation within the AI space is accelerating at a breakneck pace. However, many underlying techniques are open-sourced, well-documented, and available to any company with the data science talent to take advantage of them (including your own company). Therefore, we believe it is not the use of AI techniques, having an “AI forecasting model,” or the availability of data scientists that will produce value in your supply chain. 

Combining techniques and applying them to a specific business problem has unlocked value for our customers. has spent years combining and testing AI techniques to solve the fatal flaw of legacy advanced planning systems’ deterministic math. We’ve built intellectual property with many process patents granted and more coming soon. Our AI platform consisting of purpose-built supply chain applications dramatically improves the time to value and eliminates the need to provide your own data scientists. 

3. People, Process, Technology:

As you explore pursuing an AI project, keep your people and process design in mind. I spoke with several supply chain practitioners this week who were exhausted by failed projects and unmet expectations. Even with a clear problem statement and your chosen partner, there is a risk that you will end up with a failed “science experiment” without a clear understanding of how specific people will use innovative technology to support a particular process. 

This week my friends Klaus Imping and Michael Ciatto released their book titled “Tribal f*cks up Digital.” Even as technological advancement accelerates, they describe that the heart of innovation and transformation is still PEOPLE. When the people of any organization do not adopt new processes and technology, it neutralizes enablement and sabotages effectiveness. Klaus and Michael believe tribal ways of working as the root cause of poor digital adoption. 

Supercharging your planning stack with AI can unlock enormous value for your organization. But this transformation will require your people to adopt new processes. At, we utilize innovative technology like Predictive AI, Graphical Neural Networks, Generative AI, and Reinforcement Learning. But we know that succeeding in our mission of creating a world without waste requires focusing on building a purpose-built platform to support people within specific supply chain processes. Therefore, we would like to give examples of how you can utilize the power of probabilities to capture hidden waste and boost profits. 


If you’re interested in learning more about applying AI to your current supply chain planning systems, we are ready to give you specific examples of how you can utilize the power of probabilities to capture hidden waste and boost profits. Just reach out here!