Hacking Uncertainty

Geopolitical upheaval, trend shifts, and even tactical adjustments to inventory flow can spawn risks and dependencies that AI algorithms are ill-equipped to tackle on their own. Even the most powerful new AI algorithms rely on historical patterns, like last year’s orders or shipping logs. But today’s supply chains are riddled with surprises and entanglements, like a sudden port strike or the working capital effect of new tariffs, that don’t yet exist in the data. 

Enter simulation – a framework and toolset that produces digital replicas of physical supply chains to allow leaders to pre-test or validate new scenarios in the safety of a 100% virtual sandbox.  

While AI grabs headlines and budgets (we found 41% of executives see autonomous agents as breakthrough tech vs just 12% for simulation and digital twins), simulation is the unsung hero in supply chain leaders’ risk response arsenal. 

The full report explores the gains simulation can offer and how to capture them, as well as how leaders use supply chain simulation for strategic growth advantage. Our data, analysis, and use cases show how organizations can model complex scenarios, test responses, and adapt quickly – before reality forces their hand.  

Leaders Are On It  

Supply chain leaders are increasingly tuning into the simulation opportunity. Zero100’s analysis of over 16,000 supply chain patent applications found that simulation patent applications nearly doubled over the five-year period from 2019 (19%) to 2024 (35%).   

Even CEOs are taking note. On a Q2 2024 earnings call, Walmart President and CEO Doug McMillon made mention of his company’s “digital twin that allows us to simulate and optimize our supply chain in real-time.” In the report, we deep dive into the entire journey of Walmart's simulation-optimization triumph alongside numerous cross-industry use cases.  

Hurdles and Breakthroughs 

The full report highlights major simulation implementation challenges and how companies generally mitigate deployment risk. Here, we share just a few: 

Data fragmentation: The data ecosystem needed to run effective simulation isn’t always mature.
Success Story: Home Depot overcame this problem by starting its simulation journey with just three core data domains (store inventory, distribution center operations, and transportation), proving value before expanding to 12 interconnected data streams.
Approach: Begin with available data, demonstrate early wins, and expand from a base of proven value.

Talent Gap: Specialized data science and engineering skills needed to run effective simulations, particularly advanced digital twins, remain scarce. Also, maintenance demands ongoing tuning of models, sensor health checks, and software updates.
Success Story: Schneider Electric partnered with Georgia Tech to create a development program, building a pipeline of 120+ simulation experts over three years.
Approach: Combine hiring, university partnerships, and internal development programs to accelerate domain expertise.

Technical Complexity: Developing and linking multiple simulation-optimization model pairs may require algorithm development beyond the out-of-the-box capabilities of some supply chain planning and execution platforms.
Success Story: The North American division of a leading global cosmetics manufacturer partnered with a modular simulation platform provider with standardized interfaces, allowing teams to add specialized algorithms over time without disrupting the core system.
Approach: Architect for modularity and ease of integration, then test, learn, prove value, and expand.

The Big Picture 

Supply chain simulation offers a rare arbitrage opportunity for those who see its untapped enterprise potential. Forward-thinking supply chain leaders will seize the moment by:  

• integrating risk professionals into integrated planning forums like IBP/RBP,  

• establishing a risk lab or game room that stress-tests corporate risk scenarios with supply chain simulation technology and know-how, 

• and forming a risk and resilience steering committee to capture learnings and drive lab breakthroughs into scaled agile war rooms.  

For most, board-level integration of simulation-optimization remains an open runway and the dozens of case examples captured in the full report demonstrate that focused investments in this realm deliver outsized self-funding returns.  

The wave is cresting. Let’s surf. 

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