The Dying Art of Supply Chain Modeling

Over the past decade, supply chain modeling has come a long way, particularly in how we solve network, inventory, and transportation problems. While popular off-the-shelf tools have made modeling more accessible, they have also led to a decline in the creative and critical thinking that makes great models… well great.

“As more teams lean on these one size fits all solutions, workarounds are piling up, solutions feel disconnected from reality, and innovation often takes a backseat.”

While automation and convenience are essential, is the art of supply chain modeling dying? And if so, should we fight to bring it back?

The Rise of Canned Modeling 

In the early days, supply chain modeling was the domain of Operations Research experts—complex, technical, and only accessible to a select few who knew how to unlock its value. But over the past decade, that’s changed. Pre-built solutions have become the standard for creating models by filling in tables instead of starting with the problem at hand. While these solutions have made it easier to interact with the science, they’ve also led to a sense of complacency and a growing risk that modelers lose touch with the problem-solving mindset that once defined the field.

Convenience Over Creativity 

Out-of-the-box tools are a double-edged sword—packaged into ready-made solutions for common supply chain challenges like network optimization, inventory positioning, and transportation route planning. These solutions, often built on industry practices, allow modelers to get standard answers but make sweeping approximations for real-world challenges. It’s more common than we’d like to admit: recommendations that look good on paper but simply can’t be implemented. And with convenience so readily available, many modelers have stopped digging into the real drivers of their supply chain problems themselves. Instead, they default to pre-built frameworks and end up shaping the problem to fit the solution, rather than the other way around. As a result, supply chain strategies are beginning to look more and more alike. When everyone uses the same tools in the same way, true differentiation becomes harder to achieve—just when flexibility and agility matter most. 

“For organizations facing complex or unique challenges, relying solely on these pre-configured approaches can be more than limiting—it can be risky.”

What Canned Tools Can't Solve 

Packaged software has helped solve many well-defined supply chain problems, but its limitations become clear the moment things get messy—which they almost always do. Modeling isn't just about pressing buttons and getting results; it’s about understanding the nuances of the problem at hand and crafting a solution that’s both innovative and effective. Here are just a few areas where pre-configured tools tend to come up short:

• Handling Unique Business Constraints 
  No two supply chains are the same. From niche service-level agreements to complex capacity rules and shifting lead times, every organization faces its own set of constraints. While packaged tools can handle the basics, they’re often rigid when it comes to modeling anything that doesn’t fit their predefined structure. The result? Modelers end up bending their business to fit the tool instead of building a model that reflects how their supply chain actually works.

• Capturing Cross-Functional Dependencies 
  Supply chains don’t operate in silos—decisions in one area ripple through the rest. Shift your network design, and your last mile routing is off. Adjust inventory policies, and now your service levels wobble. But most packaged tools treat these as separate modules, missing the bigger picture. To model effectively, you need to go beyond the boundaries of individual modules and think in terms of end-to-end solutions. That kind of integrated thinking rarely comes pre-built.

• Responding to Dynamic Change 
  It’s a cliché because it’s true—today’s supply chains are more volatile than ever. A pandemic, a port shutdown, a bankrupt supplier, a viral TikTok trend—disruptions can hit from any angle, at any time. Static models built on historical data and fixed assumptions simply can’t keep up. By the time a traditional tool churns out an answer, the problem has already changed. Today’s modelers need agile, adaptive tools that can evolve with the business—not months later, but in weeks if not days!

Bringing Back the Art of Supply Chain Modeling 

The pace and complexity of today’s supply chains show no signs of slowing down—and that means it’s time to breathe new life into modeling. We need to bring back the art of this powerful craft. That means going beyond the boundaries of decades old canned technology, reigniting the creativity, curiosity, and problem-solving mindset that great modeling demands. Here are four ways to break free from the mold and get back to building models that will make a lasting impact on your organization for years to come:

1. Embrace Composability 

   Canned solutions can handle the basics, but real supply chain challenges often demand more. Modelers need to invest in building composable models tailored to their organization’s unique needs. That might mean digging into the data to uncover meaningful patterns, setting up data pre-processing pipelines to support modeling efforts, incorporating custom constraints that off-the-shelf tools can’t support, or weaving together traditionally separate models—like network, inventory, and transportation—into a cohesive, end-to-end solution.
   
   

2. Think Outside the Silo

   Supply chains span across functions—procurement, manufacturing, distribution, customer service—and the best models reflect that interconnected reality. Too often, modeling efforts stay stuck in functional silos, treating transportation, inventory, and production as separate problems. But decisions in one area almost always ripple into another. Modelers need to think across these boundaries and consider how each part of the supply chain impacts the rest. That kind of holistic, cross-functional thinking leads to smarter, more resilient solutions.
   
   

3. Invest in Dynamic Modeling 

   As supply chains become faster and more unpredictable, static models built on historical data just don’t cut it anymore. The future lies in dynamic modeling—where real-time data, AI, machine learning, and predictive analytics come together to power models that evolve as conditions change. These adaptive models can respond to disruptions, forecast future trends, and deliver insights when they’re needed most—not weeks or months later. And when you productize these models with intuitive, business-friendly interfaces, you empower stakeholders across the organization to engage directly, understand the value, and make faster, smarter decisions. It’s an “always-on” approach to modeling—one that meets the pace of modern supply chains. 
   
   

4. Productize & Engage the Business

   To fully realize the value of dynamic models, they need to be accessible across the organization—not just to technical teams. That means creating simple, tailored interfaces that reflect the needs of different stakeholders, whether in operations, finance, or logistics. When more people can interact with real-time models without deep technical expertise, it leads to faster decisions and a more responsive supply chain. Democratizing access turns modeling into a business-wide capability, not just a technical one—and that’s where real impact starts.

Reviving the Art

The art of supply chain modeling may not be dead yet—but it has been fading. Years of leaning on rigid, out-of-the-box tools have chipped away at the creativity, flexibility, and critical thinking that once defined the craft. We’ve gained speed, but often at the expense of depth.

It’s time to bring the art back—and not with just another tool, but with the right foundation.

A platform-first approach gives modelers the environment they need to do meaningful work: the freedom to move fast, tailor solutions, bridge silos, and make the science more accessible across the business. It’s how we move from one-off answers to living, adaptable models that evolve with the supply chain.

“We believe modelers have a critical role to play in shaping the future of enterprise supply chains.”

By giving them the power and flexibility they deserve, they aren’t just reviving a lost art—they are setting the stage for the next wave of supply chain innovation.