Published by
Don Hicks
Published on
March 26, 2026
.jpg)
You can’t plan your way out of a bad structure.
Managing the supply chain means making good decisions. Decisions everywhere: what to stock, where to stock, when to reorder, how much to reorder. Thousands of decisions must be made every day, and those decisions are called “Planning.” Supply chain planners try to optimize the operation, to make the best decisions they can, with the supply chain you have today. Great planners and processes make the supply chain run as well as it is capable of running. But there are always limits, always constraints that can’t be overcome.
The fact is, you never designed this supply chain. It evolved over months and year, one well-intended decision at a time. Why is that factory in that town? It made sense at the time. Why are you distributing through that facility in the southeast? It was a really good idea—then. But now, you’re in the wrong markets, with the wrong suppliers, running the wrong footprint, and no amount of planning can change that. You need a new design. You need change.
Most supply chain technology projects are rightfully focused on visibility and planning. What choice do you have? You can’t stop the bus, you gotta keep on riding! You have to optimize the network you’ve got, and typically that starts with pulling together all the data across the many different systems and functional parts of the supply chain. That is called a Digital Twin.
When done right, a digital twin is an end to end, complete view of your existing supply chain. It’s a control tower. It tells you what’s moving, what’s late, what’s at risk. It replicates what exists today. It can also improve your planning systems with the most accurate and up to date information.
That’s not nothing. But by definition the twin accepts the constraints of your current network as a given. Your Digital Twin offers the promise of making change to the network, but where does that change come from? How does a copy of your existing network reveal that other network you wish you had, the network that’s NOT a copy?
Here’s the thing about a digital twin: it’s built on your existing data. It’s descriptive. The digital twin only contains values for things that already exist in your network. Suppliers you actually use. Facilities you actually operate. Lanes you actually ship.
So what happens when you want to ask a real “what-if” question? What if we sourced from a supplier we’ve never used? What if we opened a distribution center in a region we’ve never operated in? What if we completely restructured our network for a new tariff environment?
There’s no data in the database to base the calculations on. No transaction or shipment history. The digital twin has nothing to show you. You’re not exploring possibilities — you’re just watching what exists from a different angle.
This is all fine when you’re using a Digital Twin as a control tower. It’s not enough for a company trying to make bold strategic decisions about the future. This isn’t the Twin you’re looking for.
The Third Twin is your supply chain re-engineered and optimized; it’s not a replica of what you have, but a rigorous model of the alternative future network you actually need. Your Digital Twin is descriptive, but what you want is to use it to be predictive.
Think of it as a sandbox. You take your existing supply chain, take your digital twin as the starting point, and get a place to make major or minor changes without consequence. Try different routes. Try different customers. Try different suppliers. Try an entirely different network. All of that with your existing data as the foundation — extended into possibilities that don’t exist yet.
This is really what supply chain design means: design is the ability to answer any question about your supply chain, now, but importantly, in the future. What if I did this? What’s the right move here? If you can formulate the question, we can answer it. That’s the power of the Third Twin — and it’s something a digital twin or a planning system cannot do, because it’s not meant to do it.
The challenge with modeling a future state supply chain is that the future hasn’t happened yet. It doesn’t exist except in our imaginations and inspirations. The test a change, you still have to build up costs and flows for which the historical data hasn’t been created. New lane rates. Supplier costs you’ve never negotiated. Labor costs at a greenfield site. Shortage rules, expedite policies, and new business logic.
This is where AI plays a real, substantive role, not the overhyped version. AI and neural networks are pattern recognition machines trained on historical data, which makes them excellent at filling in stable, predictable gaps. Estimating freight rates on lanes based on comparable corridors. Projecting demand at new locations based on regional patterns.
Neural Networks are prediction machines, but not crystal balls. When a prediction problem has historical data, stable patterns, and light context, ML/NN works great. You use the generated data to inform and flesh out novel modeling constructs in a supply chain model of the larger system. NNs can extrapolate from history, but it can’t predict the complex knock-on effects of a transformation. Fortunately, we have other tools in our toolkit, namely discrete event simulation and mathematical optimization modeling.
When you combine AI filling in the missing data with simulation and optimization modeling of the alternative future, you get something genuinely powerful: confident decisions about supply chains you’ve never operated, at a speed that used to take months and now takes days.
And that speed is accelerating. The agentic AI breakthroughs we’ve made at Optilogic mean AI agents are now constantly monitoring your data, generating new network models, surfacing true What-Ifs and new scenarios you might not have thought to ask. The Third Twin isn’t just a place you go to run experiments manually; it’s becoming a continuously evolving model that your AI agents are helping you build and refine.
We’re at an inflection point. AI is continuing to make planning super-efficient across any size company. Demand forecasting, replenishment, inventory optimization — these are becoming table stakes. The next differentiator isn’t planning better. It’s designing smarter.
The companies that win will be the ones willing to challenge their sacred cows and ask bold questions about what their supply chain should look like, not just how to run it more efficiently. And to answer those questions faster than their competitors.
The Third Twin is where that work happens safely, without touching live operations. It’s your playground for imagination, backed by real engineering.
Optilogic customers expect that we bring the best operations research and computational technologies available to help them make better decisions. AI is transforming the landscape, letting us leverage classical models faster and more accurately than ever before. Computing power on demand has enabled our customers to scale those models to SKU, order and shipment levels with the flick of a switch.
Digital Twins are nothing more or less than the starting point for the future. To design a better future you must start with where you are at today. And then move beyond it.
You can do better than a Digital Twin. Apply your imagination and creativity to create a Third Twin. Or a Fourth Twin. Or a Fifth Twin, or a Fiftieth Twin. The only limiting factor now is your vision.
You can’t plan your way out of a bad structure.
Managing the supply chain means making good decisions. Decisions everywhere: what to stock, where to stock, when to reorder, how much to reorder. Thousands of decisions must be made every day, and those decisions are called “Planning.” Supply chain planners try to optimize the operation, to make the best decisions they can, with the supply chain you have today. Great planners and processes make the supply chain run as well as it is capable of running. But there are always limits, always constraints that can’t be overcome.
The fact is, you never designed this supply chain. It evolved over months and year, one well-intended decision at a time. Why is that factory in that town? It made sense at the time. Why are you distributing through that facility in the southeast? It was a really good idea—then. But now, you’re in the wrong markets, with the wrong suppliers, running the wrong footprint, and no amount of planning can change that. You need a new design. You need change.
Most supply chain technology projects are rightfully focused on visibility and planning. What choice do you have? You can’t stop the bus, you gotta keep on riding! You have to optimize the network you’ve got, and typically that starts with pulling together all the data across the many different systems and functional parts of the supply chain. That is called a Digital Twin.
When done right, a digital twin is an end to end, complete view of your existing supply chain. It’s a control tower. It tells you what’s moving, what’s late, what’s at risk. It replicates what exists today. It can also improve your planning systems with the most accurate and up to date information.
That’s not nothing. But by definition the twin accepts the constraints of your current network as a given. Your Digital Twin offers the promise of making change to the network, but where does that change come from? How does a copy of your existing network reveal that other network you wish you had, the network that’s NOT a copy?
Here’s the thing about a digital twin: it’s built on your existing data. It’s descriptive. The digital twin only contains values for things that already exist in your network. Suppliers you actually use. Facilities you actually operate. Lanes you actually ship.
So what happens when you want to ask a real “what-if” question? What if we sourced from a supplier we’ve never used? What if we opened a distribution center in a region we’ve never operated in? What if we completely restructured our network for a new tariff environment?
There’s no data in the database to base the calculations on. No transaction or shipment history. The digital twin has nothing to show you. You’re not exploring possibilities — you’re just watching what exists from a different angle.
This is all fine when you’re using a Digital Twin as a control tower. It’s not enough for a company trying to make bold strategic decisions about the future. This isn’t the Twin you’re looking for.
The Third Twin is your supply chain re-engineered and optimized; it’s not a replica of what you have, but a rigorous model of the alternative future network you actually need. Your Digital Twin is descriptive, but what you want is to use it to be predictive.
Think of it as a sandbox. You take your existing supply chain, take your digital twin as the starting point, and get a place to make major or minor changes without consequence. Try different routes. Try different customers. Try different suppliers. Try an entirely different network. All of that with your existing data as the foundation — extended into possibilities that don’t exist yet.
This is really what supply chain design means: design is the ability to answer any question about your supply chain, now, but importantly, in the future. What if I did this? What’s the right move here? If you can formulate the question, we can answer it. That’s the power of the Third Twin — and it’s something a digital twin or a planning system cannot do, because it’s not meant to do it.
The challenge with modeling a future state supply chain is that the future hasn’t happened yet. It doesn’t exist except in our imaginations and inspirations. The test a change, you still have to build up costs and flows for which the historical data hasn’t been created. New lane rates. Supplier costs you’ve never negotiated. Labor costs at a greenfield site. Shortage rules, expedite policies, and new business logic.
This is where AI plays a real, substantive role, not the overhyped version. AI and neural networks are pattern recognition machines trained on historical data, which makes them excellent at filling in stable, predictable gaps. Estimating freight rates on lanes based on comparable corridors. Projecting demand at new locations based on regional patterns.
Neural Networks are prediction machines, but not crystal balls. When a prediction problem has historical data, stable patterns, and light context, ML/NN works great. You use the generated data to inform and flesh out novel modeling constructs in a supply chain model of the larger system. NNs can extrapolate from history, but it can’t predict the complex knock-on effects of a transformation. Fortunately, we have other tools in our toolkit, namely discrete event simulation and mathematical optimization modeling.
When you combine AI filling in the missing data with simulation and optimization modeling of the alternative future, you get something genuinely powerful: confident decisions about supply chains you’ve never operated, at a speed that used to take months and now takes days.
And that speed is accelerating. The agentic AI breakthroughs we’ve made at Optilogic mean AI agents are now constantly monitoring your data, generating new network models, surfacing true What-Ifs and new scenarios you might not have thought to ask. The Third Twin isn’t just a place you go to run experiments manually; it’s becoming a continuously evolving model that your AI agents are helping you build and refine.
We’re at an inflection point. AI is continuing to make planning super-efficient across any size company. Demand forecasting, replenishment, inventory optimization — these are becoming table stakes. The next differentiator isn’t planning better. It’s designing smarter.
The companies that win will be the ones willing to challenge their sacred cows and ask bold questions about what their supply chain should look like, not just how to run it more efficiently. And to answer those questions faster than their competitors.
The Third Twin is where that work happens safely, without touching live operations. It’s your playground for imagination, backed by real engineering.
Optilogic customers expect that we bring the best operations research and computational technologies available to help them make better decisions. AI is transforming the landscape, letting us leverage classical models faster and more accurately than ever before. Computing power on demand has enabled our customers to scale those models to SKU, order and shipment levels with the flick of a switch.
Digital Twins are nothing more or less than the starting point for the future. To design a better future you must start with where you are at today. And then move beyond it.
You can do better than a Digital Twin. Apply your imagination and creativity to create a Third Twin. Or a Fourth Twin. Or a Fifth Twin, or a Fiftieth Twin. The only limiting factor now is your vision.
Fill out the form to unlock the full content
.jpg)
