The Role of Digital Twins in Modern Supply Chain Optimization
Digital Twins have emerged as powerful tools in supply chain optimization and are continuing to grow in popularity across industries. In 2023, the Digital Twin in the logistics market was valued at $1.2 billion and is projected to grow at a CAGR of over 25.7% between 2024 and 2032. In a world where efficiency and agility are paramount, Digital Twins have become a must-have for any company aiming to stay competitive.
What is a digital twin in supply chain?
A digital twin is a virtual replica of a physical supply chain system — including plants, warehouses, and transportation flows — that allows companies to simulate scenarios, predict outcomes, and optimize decisions before executing them.
Digital Twins are the answer if you’re looking to monitor and predict logistics, test strategies in a risk-free virtual environment, and optimize operations seamlessly. However, they go beyond passive analytics by actively taking action to enhance supply chain performance. While “optimization” can sound abstract, real-world results speak volumes:
Lower transportation costs through more precise supply network planning.
Higher on-time/in-full service to customers, boosting satisfaction and loyalty.
Improved order fill rates, ensuring inventory meets demand.
With these advantages, it’s clear that Digital Twins are reshaping the future of logistics. Now, the question is, how can you enhance existing supply chain systems with the power of a Digital Twin?
In this blog post, we explore the potential of these cutting-edge tools and how ProvisionAi’s technology empowers supply chain managers to focus on strategic needs while the DIgital Twin prescriptively handles the day-to-day tasks.
Digital Twin Glossary
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Select a term to see the definition.
Digital Twins vs. Simulation: Key Differences
Before we discuss Digital Twins in logistics, let’s define a Digital Twin and explain how it differs from simulation.
What is a Digital Twin?
A Digital Twin is a virtual replica of a physical entity that mirrors its status, working condition, or position. Think of it as a living model of your operation or network, enabling monitoring, diagnostics, and prescribing actions. Digital Twins are like having a living model of the operation or network that you can explore in-depth without interfering with operations.
However, this definition is somewhat limited. While Digital Twins are often predictive, their true value in logistics optimization is in providing prescriptive optimization, i.e., action. The Digital Twin does the work, freeing staff to focus on more strategic tasks.
How is Simulation Different?
Simulation is more strategic or tactical. It is a model of a real system upon which experiments can be conducted to understand its behavior or evaluate various operating approaches. It doesn’t require a physical counterpart or real-time data to function.
Key Distinctions:
Real-time Monitoring vs. Scenario Testing:
Digital Twins offer real-time insights, while simulations focus on hypothetical “what-if” scenarios.
Predictive vs. Prescriptive Capabilities:
While simulations may forecast potential outcomes, Digital Twins go further by offering prescriptive solutions, actively recommending actions, or even initiating them. It’s one thing to predict; it’s another actually to act.
Dynamic vs. Static Models:
Digital Twins continuously update with real-world data, whereas simulations are typically based on fixed data sets and assumptions.
Both simulations and Digital Twins aim to understand, predict, and optimize systems. However, their key difference lies in functionality: simulations focus on analysis and testing, while Digital Twins provide real-time insights and prescriptive actions. They complement each other, with simulations often supporting the development and operation of Digital Twins.
Simulation in Supply Chain Example: Finding the Best Facility Locations
One typical application of simulation in supply chain management is determining the optimal location for warehouses and plants. These decisions drive significant cost and service outcomes, considering factors like:
- Transportation costs
- Labor costs
- Proximity to customers
- Access to raw materials
By simulating different scenarios, such as fuel-cost changes or volume growth, supply chain managers can evaluate the impact of other variables on the location decision and make informed decisions that will optimize their supply chain operations and mitigate risk.
Using a Digital Twin to Complete Truck Routes Faster
Truck operators can use Digital Twins to map transportation routes and optimize logistics. This technology extends what we have on our phones today—Waze constantly updates the fastest way home based on traffic bottlenecks. Waste Management, the large trash hauler and recycler, has recently implemented this technology. They call it “Waze on Steroids” and recognize significant productivity improvements.
Digital Twins in Logistics Optimization
Digital Twins are playing a growing role in logistics and supply chain management. Forty-plus years ago, companies started modeling industrial processes. On a computer screen, plant staff could see a schematic of a large and complex operation showing all the pieces of equipment and their operational status, as well as the real-time work-in-process inventories waiting to be processed.
This was and continues to be an excellent means of bottleneck identification. Since its early days, this visibility has expanded to other parts of the supply chain.
Using Digital Twins for Planning and Forecasting
Digital Twins can help with planning and forecasting, but so far, the applications are more theoretical than practical. The concept is that monitoring the environment or sales can trigger action. For example:
- Watching the weather and traffic flows allows one to predict the amount of ice cream sold at each store. Is this worth the effort?
- As sales happen, the demand signal is sent to the factory to make a replacement unit. What is gained by being “real-time” in this situation?
- Monitoring the dock for congestion and sending a signal to waiting carriers is undoubtedly too late to be helpful. Creating a feasible dock schedule hours/days in advance provides a more significant benefit.
As you can see, we are not there yet. But with the Digital Twin logistics market’s 25.7% growth between 2024 and 2032, we’ll likely see this technology expand into planning and forecasting soon enough.
Using Digital Twin to Reschedule Operations
Some warehouse orchestration programs, like AutoScheduler, can adjust shipment times, move labor, or relocate inventory by linking expected truck arrival time data with what is happening in the warehouse. The arrival time data is gleaned from companies like FourKites or Project44. The same is true in manufacturing, where the failure of a second or third-tier supplier can be mitigated. In both these cases, the reaction is generally not real-time but updated on a regular but short cadence – for example, every 30 minutes.
Using Digital Twins with Optimization to Manage Replenishment
The line is, “Digital Twins with optimization rapidly scale capacity, increase resilience, and drive efficient operations.” The reality is again that this needs to run on a short-cycle-time cadence, not real-time. And that makes sense because events are generally discrete.
Prescriptive Supply Planning Decisions From ProvisionAi Technology – LevelLoad
Traditional supply planning has often overlooked cost and operational constraints, leading to volatile replenishment plans. As the graph to the right illustrates, this volatility is a persistent challenge that hurts operations.
Shipments per day on a single lane
Enter LevelLoad: A Smarter Way to Plan
LevelLoad, an optimizing Digital Twin, addresses these issues by gathering extensive data from operational and planning systems. It simultaneously optimizes all flows in the network for the next 30 days—a crucial capability since adjusting volume on one distribution lane impacts others.
For instance, if a receiving warehouse reaches capacity, the manufacturing site must redirect shipments to another location to manage its own space constraints.
LevelLoad goes beyond prediction by providing prescriptive solutions. It determines the number of trucks needed on each lane and optimizes what should be loaded on each truck using the latest data. Once optimized, it automatically creates and implements an action plan, ensuring carriers are booked well in advance.
Short-Term Benefits
In the short term, being able to secure trucks earlier and with much less volatility resulted in:
- First tender acceptance rates jumped to nearly 100%.
- Deployment transportation costs dropped by approximately 4%.
- Customers experiencing improved shipment timeliness and order fulfillment. (enhanced OTIF)
- Process automation
- Staff are freed from performing routine tasks, allowing them to focus on strategic opportunities.
Long-Term Advantages
By reducing volatility by 60%, LevelLoad has helped carriers minimize deadhead miles and improve equipment utilization. These efficiencies have reduced freight rates, driving long-term cost savings for the company.
LevelLoad’s prescriptive power delivers both immediate and lasting improvements in supply chain performance, making it an indispensable tool for logistics optimization.
Unlock Supply Chain Efficiency with Digital Twins and Simulation
Digital Twins and simulation are transformative tools that drastically improve supply chain operations. They allow supply chain managers to monitor real-time conditions, predict potential issues, test strategies in a risk-free environment, and optimize overall performance.
While simulation is used for analysis and testing in the design phase, Digital Twins serve throughout the entire lifecycle of their physical counterparts.
Supply chain managers need a deliberate approach to harness the power of Digital Twins and simulation fully. This includes:
Strategic Planning: Aligning tools with business goals.
Skilled Personnel: Leveraging expertise to interpret and act on data insights.
Robust Data Management: Ensuring accurate and real-time data flow.
Adopting Digital Twins and simulation provides companies with a significant competitive edge, enabling them to enhance efficiency, reduce costs, and stay ahead in a rapidly evolving industry.
Frequently Asked Questions
How do digital twins improve supply chain planning?
Digital twins connect real-time data with advanced modeling to reveal bottlenecks, inefficiencies, and risks. This enables planners to run “what-if” simulations and choose the most cost-effective, service-friendly options.
What’s the difference between a digital twin and simulation?
A simulation models a single scenario, while a digital twin is a living model that updates continuously with real-world data, making it more accurate and actionable.
Why are digital twins important for logistics and transportation?
In logistics, digital twins can optimize truck routing, warehouse throughput, and demand balancing, cutting costs, emissions, and service failures.
How do digital twins help reduce costs?
By modeling multiple flow and load scenarios, digital twins help companies fill trucks fuller, balance inventory, and avoid costly last-minute fixes, leading to measurable cost savings.
Can digital twins support Scope 3 emissions reduction?
Yes. By identifying ways to reduce empty miles and optimize loads, digital twins directly reduce CO₂ per shipment — making them a powerful lever for Scope 3 compliance.
What industries use digital twins in supply chain?
CPG, retail, automotive, pharma, and manufacturing industries rely on digital twins to manage complex networks, balance service and cost, and meet sustainability targets.
What are common challenges in implementing digital twins?
Data integration, siloed systems, and organizational change are the biggest hurdles. Companies succeed when they pair automation (AutoO2, LevelLoad) with digital twin strategy.
How quickly can companies see ROI from digital twins?
Many companies see ROI within 6–12 months, through reduced freight spend, improved OTIF performance, and better inventory positioning.
What is the future of digital twins in supply chain?
The future is AI-powered digital twins — models that learn continuously, self-correct, and provide prescriptive recommendations across cost, service, and sustainability levers.