Digital Twin
See the Operation Before You Change It.
Model your warehouse, run representative demand, and evaluate how proposed automation could perform before physical deployment reaches the floor.
Validate the assumptions behind the investment. Explore throughput, workflows, resource constraints, system behavior, and operating scenarios in a synchronized digital environment.
Bring in the relevant layout, racks, inventory, workflows, demand patterns, and operating constraints.
Run representative order profiles, SKU behavior, volume patterns, and workflow assumptions.
Surface potential congestion, capacity limitations, workflow conflicts, and system dependencies earlier.
Use modeled operating evidence to inform system design, deployment priorities, and investment decisions.
Physical ↔ Digital
Your Warehouse. Its Digital Counterpart.
The Digital Twin begins with the operation you already run. Warehouse conditions, inventory, workflows, demand, and automation behavior become the foundation for a synchronized environment where proposed changes can be evaluated before reaching the floor.
The Warehouse as It Exists
Existing racks, inventory, workflows, labor patterns, order profiles, material flow, and operating constraints establish the conditions the proposed solution must serve.
The Operation Before Deployment
Recreate facility behavior, inventory movement, robotic execution, workflow logic, and operating scenarios in an environment where alternatives can be tested without disrupting live operations.
One Operating Truth. Two Environments.
How Validation Works
Model. Simulate. Validate. Then Deploy.
Turn the current operation into a controlled digital environment, introduce proposed automation, and test how the system responds before physical implementation.
Recreate the Operation
Represent the relevant warehouse layout, racks, inventory, workflows, resources, automation, and operating constraints in a digital environment.
Establish a common operating baselineRun Operating Scenarios
Introduce representative order profiles, SKU behavior, volume changes, resource availability, and proposed automation configurations.
Observe how the system behavesEvaluate the Proposed Design
Examine throughput, utilization, queues, handoffs, workflow interactions, and potential bottlenecks across the proposed operation.
Refine assumptions with operating evidenceMove Forward With Better Information
Use the validated design to inform equipment configuration, workflow decisions, implementation priorities, and the path to physical deployment.
Translate validation into implementationTest the Conditions That Actually Matter.
Replace Assumptions With Operating Evidence.
The objective is not simply to create a 3D representation of the warehouse. It is to create a controlled environment where teams can compare alternatives, expose constraints, refine system design, and make better-informed automation decisions before implementation.
From Performance to Business Case
Evaluate the Operation. Then Evaluate the Investment.
Digital Twin analysis connects operational performance with business decisions. Instead of relying only on theoretical averages, teams can evaluate proposed automation against the conditions, constraints, and priorities of the operation being considered.
Start With the Conditions That Drive Performance.
The quality of the business case depends on understanding what the operation actually has to accomplish.
- Order profile and volume variability
- SKU velocity and inventory distribution
- Labor requirements and process assumptions
- Available storage and operating capacity
- Proposed automation configuration
- Service-level and workflow requirements
See What the Proposed Operation Could Require.
Modeling turns operational assumptions into evidence that can inform the design, system scope, and investment case.
- Throughput behavior under modeled demand
- Equipment and resource utilization
- Potential bottlenecks and queue formation
- Capacity and storage implications
- System sizing requirements
- Inputs for investment and deployment decisions
Examine the Levers That Shape the Business Case.
The objective is not to generate one universal ROI number. It is to understand how proposed automation changes the operating variables that determine value in a specific facility.
Evaluate how orders, tasks, and material flow move through the modeled system under representative demand.
Understand productive capacityExamine where work changes, where travel may be reduced, and how staffing assumptions interact with automation.
Inform resource requirementsExplore how storage, rack utilization, workflow design, and automation configuration affect available operating capacity.
Understand space and storage implicationsExamine how robots, lifts, storage locations, work areas, and other resources are used across scenarios.
Identify under- or over-utilized resourcesSurface congestion, queue formation, handoff constraints, and interactions that may limit expected performance.
Find constraints before installationCompare equipment quantities and system configurations against the modeled performance required by the operation.
Inform the proposed automation scopeConnect Operational Evidence to Investment Decisions.
A stronger automation business case starts with understanding what changes operationally, what resources are required, where capacity is created, and what constraints remain. Those modeled outcomes can then be combined with customer-specific capital, labor, service-level, growth, and financial assumptions to evaluate the proposed investment.
Virtual Commissioning
Test Execution Before Equipment Arrives.
The Digital Twin can extend beyond performance modeling into virtual commissioning—providing a controlled environment to evaluate workflows, interfaces, orchestration logic, and exception handling before physical go-live.
Make the First System Run a Digital One.
A warehouse automation deployment involves more than equipment. Software interfaces, task sequences, routing logic, system handoffs, and exception behavior all have to work together. Virtual commissioning creates an environment where those interactions can be exercised before they depend on physical equipment.
Find integration and workflow issues earlier. The objective is to move more testing upstream, where changes can be evaluated digitally instead of discovering them for the first time during physical commissioning.
From Interface Logic to Virtual Execution.
Test → Observe → RefineExercise system messages, data exchanges, and integration behavior.
Test task sequences, routing decisions, priorities, and handoffs.
Introduce failures, blocked paths, unavailable resources, and unusual events.
Exercise the proposed operating sequence without depending on physical equipment.
Carry forward tested workflows and refined assumptions into commissioning.
Evaluate how WMS, orchestration, controls, automation, and operational systems exchange information across the proposed workflow.
Test the sequence of releases, tasks, movements, handoffs, and completions before physical execution depends on them.
Introduce unavailable resources, blocked paths, delayed tasks, or other exceptions to observe how the proposed logic responds.
Adjust priorities, routing rules, task logic, and workflow assumptions as issues are identified during virtual execution.
Find Issues Earlier. Refine Before Go-Live.
Arrive on Site With More Already Tested.
Virtual commissioning does not eliminate the need for physical testing. It moves more validation upstream so teams can arrive at installation with better-understood interfaces, workflows, control logic, exception scenarios, and operating assumptions.
After Go-Live
The Model Doesn't Stop When the System Starts.
Once automation is operating, live system data can reconnect the physical warehouse with its digital counterpart—creating a continuing source of operational context for understanding performance, investigating change, and informing what happens next.
Robots, workflows, inventory, resources, and operating conditions generate real execution behavior.
Execution data provides visibility into what the operating system is actually doing.
Operational data can be connected back to the model to provide context around system behavior.
Compare operating behavior, investigate constraints, and evaluate potential changes or scenarios.
Use operational insight to inform workflow changes, configuration, priorities, and future system decisions.
From a Project Model to an Operating Asset.
Connect What Was Modeled With What Actually Happened.
Before deployment, the Digital Twin helps teams understand how a proposed system could perform. After go-live, operational telemetry provides evidence about how the system is performing. Bringing those two perspectives together creates a stronger foundation for investigating differences, testing changes, and informing future operating decisions.
Compare modeled operating expectations with observed system behavior.
Understand how equipment, storage locations, and operating resources are being used.
Explore what changing order profiles, volume, or operating rules could mean for the system.
Evaluate potential changes before adding capacity, equipment, workflows, or automation scope.
Progressive Scale
Scale What Works. Validate What Comes Next.
Modernization does not have to begin with the final future-state system. Use what has already been learned from the operation to evaluate the next workflow, capacity requirement, or automation decision before expanding the physical deployment.
Start With What Is Working
Use actual operating behavior and the validated model to understand current throughput, utilization, constraints, and available capacity.
Evaluate the Next Workflow
Model an additional aisle, process, workflow, or automation use case before introducing the change to live operations.
Test Additional Capacity
Compare equipment quantities, storage requirements, resource utilization, and workflow impacts before adding physical system capacity.
Apply What You Learned
Carry forward validated assumptions, operating lessons, and deployment patterns when evaluating similar opportunities elsewhere in the operation.
Let Each Deployment Inform the Next One.
After go-live, operating data creates a stronger baseline for future decisions. Instead of treating expansion as a completely new design exercise, the next scenario can begin with what has already been observed, validated, and learned.
Scale With Evidence—Not Assumption.
The goal is not to predict every future automation decision on day one. It is to establish a repeatable process where teams can deploy, measure, model the next change, and expand when the operating and business case supports it.
Model Your Operation
Start With the Warehouse You Actually Operate.
Bring the layout, demand, inventory, workflows, constraints, and automation questions that matter to your operation. Cartesian can help turn them into a digital environment where proposed changes can be evaluated before reaching the floor.
You do not need to know the final automation answer first. Start with the operating problem, model the relevant scenarios, and use the evidence to determine what the next decision should be.
From Operating Question to Better-Informed Decision.
Layout, rack configuration, order profile, inventory, workflows, constraints, and operating priorities.
Introduce the proposed automation, workflow changes, resource assumptions, and operating conditions that need to be evaluated.
Examine performance, constraints, system sizing, operating implications, and the potential path to deployment.
The Digital Twin gives warehouse teams a way to explore automation decisions in the context of the operation they already run—helping move the conversation from “What could we automate?” toward “What should we change, and why?”