3D warehouse & process models
Represent storage, docks, work areas, routes and process dependencies at the level needed for the decision. Use visualization to check operating assumptions with the floor team.
← Warehouse Design & Optimization
Warehouse Simulation & Digital Twin
Evaluate how warehouse layouts, resources and operating rules perform under different conditions.
You need to compare layouts, staffing levels or automation options under normal and peak demand before committing resources. We agree the operating requirements, input data and decision criteria before developing recommendations.
On site in Hyderabad, across Telangana and India. All our services in Hyderabad →
- AnyLogic process & material-flow simulation
- NVIDIA Isaac Sim robotics digital twin
- Throughput, travel & resource utilization
- What-if scenarios & bottleneck analysis
02 / Digital simulation
Test the operation
before changing it.
Compare movement, resource constraints and demand scenarios before committing to physical changes.
TV / WAREHOUSE TO LINEPUTAWAY · REPLENISH · LINE PICK
ILLUSTRATIVE SIMULATION — NOT CLIENT DATA
Model metrics (illustrative)
- Elapsed simulated time
- 0 s
- Active resources
- 0 / 3
- Receiving batches
- 0
- Batches to line
- 0
- Resource utilization
- 0%
- Supermarket replenishments
- 0
- Truck waiting
- 0 s
Resource utilization
F1
F2
P1
F1 collects received pallets and puts them into reserve storage. F2 takes available stock from storage to replenish the supermarket. P1 walks through three supermarket bays, builds a cart and passes it to the production line. Trucks wait at holding points while the single-width storage aisle is occupied; no overtaking during putaway or retrieval.
F1 · Putaway: Waiting for received pallet · Empty · 0s waiting
F2 · Replenishment: Waiting for stored pallet · Empty · 0s waiting
P1 · Line picking: Waiting for line kit · Empty · 0/3 bays picked
Model assumptions & measurement definitions
- One inbound batch moves through three dependent stages. Stock becomes available to the next resource only after unloading. A resource waits when its collection point has no available stock.
- Inbound batches arrive every 32s. Each collection and unloading takes 4s. Trucks move at 22 drawing units/s and the picker at 14; these synthetic coordinates have no physical scale. Each inbound batch represents a three-component assortment. The picker reserves one assortment, collects one component at each of three bays, then hands the loaded cart to the line.
- Travel follows storage aisles and the front cross-aisle. The alternative relocates the storage handover nearer the supermarket. Counts show available batches; materials being collected or transported remain assigned to their resource.
- Utilization is occupied resource-seconds divided by three resources × elapsed seconds, including empty returns; waiting at the holding point is excluded. The storage aisle is reserved for one truck through entry, handling and empty exit. Waiting time is tracked separately. Other traffic conflicts, rack capacity, SKU compatibility, production consumption and replenishment triggers are not modeled.
- Playback starts paused and pauses offscreen or in a hidden tab. All results are illustrative, not validated warehouse performance.
Advanced application · robotics & automation modeling
For decisions involving AGVs, AMRs, conveyors or automated storage, explore equipment constraints and interfaces alongside the wider warehouse model.
Simulated time0 s
Completed transfers0
Queued tasks0
Busy / resources0 / 2
Resource utilization0%
Completion rate0 / sim h
Selected robot 1: idle · task none · 0s to next phase · area receiving
Model assumptions & limitations
Two transport resources use their marked pickup/drop-off ports and color-matched lanes, with one FIFO task queue. Each task follows pickup → loaded travel → drop-off → empty return. A transfer completes after drop-off. Utilization includes empty return. The drawing shows storage-buffer pickup → picking handover → separate empty-return lanes. Loaded and empty paths stay in the clear corridor. Selected work areas are annotations and do not change this transport-only scenario.
Arrivals every 14s; pickup 6s; travel 18s; drop-off 4s; return 12s. Manual/AMR differences are assumed input timings, not measured or inherent technology advantages. No traffic interaction, safety validation, battery/charging, shared docking, variability or faults are modeled.
Scenario changes reset the model. Playback never autostarts and pauses offscreen or in hidden tabs. This is not a validated capacity model or commercial AMR fleet simulator.
Feasibility & business case
Make the investment justify itself.
Change the estimated operating assumptions. Add your own cost assumptions to evaluate a simple, undiscounted payback.
Assumption-based outputs
- Daily transport demand
- 480 tasks / day
- Manual baseline effort
- 40.0 person-h / day
- Nominal transport capacity
- 240 tasks / day
- Eligible tasks served (bounded)
- 240 tasks / day
- Potential released effort
- 13.0 person-h / day
- Manual effort remaining
- 27.0 person-h / day
Simple paybackNot established
Supply hourly labor cost, positive installed investment, annual operating cost and a non-zero cost-realization percentage. Released time alone is not cash savings.
Formulas, units & feasibility limits
- CU = one consistent currency unit chosen by you. All default operating values are synthetic; financial inputs are intentionally empty.
- Daily tasks = tasks/shift × shifts/day. Manual person-hours/day = daily tasks × manual minutes/task ÷ 60.
- Nominal transport capacity = resources × shift hours × shifts × 60 × availability fraction ÷ complete cycle minutes. Cycle must include pickup, loaded travel, delivery and empty return.
- Served tasks = min(eligible daily tasks, nominal capacity). Released person-hours = served tasks × manual minutes ÷ 60 × (1 − retained effort fraction). Manual effort remaining = baseline − released effort.
- Annual net benefit = released hours/day × operating days × cost/hour × cost-realization fraction − incremental annual operating cost. Simple payback = installed investment ÷ positive annual net benefit.
- No promise of equivalent labor removal or capacity. Charging, traffic, interfaces and downstream constraints must be reflected in a validated cycle and availability; peaks and congestion can reduce capacity further. Capital cash flows, financing, taxes, residual value and discounting are excluded. Vendor quotes, observed routes, peak demand and pilot validation are needed before an investment decision.
Receiving / assessment questions
- Material movement
- Dock unloading to inspection and receiving staging.
- Repetitive handling
- Repeated pallet transfers and receipt handovers.
- Potential application
- Pallet transport after receipt and load identification are confirmed.
- Operational constraints
- Trailer interfaces, variable load condition, dock peaks and pedestrian interaction.
- Data required
- Arrival profile, pallet dimensions, dwell time, unload method and receipt exceptions.
Technology explorer
The right technology depends on the work.
Autonomous Mobile Robots (AMRs)
- Example use cases
- Transport totes, carts or pallets between agreed pickup and handover points.
- Integration requirements
- Task dispatch, fleet coordination, load transfer, charging and WMS confirmation.
- Operational limitations
- Shared traffic, localization changes, charging and blocked handover points require design.
- Suitable conditions
- Repeatable transport demand, suitable floors and managed human/vehicle interaction.
Representative equipment photographs · not TvastrOps installations · no vendor endorsement or compatibility claim
Systems integration architecture
A robot is only one part of the system.
Work requests flow toward execution; acknowledgements, exceptions and completion confirmations return to their owners.
ERP / WMS
Function
Owns business orders, inventory and authorized work release.
Example data exchanged
Order/task identity, SKU, quantity, locations and completion status.
Dependencies
Agreed source of truth, location master and idempotent transaction ownership.
Failure considerations
Duplicate, delayed or canceled work must not create duplicate stock movements.
Conceptual responsibility model, not a prescribed software stack. Interfaces and safety controls require supplier-specific engineering and verification; business software is not a safety control.
Service overview
Evaluate operating performance.
We model process and material flow in AnyLogic (discrete-event simulation of arrivals, queues, routes and resources) and robots and automation in NVIDIA Isaac Sim (physics-based 3D simulation of AMR fleets, robotic picking and conveyors inside a digital twin of the building), depending on the question and available data. It is checked against observed behavior where a baseline exists. Inputs, sensitivities and limitations accompany the findings; a visual model alone is not evidence of validated capacity or a live connected digital twin.
We build decision-specific warehouse models to examine how arrivals, tasks, routes, queues and resources interact. The purpose is to compare operating alternatives on a common demand profile before committing space, staffing or capital.
Physical layouts and rack planning are covered by Warehouse Layout DesignB2 / Scope of work
Models built around the decision.
We agree the areas, data requirements and deliverables during scoping. The activities below define the service’s technical focus.
Discrete-event & material-flow logic
Model arrival patterns, task release, queues, handling and resource availability in AnyLogic. Define event rules and exceptions rather than assuming continuous, unconstrained movement.
Throughput, capacity & utilization
Compare completed work, waiting and workforce or equipment occupancy across demand and resource plans. Separate model outputs from measured operational results.
Robotics & automation in NVIDIA Isaac Sim
Where automation is on the table, test AMR and AGV fleets, robotic picking and conveyors in NVIDIA Isaac Sim: a physics-based 3D digital twin of your warehouse with simulated robots and sensors, run against your layout and order profile before you buy. Supplier interfaces and the supplier's own validation are defined for each application.
SIM-02 / Technical explanation
Equipment movement & travel
Trace forklift, picker and transport routes, loaded and empty movement, handovers and travel distance. State route geometry, speed and handling-time assumptions.
SIM-03 / Technical explanation
Congestion & what-if evaluation
Test alternative layouts, staffing levels, peak arrivals and operating rules. Identify queue locations, shared-aisle constraints and bottlenecks, and assess sensitivity to uncertain inputs.
Technical approach
From inputs to an operating decision.
Floor observation and operating data establish the basis; review and verification close the loop.
- 01
Frame the decision
Agree alternatives, performance measures, model boundary and the evidence needed to change the decision.
- 02
Collect & check inputs
Observe task times and routes; review demand, staffing and equipment data. Record missing inputs and assumptions.
- 03
Build & compare scenarios
Check model logic and baseline behavior, then compare alternatives on the same demand and measurement basis.
- 04
Report & verify
Issue findings, sensitivities and limitations. Define a pilot and compare live observations with modeled expectations.
Applications
When this work is useful.
Typical operating situations; these are not client case studies.
- You are choosing between layouts, staffing models or automation options.
- Peak demand is approaching and you need to know where the operation will queue.
- An investment case depends on throughput or capacity assumptions nobody has tested.
Engineering deliverables
What you receive.
Process model & scenario definitions
The decision being tested, the scenarios that could change it and the input data each one depends on.
Flow, queue & utilization analysis
Throughput, waiting, resource utilization and congestion compared across scenarios on the same demand.
Automation feasibility assessment
Where robotics or automation fits the observed work, and the assumptions behind that view.
Layout scenario comparison
Layout alternatives modeled against the same demand, with travel and congestion compared.
2D/3D design visualization
Visual models that let operations, finance and suppliers see each option and challenge it.
Model assumptions & limitations
A plain record of what the model includes, what it leaves out and which inputs move the answer.
Decision support & pilot plan
A recommendation, the pilot that would confirm it and the measures for comparing live results with the model.
Scope is agreed during assessment and sized to the decision. Automation integration is carried out alongside specialist equipment vendors and software providers.
How we track results
Measure the whole operation.
Material movement time
Elapsed time from released transport request to confirmed delivery; separate dispatch delay, travel and handover.
System throughput
Completed transfers per defined period, alongside demand, item mix and constraints.
Equipment utilization
Busy equipment time divided by available resource time; separate productive travel, return and waiting.
Queue & waiting time
Open tasks and elapsed waiting before service. Record queue age as well as queue size.
Manual handling effort
Observed operator hours across pickup, transport, handover and exception recovery; do not assume all work disappears.
Downstream delivery reliability
Required deliveries completed within their agreed operating window; validate timing with downstream teams.
Actual baselines, observation periods, acceptance criteria and improvement targets are established during assessment.
[ Questions buyers ask ]
Common questions
Should we automate our warehouse?
Test it before you buy it. We model your orders, layout and staffing, then compare the automation option with improving the current process under normal and peak demand, so the decision rests on throughput and utilization rather than a vendor's pitch.
What is a warehouse digital twin?
A computer model of your warehouse that behaves like the real one: orders arrive, people and equipment move, and queues form. We build process and layout models in AnyLogic and robotics and AMR fleet scenarios in NVIDIA Isaac Sim, then use them to test changes safely.
How many AMRs or forklifts do we need?
Simulation answers this by running your real order profile through different fleet sizes and showing utilization, congestion and throughput for each, including at peak. You see the point where adding more units stops paying off.
What data does a warehouse simulation need?
Order lines over time, item data, the layout, process times, and staffing and shift patterns. Where data is missing we state the assumption openly in the model, so you know how far to trust each result.
Is simulation worth it for a mid-sized warehouse?
It is when a decision is expensive to reverse: a new layout, a relocation, a peak staffing model or an automation purchase. For smaller changes we will tell you if a simpler analysis will do.
Start here
Let’s test your next decision.
Tell us what you’re deciding: a layout, a staffing model, a peak plan or an automation investment. We’ll tell you whether modeling would help and what it would take.