Validate the design before you commit the capital.
Simulation and digital twin modeling allow us to test system designs, identify bottlenecks, validate throughput, and optimize configurations before any capital is committed to equipment. We do not recommend systems we have not validated.
Why Simulation Matters.
Warehouse automation systems are complex, dynamic, and expensive. The interactions between storage systems, transport equipment, picking stations, conveyors, and software create emergent behaviors that cannot be predicted by static analysis or spreadsheet models.
Discrete-event simulation models the warehouse as a dynamic system — capturing the stochastic variability of real operations, the interactions between system components, and the impact of operational scenarios on throughput, utilization, and performance.
Digital twin technology extends simulation into operations — creating a live virtual model of the warehouse that can be used for ongoing optimization, scenario planning, and operational decision support.
Deliverables
What We Deliver.
Simulation Model Development
Development of a discrete-event simulation model representing the proposed warehouse system — storage, transport, picking, packing, and software logic.
Throughput Validation
Validation of system throughput against requirements across normal operations, peak periods, and stress scenarios.
Bottleneck Analysis
Identification of system bottlenecks, constraints, and underperforming components — with recommended design modifications.
Scenario Analysis
Analysis of operational scenarios including volume growth, SKU mix changes, seasonal peaks, equipment failures, and process variations.
Configuration Optimization
Optimization of system configuration — equipment counts, buffer sizes, routing logic, staffing levels — to maximize performance and minimize cost.
Digital Twin Design
Design of a digital twin architecture for ongoing operational monitoring, optimization, and scenario planning post-implementation.
Our Process
How We Approach Simulation.
Data Collection
Collect operational data — order profiles, SKU velocity, volume distributions, processing times — to parameterize the simulation model accurately.
Model Development
Build the simulation model representing the proposed system design, including all major equipment, processes, and software logic.
Model Validation
Validate the model against known operational data or vendor performance specifications to confirm model accuracy.
Baseline Simulation
Run baseline simulations to establish system performance under normal operating conditions and confirm throughput requirements are met.
Scenario Testing
Test system performance across a range of operational scenarios — peak volumes, equipment failures, process variations — to identify risks and optimization opportunities.
Optimization & Reporting
Optimize system configuration based on simulation results and deliver a comprehensive simulation report with findings and recommendations.
Ready to validate your system design?
Simulation is the most cost-effective investment you can make before committing capital to automation equipment.