Translate a warehouse process map and operational data into a discrete-event simulation model structure.
Warehousing & Procurement
Warehouse Digital Twins and Discrete-Event Simulation for Capacity Planning
Learn to build and use warehouse digital twins and discrete-event simulation models to test layout, staffing and equipment decisions before committing capital or disrupting operations.
🗓️Duration5 training days
📚Content4 modules · 8 sessions
🎓On completionAccredited attendance certificate
About the programme
Course Overview
Redesigning a warehouse layout or adding a new automation system on the strength of a spreadsheet forecast is a common way to discover, after the concrete is poured, that peak-hour congestion or dock scheduling was never properly tested. Digital twins and discrete-event simulation let planners build a working model of a warehouse and run it forward in time under realistic order profiles, so bottlenecks, staffing gaps and equipment shortfalls show up before real money is spent. This course teaches participants to build simulation models from process maps and operational data, validate them against current performance, and use them to test layout changes, staffing levels, slotting strategies and automation investments under peak, average and disrupted conditions. Participants learn the modelling logic behind discrete-event simulation, how to represent resources such as forklifts, pickers and dock doors with realistic constraints, and how to connect a model to live data feeds to create an ongoing digital twin. The course covers selecting simulation software appropriate to project scale, interpreting output such as utilisation, queue length and throughput, and presenting results credibly to stakeholders approving a capital change. A hands-on modelling exercise on a sample distribution centre has participants build, validate and run a simulation to answer a real capacity question.
Expected Learning Outcomes
Validate a simulation model against current throughput and utilisation data before trusting its output.
Model resource constraints such as forklifts, pickers, dock doors and conveyors realistically within a simulation.
Test layout, staffing and slotting changes under peak, average and disrupted demand scenarios.
Evaluate proposed automation investments by simulating their impact on throughput and bottlenecks.
Select simulation or digital twin software appropriate to the scale and complexity of a given project.
Present simulation results and recommendations credibly to stakeholders approving capital investment.
Who Should Attend
Warehouse and distribution centre design engineers evaluating layout or automation changes.
Operations managers planning capacity for peak season or business growth.
Industrial and process engineers building simulation models for logistics operations.
Supply chain network planners assessing distribution centre capacity across a network.
Continuous improvement and Lean practitioners testing process changes before implementation.
Consultants and project managers commissioning simulation studies for warehouse clients.
Course Modules
Select any module to see its sessions and points.
01Foundations of Warehouse Simulation Modelling
2 sessions · 8 points
Session 1Understanding Discrete-Event Simulation Logic
- Explain how discrete-event simulation represents entities, resources and events moving through a process.
- Distinguish discrete-event simulation from simpler spreadsheet or static capacity models.
- Identify the warehouse processes, such as receiving, put-away and picking, most valuable to model first.
- Assess when a full digital twin, updated with live data, adds value over a one-off simulation study.
Session 2Gathering Data and Building the Model
- Collect process times, resource counts and order profile data needed to parameterise the model.
- Represent resource constraints such as forklift availability, dock doors and picker headcount accurately.
- Build the model structure in simulation software, sequencing processes to match the real operation.
- Document assumptions and simplifications made in the model for later validation and stakeholder review.
02Validating and Testing the Model
2 sessions · 8 points
Session 1Validating Against Current Performance
- Run the model against historical order data and compare output to actual recorded throughput.
- Identify and correct discrepancies between simulated and actual utilisation or queue lengths.
- Test model sensitivity to key assumptions such as pick rate or dock turnaround time.
- Obtain operational stakeholder sign-off that the validated model reflects real warehouse behaviour.
Session 2Running Scenario and What-If Analysis
- Test layout changes, such as relocating fast-moving stock, and measure the simulated impact on travel time.
- Model staffing scenarios to identify the minimum headcount that meets service levels at peak volume.
- Simulate the introduction of automation, such as conveyor or robotic picking, on overall throughput.
- Run disrupted-condition scenarios, such as a dock closure or system outage, to test operational resilience.
03Applying Simulation to Capacity Decisions
2 sessions · 8 points
Session 1Evaluating Layout and Slotting Options
- Compare alternative warehouse layouts using simulated travel distance and congestion metrics.
- Test slotting strategies against simulated pick paths to quantify productivity improvement.
- Assess cross-docking or flow-through configurations for their effect on dock and floor congestion.
- Use simulation results to prioritise layout investments by measured return in throughput or labour.
Session 2Assessing Automation and Equipment Investment
- Model the throughput impact of adding conveyors, sortation or robotic picking systems before purchase.
- Identify bottlenecks that would remain after a proposed automation investment, avoiding over-promised gains.
- Compare equipment investment options using simulated utilisation and payback under realistic volume.
- Test the resilience of an automated design against equipment downtime or maintenance scenarios.
04Presenting Results and Sustaining a Digital Twin
2 sessions · 8 points
Session 1Communicating Simulation Findings
- Translate simulation output such as utilisation and queue length into business-relevant recommendations.
- Use visualisation and animation from the simulation to make bottlenecks tangible to non-technical stakeholders.
- Present a business case for a capacity investment supported by simulated before-and-after comparisons.
- Address stakeholder scepticism about model assumptions with transparent sensitivity analysis.
Session 2Building an Ongoing Digital Twin
- Connect a validated simulation model to live warehouse management system data for continuous use.
- Establish a process for updating the model as layout, equipment or order profiles change over time.
- Use the digital twin to test operational changes quickly before they are implemented on the live floor.
- Assign ownership for maintaining model accuracy and governance as the digital twin becomes embedded.
What the participant receives
4 course modules
A structured syllabus
8 training sessions
across 5 days
32 detailed points
Applied, detailed content
Accredited attendance certificate
On completing the programme
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