Launching a new warehouse with SimPy at Rent the Runway
summary
Rent the Runway uses SimPy discrete event simulator to model launching a second warehouse in Texas, encoding generators for customer orders, resources for SKU inventory, and a clock-like environment. The simulation runs on historical order streams and probabilistic demand to predict inventory ramp rate through returns, cross‑shipments and minimize split shipments. Development emphasizes acceptance tests, heavy logging — save event dictionaries as JSON to S3 — and postrun analysis with pandas and seaborn to decide staffing, transfer schedules, and buying strategy.
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chapters (19)
- 0:00Simulation at Rent the Runway
- 1:43Need for warehouse simulation
- 3:10Rent the Runway’s business model
- 6:05Rapid business growth
- 7:29Second warehouse in Texas
- 9:35Discrete event simulation limits
- 12:23Why SimPy for simulation
- 13:47SimPy introduction and components
- 14:30SimPy car process example
- 15:56SimPy warehouse order example
- 17:18Simulating warehouse operations
- 21:08Key decisions from simulation
- 23:57Seasonality impact on inventory
- 25:02Simulation project takeaways
- 26:47Team and stakeholder management
- 27:34Q&A: Simulations vs optimization
- 29:21Q&A: Measuring simulation success
- 31:09Q&A: Modeling demand spikes
- 32:45Q&A: Post-launch behavior
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