Dynamic warehouse replenishment using statistical modeling and reinforcement learning for a major luxury retailer.
Worked on the development of an intelligent replenishment system that balances inventory holding costs against stockout risk through classic stochastic approaches and learning approaches via simulation.
Stack
Challenge
Kering's luxury retail warehouses faced a classic inventory dilemma: minimize average stock levels to reduce carrying costs while keeping missing sales below acceptable thresholds. Static replenishment rules could not adapt to seasonal demand shifts and promotional events, leading to either excess inventory or costly stockouts.
Approach
Run as a capstone collaboration with Kering, the work explored and developed a dynamic replenishment algorithm combining statistical demand modeling with reinforcement learning.
Results
- ●Reduced average stock levels
- ●Controlled missing sales rate
- ●Validated via simulation backtesting
How Frema Labs applies this for you
If inventory cost and stockouts pull against each other, the same demand-modelling and simulation approach can size dynamic replenishment for your network.
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