Case Studylightgbminventory optimizationzalandodiscrete event simulation
Zalando Deploys ZEOS Inventory Optimization Tool
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9.3
Zalando recently published in Nature Scientific Reports a practical replenishment optimization paper describing the ZEOS tool, which combines LightGBM probabilistic forecasts, discrete-event simulation, and an extended (R, s, Q) policy optimized via Monte Carlo. In a computational backtest (Oct 2023–Sep 2024) across ~2 million articles and ~800 merchants, the engine achieved +22.11% GMV, +21.95% gross margin, 91.14% fill rate, and 86.40% availability.
Scoring Rationale
Strong peer-reviewed evidence and large-scale backtest drive score; limited by focus on fashion e-commerce rather than cross-industry generality.
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