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A practical approach to replenishment optimization with extended (R, s, Q) policy and probabilistic models.

Alva Presbitero1, Andreas Syrén2, Hagop Dippel2

  • 1Zalando, 10243, Berlin, Germany. alva.presbitero@zalando.de.

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PubMed
Summary

This study integrates probabilistic demand forecasting with inventory policy optimization for e-commerce. The novel Zalando E-commerce Operating System (ZEOS) tool improves efficiency and profitability in dynamic retail environments.

Keywords:
E-commerce supply chainInventory managementProbabilistic forecastingProfit contributionReplenishment optimizationRsQ policy

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Area of Science:

  • Operations Research
  • E-commerce Logistics
  • Supply Chain Management

Background:

  • Effective inventory management is critical in e-commerce due to fluctuating demand and complex fulfillment networks.
  • Existing optimization models often use simplified demand assumptions, failing to capture real-world uncertainties.
  • Bridging predictive modeling with practical replenishment decisions is a key challenge.

Purpose of the Study:

  • To integrate probabilistic demand forecasting with advanced inventory policy optimization.
  • To extend the classical [Formula: see text] policy for distributed fulfillment and seasonal assortments.
  • To develop a practical tool for e-commerce inventory optimization.

Main Methods:

  • Developed the Zalando E-commerce Operating System (ZEOS) Inventory Optimization Tool.
  • Unified one-shot inventory policy optimization with probabilistic gradient-boosting models (LightGBM).
  • Adapted the [Formula: see text] policy for distributed networks and seasonal assortments.

Main Results:

  • Achieved significant uplift in Gross Merchandise Value (GMV) and GMV after fulfillment costs compared to human and classical baselines.
  • Maintained high operational availability ([Formula: see text]) and demand fill rate ([Formula: see text]).
  • Probabilistic forecasts with percentile objectives and a 12-week horizon showed optimal performance.

Conclusions:

  • The ZEOS tool effectively bridges probabilistic forecasting and policy optimization for e-commerce.
  • The approach enhances efficiency, reduces costs, and improves profitability in dynamic retail.
  • This research offers a pioneering solution for complex inventory management challenges.