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ZEOS Inventory Optimisation System

ZEOS Inventory Optimisation System is the umbrella name this wiki gives to Zalando ZEOS's AI-driven replenishment recommendation product — a two-pipeline system that produces probabilistic 12-week demand forecasts for 5 million SKUs and feeds them into a Monte Carlo + black-box-optimiser inventory engine that recommends what to stock, when to replenish, and where in a multi-echelon warehouse network. Exposed to Zalando B2B partners through the partner portal with both daily batch and real-time interactive endpoints.

ZEOS is Zalando's B2B logistics & e-commerce service (zeos.eu); the inventory-optimisation system documented here is one of the AI-driven tools ZEOS offers partners alongside fulfillment, logistics, and storefront services.

Role

Two-stage replenishment decision pipeline:

  1. Demand Forecaster — weekly pipeline producing probabilistic 12-week forecasts per (article_id, merchant_id, week) over 3 years of sliding-window history, 5M SKUs, end-to-end under 2 hours.

  2. Replenishment Recommender — daily batch + real-time online optimiser consuming the forecast + per-SKU inventory state, pricing, cost factors, lead times; uses Monte Carlo simulation + gradient-free black-box optimisation.

Cost optimisation objective (from the post)

$$Min\ Costs(\theta) = C_{storage}(\theta) + C_{lost\ sales}(\theta) + C_{overstock}(\theta) + C_{operations}(\theta) + C_{inbound}(\theta)$$

Find replenishment decisions θ* that:

  • Reduce stockouts to avoid lost-sales cost.
  • Limit inventory in warehouses to reduce stock-holding cost.
  • Balance long-term overstock cost vs short-term lost-sales cost.
  • Satisfy operational constraints (lead times, desired review frequency).
  • Capture the stochastic nature of demand / lead times via Monte Carlo simulation.

Platform substrate

Both constituent pipelines are implemented on zFlow, Zalando's internal ML platform. zFlow:

Delivery to partners

Partners interact via the Zalando B2B partner portal, which renders:

  • Holistic inventory-health metrics and KPIs.
  • Daily batch replenishment reports covering all of the partner's articles.
  • Interactive / online optimisation — partners can mutate inventory settings and re-score their catalog on the fly. See concepts/partner-portal-interactive-planning.

Canonical disclosure (2025-06-29)

See sources/2025-06-29-zalando-building-a-dynamic-inventory-optimisation-system-a-deep-dive for the full architectural disclosure. Key quote on the two-pipeline framing:

"We break the inventory optimisation problem into two isolated but connected building blocks: Demand Forecast and Inventory Optimisation."

Key quote on scale:

"Our weekly forecasting pipeline processes 3 years of historical data for 5 million SKUs (size and colour) using a sliding window approach, and takes less than 2 hours."

Positioning on the wiki

Seen in

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