ZEOS Builds Dynamic Inventory Optimization System
ZEOS is developing an AI-driven dynamic inventory optimisation system to provide replenishment recommendations for e-commerce partners, targeting millions of articles and using a 2.5-year history with weekly probabilistic forecasts. The system combines LightGBM via Nixtla MLForecast, Monte Carlo simulations, gradient-free optimisers, and zFlow infrastructure on AWS/Databricks to deliver daily batch and real-time endpoints for partners, reducing stockouts and holding costs.
Key Points
- 1Deploys probabilistic weekly demand forecasts for millions of SKUs using LightGBM and Nixtla MLForecast
- 2Uses Monte Carlo simulations and gradient-free optimisers to optimise replenishment under lead-time and demand uncertainty
- 3Provides daily batch and real-time endpoints via zFlow for partners to plan and adjust inventory
Scoring Rationale
Practical, production-grade architecture and forecasting details drive score; limited research novelty and quantitative outcomes constrain higher impact.
Sources
Public references used for this report.
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