Case Study: Element AI achieves better retail demand forecasting and shelf replenishment with AnyLogic

Tackling Retail Out-of-Stock with AI

Element AI wanted to explore how simulation could support AI development, especially for retail challenges like out-of-stock prevention and shelf replenishment. With help from AnyLogic, the company examined whether simulation could generate training data, improve AI-driven agent behavior, and support future reinforcement learning use cases.

AnyLogic helped Element AI build a grocery store simulation model that generated five years of minute-by-minute demand data and tested employee task-prioritization policies for restocking. The results showed AI-based forecasting could improve next-hour demand prediction accuracy from 61% with a lag baseline to up to 80%, while also giving Element AI a testbed for comparing restocking strategies and evaluating KPIs such as revenue, wait times, and out-of-stock events.

Element AI