Demand forecasting and inventory optimisation
The problem
Forecasting demand across thousands of stores and millions of SKUs — and keeping shelves stocked without overstocking — is beyond traditional fixed-reorder methods, especially when conditions shift suddenly.
The AI approach
Machine-learning and deep-learning models forecast demand at store-SKU-day granularity, ingesting historical sales, weather, local events, search and promotional signals, and feeding automated replenishment; the system retrains continuously and adapts in real time (e.g. rerouting around weather disruptions).
Evidence it works
Reported outcomes are largely from secondary sources and vary, so they’re best read as directional: materially reduced stockouts, improved inventory turnover and lower logistics costs (commonly cited figures include ~16% fewer stockouts and, separately, up to ~90% inventory accuracy). Walmart has described a generative-AI/agentic fulfilment stack and invested heavily in AI and automation.
What “good” looks like
Fewer stockouts and less overstock/waste, with forecasts that adapt to shocks and replenishment that responds automatically.
Feasibility & cost shape
Large-scale data and infrastructure investment for a retailer of this size; smaller organisations can start with a single category on cloud platforms.
A reminder that forecasting value is unlocked by data integration and granularity, not the algorithm alone — and that “adapts to shocks” matters more than a headline accuracy number.
Based on publicly reported information about the Walmart work.
This is an industry example included for illustration. It is not a Leia Intelligence project, and no client of ours is implied. Figures are as publicly reported by the original parties.
Sources: PYMNTS · SupplyChainBrain · Walmart Global Tech blog · multiple retail-AI case write-ups (figures vary)