IKEA lifts demand forecast acceptance to 98% with AI-powered Demand Sensing tool

Managing demand planning across 450+ stores and e-commerce operations in 54 markets means IKEA forecasts potentially billions of individual product needs each year. For a retailer of this scale, forecast accuracy is directly tied to customer satisfaction and operating cost. The legacy statistical system generated forecasts top-down — starting at global level and disaggregating to country and store — relying on historical sales patterns. This approach struggled to capture localised demand signals. Inaccurate forecasting risks overstock, which drives up costs, as well as too few products in stores, which means longer waits for customers. In the past, roughly 8% of forecasts required manual correction.

IKEA built Demand Sensing, an internally developed AI forecasting platform described as set to be its largest supply chain AI deployment. The system applies artificial intelligence to up to 200 data sources per product — a substantial expansion beyond historical sales data. Inputs include weather forecasts, festival and holiday buying patterns, salary-cycle purchasing effects, and seasonal demand patterns. Critically, the architecture inverts the legacy model: rather than disaggregating global forecasts downward, Demand Sensing constructs forecasts bottom-up from individual store level, then aggregates to market, country, region, and global views. The platform covers both in-store and online channels, with a flexible forecast horizon running from daily resolution out to four months. The tool was rolled out in Norway.

Demand Sensing delivered a measurable step-change in forecast quality. The headline outcome was a forecast acceptance rate rising from 92% to 98%, with the manual override rate falling from 8% to 2%. Beyond the headline numbers, the bottom-up architecture resolved a specific accuracy failure in the legacy system: a localised sales spike — for example in the Furuset store in Oslo — previously propagated upward and inflated national forecasts incorrectly. Under Demand Sensing, that signal stays scoped to the relevant store. Qualitative outcomes include reduced emergency markdowns on overstocked product, lower logistics waste, and a leaner planning workflow for supply chain teams. Cost savings from improved inventory accuracy are designed to flow through to end-customer pricing.

• A bottom-up, store-level forecast architecture prevents local demand noise from contaminating regional or national planning — a structural fix that statistical disaggregation cannot replicate.
• Integrating 200+ external data sources (weather, festivals, salary cycles) allows the model to anticipate demand shifts that purely transactional history will always lag.
• Omnichannel signal unification — treating online and in-store demand as a single input — is a prerequisite for accurate planning in modern retail; siloed channel forecasts create inventory mismatches.
• Piloting in a single market before global rollout reduces deployment risk and allows the model to be validated against a known baseline before scaling.
• Reducing manual overrides from 8% to 2% is not just an efficiency gain — it also removes planner-introduced inconsistency from the forecast, improving downstream supply chain decisions.