~$1B consumer goods retailer improves demand forecast accuracy and granularity with Ikigai
The retailer's demand forecasts were manual, relied primarily on human intuition, and lacked SKU-level granularity. With a new product launch imminent, they had no historical data to forecast demand or assess cannibalization risk to existing similar SKUs. They also needed guidance on optimal discount rates to balance demand uplift against margin erosion.
Ikigai delivered demand forecasts with 90%+ accuracy and enabled 4 additional levels of granularity, plus new-product demand forecasting and real-time what-if discount scenario analysis.
Frequently asked questions
What did this team achieve with this AI workflow?
Ikigai delivered demand forecasts with 90%+ accuracy and enabled 4 additional levels of granularity, plus new-product demand forecasting and real-time what-if discount scenario analysis.
What tools did this team use?
Ikigai, aiCast.
What results were reported?
Forecast accuracy: 90%+; Additional granularity levels in demand forecasts: 4 (source-reported, not independently verified).
How is this supply chain AI workflow structured?
Core demand forecasting → New product introduction forecast → What-if discount analysis.