supply_chain · ecommerce · workflow
~$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.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Core demand forecasting
Ikigai generates highly accurate demand forecasts 12 weeks out, filterable by State, Store, Category, and SKU.
Tools used
IkigaiaiCast
Outcome
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.
Results
Volume90%+
Grounding & classification
Source type: vendor customer story
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forecastingpredictive analyticsproduct catalogmetric backedproduction runtime claimedtools describedworkflow describedretailaccuracy improvementcost reductionvendor customer storysupply chain