Supply chain · Production
Specialty retailer improves forecast accuracy 50%+ with Ikigai demand forecasting
The problem
The retailer's spreadsheet-based forecasting was sub-optimal in accuracy, slow, and inflexible, limiting real-time adaptation to market conditions and leaving no systematic way to plan for new products and new stores that had no historical data.
Workflow diagram · grounded in source
1
AI pattern discovery from data
ai_action
“Built best-in-class demand forecasting using both historical & live data to continuously discover patterns”
2
External event data enrichment
ai_action
“Incorporated external event data to refine forecast precision”
3
NPI similarity modeling
ai_action
“Modeled similarities between existing SKUs & new products to predict demand for new releases without direct historical data”
4
Weekly forecast output
output
“Retailer now able to produce accurate, auditable, and adjustable forecasts that update weekly”
Reported outcome
Ikigai's solution delivered a 50% increase in forecast accuracy, a 15% reduction in overstock, and an 80% decrease in time to forecast.
The VP of Supply Chain stated aiCast outperformed their existing solution right out of the box.
Reported metrics
Forecast accuracy50%
Overstock15%
Time to forecast80%
Reported stack
IkigaiaiCast
Frequently asked questions
What did this team achieve with this AI workflow?
Ikigai's solution delivered a 50% increase in forecast accuracy, a 15% reduction in overstock, and an 80% decrease in time to forecast.
What tools did this team use?
Ikigai, aiCast.
What results were reported?
Forecast accuracy: 50%; Overstock: 15%; Time to forecast: 80% (source-reported, not independently verified).
How is this supply chain AI workflow structured?
AI pattern discovery from data → External event data enrichment → NPI similarity modeling → Weekly forecast output.