StyleCast - Sales Forecast AI
Artificial intelligence, E-Commerce, Retail
Description
Altun Style Co., a fast-growing menswear brand in Turkey, needed a smarter way to manage their seasonal inventory across multiple cities. To support their growth and reduce excess stock, we developed an AI-powered solution that forecasts product demand based on regional trends, seasonal spikes, and historical sales data.
The system, named StyleCast, helped transform their supply chain by offering intelligent demand predictions—ultimately improving sales performance and customer satisfaction.
Project Requirement
- Analyze historical sales and product movement data across 25+ retail locations.
- Incorporate seasonality, local events, and regional climate patterns into demand forecasting.
- Build an internal dashboard for category-level forecast insights and weekly auto-updates.
- Integrate output with existing ERP for inventory automation.
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Client:Altun Style Co.
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Completed:2024-06-30
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Location:Istanbul, Turkey
Project Result
StyleCast helped Altun Style Co. reduce overstock by 22% during Fall/Winter 2023 and increased forecasting accuracy by 31%. The team now makes informed decisions on production and stock movement, with weekly data insights and AI-based recommendations directly in their planning workflow.
- Reduced regional overstock and unsold inventory by 22%.
- Improved forecast accuracy by 31% within the first 2 seasonal cycles.
- Integrated seamlessly into ERP with no disruption to operations.
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