About
Considering each outlet's specifics, we built the AI demand forecasting solution and optimized the volume of goods in the warehouse and the range of goods in different locations. We set up a system that has processed over 8 TB of sales data. These have helped the retail business increase revenue, improve logistics planning, and achieve other business goals.
Challenge, approach, and impact
Fragmented Demand Signals Prevented Accurate Forecasting
The client relied on multiple disconnected data sources for sales, inventory, and promotions, resulting in inconsistent demand signals. Forecasts were built on incomplete and delayed data, making it difficult to accurately predict demand, align inventory with sales, and respond to seasonality or demand spikes. This caused overstock, stockouts, and limited confidence in planning decisions.
How we built
Testimonials
Andrew M.
Luxury Goods Retail
“I think what is really special about the DATAFOREST service is its flexibility, openness, and level of quality and expertise.“
Team structure
Client team
Andrew M
CEO
Project stakeholder
The client stakeholders were working closely with the team at Dataforest
Agency team
2 x Data Engineer
Production
2 x Product Manager
Production
1 x Business Analytics
Production
