Case Study – D2C Retail Startup

Optimising inventory with demand forecasting and conversational assistance

Series C direct‑to‑consumer retailer with multiple fulfilment centres

Client

Series C direct‑to‑consumer retailer with multiple fulfilment centres

Timeframe

Deployed in 8 weeks

Impact

Stock‑outs
–25%
Gross margin
+10%
Incremental annual revenue
10%

Need

Rapid growth strained the startup's supply chain. Forecasts generated in spreadsheets missed local promotions, leading to frequent stock‑outs and markdowns.

Our Build

Our team built a forecasting engine that blends historical sales with real‑time signals (events, weather, marketing) and surfaces recommendations through a chat‑based assistant. Store and warehouse managers ask "How much should I order?" and receive narrative guidance. Automated replenishment workflows trigger purchase orders and allocate inventory across the network.

Outcome

Stock‑outs fell by 25% and gross margin improved by 10%. The retailer captured an estimated US$8 million in additional annual revenue while freeing managers to focus on customer experience.

"The AI assistant tells us what to order and why; it's like having a planning team in our pocket."
Operations lead, D2C retailer

Key Features Delivered

Smart Forecasting Engine

  • • Historical sales pattern analysis
  • • Real‑time event and weather integration
  • • Marketing campaign impact modeling
  • • Multi‑location demand prediction

Conversational Assistant

  • • Natural language inventory queries
  • • Personalized ordering recommendations
  • • Real‑time inventory status updates
  • • Automated purchase order generation

Ready to Optimize Your Inventory Management?

Learn how LayeredAI can help your retail business reduce stock‑outs and increase profitability with intelligent forecasting.