Predicting Demand Before It Happens — AI for Smarter Inventory
[D2C Retail Brand] — 500+ SKUs, 3 warehouses, nationwide shipping
THE CHALLENGE
A fast-growing D2C brand was losing revenue to both stockouts and excess inventory. Their manual forecasting process relied on spreadsheets and gut feel, and it could not keep up with seasonal demand shifts, promotional impacts, and rapid SKU expansion. They were overstocking slow movers while running out of best-sellers during peak periods.
OUR APPROACH
We built a custom ML forecasting engine that integrated with the client's Shopify store, marketing calendar, warehouse management system, and external data signals (weather, economic indicators, social trends). The system provides daily demand predictions at the SKU level with automated reorder recommendations and alerts for demand anomalies.
TECHNICAL HIGHLIGHTS
- Ensemble ML model combining gradient boosting, LSTM networks, and seasonal decomposition
- Integration with Shopify, ShipBob, and Google Ads APIs
- SKU-level daily forecasts with 91% accuracy at 14-day horizon
- Automated reorder point calculation and purchase order recommendations
- Promotional uplift modeling that factors in ad spend and discount depth
The Results
- 34% reduction in inventory carrying costs
- 60% fewer stockouts during peak seasons
- $420K in annualized savings from inventory optimization
- ROI achieved within 8 weeks of deployment
- System now processes 500+ SKU forecasts daily with zero manual intervention
TECHNOLOGIES USED
- Python
- scikit-learn
- TensorFlow
- Shopify API
- Airflow
- PostgreSQL
- Metabase
- AWS Lambda