RetailIQ — AI Demand Forecasting & Inventory Optimization
AI/ML-powered ETL, sales forecasting & inventory optimization for a multi-store retailer
We rebuilt a retail chain’s data platform with AI-powered ETL and machine-learning demand forecasting — lifting sales-forecast accuracy from 62% to 91% and cutting stockouts by 78%.
Outcomes
Measurable results
sales-forecast accuracy (up from 62%)
fewer stockouts on key SKUs
less excess / overstock inventory
The Impact
Before vs After
Real, measured change after moving to an AI/ML-powered data pipeline.
The challenge
Problem
A fast-growing retailer with 40+ stores ran demand planning on spreadsheets fed by messy, siloed data from their POS, ERP and e-commerce systems. Duplicate SKUs, inconsistent product names and missing values meant forecasts were often wrong — leading to frequent stockouts on best-sellers, overstock on slow movers, and analysts losing ~25 hours a week just cleaning data by hand.
Our approach
Solution
TechNexusGen rebuilt the entire data foundation on a modern data stack. We ingested every source into Snowflake, added an AI-powered cleaning stage that automatically deduplicates SKUs, fixes formats and imputes missing values, and modeled clean data with dbt. On top of that clean layer we trained ML demand-forecasting models (XGBoost + time-series) per SKU and store, and turned those forecasts into automated inventory recommendations — reorder points, safety stock and replenishment — all monitored with MLOps so accuracy holds over time.
Key features
- AI-powered ETL with automatic SKU deduplication and cleaning
- Central Snowflake warehouse modeled with dbt
- ML demand forecasting per SKU, store and season
- Automated reorder points, safety stock and replenishment
- Near real-time pipelines with Kafka for live stock levels
- MLOps monitoring, drift detection and scheduled retraining
Tech stack
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