Inventrics AI Platform

The problem

Inventrics managed inventory across three warehouses serving the US Midwest. Their purchasing team was making reorder decisions manually — downloading reports, analyzing trends in spreadsheets, and placing orders based on gut feel combined with historical averages. The process worked when the business was smaller. At their current scale, it was breaking down.

Overstock was costing them in carrying costs. Stockouts were costing them in lost sales and emergency shipping premiums. Their purchasing manager was spending 25 hours a week on inventory analysis instead of vendor relationships. And as they opened the third warehouse, they realized the manual approach simply couldn't scale further without another hire.

What we built

We built Inventrics: an AI inventory intelligence platform that integrates with their ERP, analyzes demand patterns across all three warehouses in real time, and generates daily reorder recommendations with confidence levels and supporting rationale.

The system ingests data from their warehouse management system, point-of-sale feeds, supplier lead time records, and seasonal trend data. An ML model trained on three years of their historical inventory and demand data generates SKU-level forecasts on a rolling 30/60/90 day basis.

Purchasing staff review a daily recommendations digest — a clean interface showing which items to reorder, at what quantity, from which supplier, and why. For high-confidence recommendations, they can approve in bulk. Edge cases and unusual patterns are flagged for manual review. The purchasing manager's 25-hour weekly process became a 4-hour review workflow.

Technology

• Python ML pipeline (scikit-learn, Prophet for time-series forecasting)
• React + Next.js dashboard
• PostgreSQL for inventory and forecast data
• ERP integration via REST API and nightly CSV sync
• AWS deployment with scheduled retraining pipeline

Results

In the first six months post-launch: overstock costs dropped 34%, stockouts reduced by 28%, and the purchasing team reclaimed approximately 20 hours per week that previously went to manual analysis. The third warehouse was fully integrated into the system within the first two weeks.

The client extended the engagement to add a supplier performance module — tracking on-time delivery rates and building that into reorder recommendations to automatically shift volume toward better-performing suppliers.