Case Study · 2024
AI-Powered Demand Forecasting
TensorFlow models forecast product demand from sales and inventory history — served through Django REST and a React dashboard.
Context
Ops had sales and inventory history, not a forecast. They needed a model, an API, and a screen.
The Problem
Without a forecast, restocking is a gut call. Gut calls don't scale across a catalogue.
Process
Prepare the series
Python data-preparation pipelines from historical sales and inventory.
Infer behind REST
TensorFlow inference services exposed through Django REST for downstream apps.
Make the forecast readable
React dashboard for demand trends, model outputs, and product-level insights.
Decisions
The model is not the product
Without an API and a dashboard, a notebook stays a notebook. Inference had to live in Django.
The grain is the product
A global forecast doesn't help the buyer. Each SKU gets its own curve.
Outcome
✦ Demand forecasts served in production via Django REST
✦ React dashboard for trends and product-level insights
Reflection
“A model in production is not an .h5 file — it's an API someone opens on Monday morning.”