AI Assisted Inventory System
Predictive stock optimization and automated vendor management.
Overview
A Django-based intelligent inventory management system that uses machine learning models to analyze historical inventory data and predict restocking requirements.
The Problem
Businesses frequently suffer overstocking costs or stockouts due to static manual reorder points that ignore historical sales trends.
Research
Studied time-series demand forecasting, automated reorder triggers, and email notification pipelines for vendor management.
The Solution
Built an interactive dashboard featuring ML restocking predictions, automated email alerts for low stock, vendor management, and billing modules.
Architecture
Django application with Machine Learning models (Scikit-Learn), MySQL database, automated SMTP email services, and Bootstrap dashboard UI.
Challenges
- 01Training predictive models on limited historical dataset patterns
- 02Automating background email notifications without blocking web server requests
Results
Proactively alerted managers to upcoming stockouts and optimized vendor ordering schedules.
Lessons Learned
“Combining machine learning predictions with automated workflows yields immediate operational ROI”
“Clear visual data dashboards enable faster decision making”