AI Assisted Inventory System
Predictive stock optimization and automated low-stock management.
Overview
A Django-based inventory management system that uses machine learning models to analyze historical inventory data and forecast restocking requirements.
The Problem
Businesses frequently suffer overstocking costs or stockouts due to static manual reorder points that ignore historical sales trends.
Research
Studied demand forecasting concepts, automated reorder triggers, and SMTP email notification pipelines for vendor management.
The Solution
Built an interactive dashboard featuring machine learning (Scikit-learn) restocking predictions, automated email alerts for low stock, vendor management, and billing functionality.
Architecture
Django application with Machine Learning models (Scikit-Learn), MySQL database, automated SMTP email notification services, and Bootstrap dashboard UI.
Challenges
- 01Training predictive models on historical dataset patterns
- 02Automating background email notifications for low-stock threshold triggers
Results
Proactively notified managers of low-stock items and provided ML-based restocking predictions within a central dashboard.
Lessons Learned
“Combining machine learning predictions with automated notification workflows yields practical operational benefits”
“Clear visual data dashboards enable faster decision making”