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Author(s):

Prof. Shreyas Shinde | Mr. Rushikesh More | Mr. Vivek Bolave | Mr. Shrinivas Ghodake | Ms. Marwa Ansari
Keywords:

Inventory Management, LSTM, Demand Fore- Casting, Flask, Machine Learning, Supply Chain, Predictive An- Alytics, Web Application, IoT, SME, Data Analytics, Automated Restocking
Abstract:
This Paper Presents The Design, Architecture, And Full-stack Implementation Of EzzeBuy, A Web-based AI-powered Inventory Management And Sales Prediction Platform. The System Integrates A Long Short-Term Memory (LSTM) Neural Network Backend For Dynamic Sales Forecasting, A Flask-based REST API For Inventory Operations, CSV-driven Data Ingestion With Drag-and-drop Support, A Data Layer Managed Using Pandas And CSV Persistence, And A Responsive Frontend Built With HTML5, CSS3, And JavaScript. The Platform Supports Real- Time Dashboard KPI Tracking, Low-stock And Near-expiry Alerting, Product-level Analytics, And AI-powered Demand Forecasting With Configurable Prediction Horizons. The Proposed Architecture Provides A Reproducible Foundation For Developing Scalable AI- Enabled Inventory Management Platforms Suitable For Small And Medium Enterprises.