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Volume 12, Issue 4 (April 2026)

Ai-powered Inventory Management System- Ezze Buy

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7.883
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Volume 12 Issue 07

July 2026

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Author(s)

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

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.

Keywords

Inventory Management LSTM Demand Fore- Casting Flask Machine Learning Supply Chain Predictive An- Alytics Web Application IoT SME Data Analytics Automated Restocking

Paper ID

IJSARTV12I4105205

Publication Date

April 30, 2026

Research Area

Computer Engineering

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