AI-Driven Inventory and Demand Forecasting System with Automated Supplier Communication and Trend Analysis
Fuente:
Zenodo
Enregistré dans:
| Auteurs principaux: | , , , , |
|---|---|
| Format: | Recurso digital |
| Publié: |
Zenodo
2026
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866901346892906496 |
|---|---|
| author | Dr. D. Stalin David N. Siva Virhush V. Ajay H. Kalai Selvan S. Manikandan |
| author_facet | Dr. D. Stalin David N. Siva Virhush V. Ajay H. Kalai Selvan S. Manikandan |
| contents | Abstraction - The project titled "AI-Driven Inventory and Demand Forecasting System with Automated Supplier Communication and Trend Analysis" presents an intelligent stock management solution designed to optimize inventory control and improve supply chain efficiency. The system leverages advanced machine learning models such as XGBoost and Prophet to analyze historical sales data and accurately predict future product demand. To enhance forecasting accuracy, the system integrates real-time trend analysis using external APIs, enabling identification of high-demand products based on market and social media trends. A centralized dashboard provides live visibility of stock levels, forecasts, alerts, and purchase recommendations, supporting data-driven decision-making. The solution also automates supplier communication through email, WhatsApp, and voice calls when stock levels fall below predefined reorder thresholds. By combining predictive analytics, trend monitoring, and automated notifications, the system reduces stockouts, minimizes overstocking, improves operational efficiency, and ensures timely replenishment. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19787216 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | AI-Driven Inventory and Demand Forecasting System with Automated Supplier Communication and Trend Analysis Dr. D. Stalin David N. Siva Virhush V. Ajay H. Kalai Selvan S. Manikandan Demand Forecasting Inventory Management Machine Learning XGBoost Prophet Supply Chain Optimization Predictive Analytics Abstraction - The project titled "AI-Driven Inventory and Demand Forecasting System with Automated Supplier Communication and Trend Analysis" presents an intelligent stock management solution designed to optimize inventory control and improve supply chain efficiency. The system leverages advanced machine learning models such as XGBoost and Prophet to analyze historical sales data and accurately predict future product demand. To enhance forecasting accuracy, the system integrates real-time trend analysis using external APIs, enabling identification of high-demand products based on market and social media trends. A centralized dashboard provides live visibility of stock levels, forecasts, alerts, and purchase recommendations, supporting data-driven decision-making. The solution also automates supplier communication through email, WhatsApp, and voice calls when stock levels fall below predefined reorder thresholds. By combining predictive analytics, trend monitoring, and automated notifications, the system reduces stockouts, minimizes overstocking, improves operational efficiency, and ensures timely replenishment. |
| title | AI-Driven Inventory and Demand Forecasting System with Automated Supplier Communication and Trend Analysis |
| topic | Demand Forecasting Inventory Management Machine Learning XGBoost Prophet Supply Chain Optimization Predictive Analytics |
| url | https://doi.org/10.5281/zenodo.19787216 |