Salvato in:
| Autori principali: | , , , , |
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| Natura: | Recurso digital |
| Lingua: | |
| Pubblicazione: |
Zenodo
2026
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| Soggetti: | |
| Accesso online: | https://doi.org/10.5281/zenodo.19787216 |
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Sommario:
- 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.