Abstract:
SmartStock AI addresses the common problem of inefficient inventory management, which leads to overstocking or stockouts, both of which can negatively impact a business's operations and customer satisfaction. The need for accurate, dynamic inventory control has never been more crucial, as businesses strive to reduce costs while improving service quality.
The solution developed in this project, SmartStock AI, combines web scraping and machine learning to automate inventory management. By scraping real-time market data, the system forecasts product demand and provides reorder alerts based on predicted shortages. This helps businesses maintain optimal stock levels without manual intervention. Built with Python, Flask, BeautifulSoup for web scraping, scikit-learn for machine learning, and SQLite for data storage, SmartStock AI seamlessly integrates with existing stock systems.
Through the implementation of the RandomForestRegressor machine learning model, the system accurately predicts future demand for various stock items. This enables businesses to automate the process of restocking and better align their inventory with market demand, reducing both excess stock and shortages. The forecasting mechanism is simple yet effective, offering businesses an efficient way to manage inventory levels.
SmartStock AI is important because it empowers businesses to optimize their inventory management using data-driven insights, ultimately reducing operational costs and enhancing customer satisfaction by ensuring products are always available when needed. This system offers a practical, scalable solution to inventory management problems for businesses of all sizes.
