A Hybrid LSTM-ARIMA Model to Enhance Inventory Management for Small Enterprises

The major problem small businesses face is proper inventory management, which results in stock out and overstocking, ultimately, in the end, translates into lost income. This research proposes a novel solution: an intuitive AI web app that directly focuses on the problem. The application enables users to leverage state-of-the-art integrations of technologies like Flask, SQLite, and models built using Python and sci-kit-learn. This potent mix provides small business owners with immediate views on sales, revenue, and stock trends that are vital for the continuous growth and expansion of the business. Armed with this data-driven knowledge, they are empowered to make strategic decisions and streamline their stock levels, resulting in big profitability notches through cost reduction and an increase in sales. Such a project can become a groundbreaking one in the field of AI-based inventory management. Offering the businesses a tangible means to raise the level of their activities from an operational to financial perspective, the project promotes performance at the individual level and also at the same time develops the overall vision in the emerging area of study. The successful rollout of the undertaken research would be a decisive factor in enhancing small businesses and paving the way for AI-based inventory management.