Jämförande analys av AI-baserad och traditionell prognostisering för lageroptimering : En Fallstudie

This study explores AI-based software for inventory optimization in the steel industry through a case study. Using historical sales data, key products were identified via ABC analysis based on revenue and volume. The study compares traditional statistical forecasting methods with an AI-driven approach, assessing their accuracy on both monthly and annual bases over a test period. The AI-based method demonstrated competitive or superior performance in several instances. These forecasts were applied to optimize order quantities and reduce inventory costs, improving overall inventory management, minimizing overstock risks, and enhancing delivery reliability. The findings underscore AI’s potential to enhance inventory management in industrial contexts, providing scalability and sustainability benefits by reducing waste. The study suggests that AI-based solutions can be a valuable tool for long-term efficiency, with broader implications for industries where precise forecasting and cost optimization are essential.