Artificial Intelligence applications in traditional steel manufacturers

Abstract

This thesis explores the emerging trends in applying Artificial Intelligence (AI) to traditional steel manufacturing, focusing on how AI can transform key areas like predictive maintenance, process optimization, quality assurance, and supply chain management. By analyzing global trends and case studies, the research examines how AI-driven innovations are improving operational efficiency, reducing downtime, and enhancing product quality while minimizing labor costs. Key objectives include identifying AI’s role in predictive maintenance and process efficiency, evaluating its impact on labor costs and quality retention, and providing a strategic roadmap for steel manufacturers to adopt AI technologies. The study aims to offer actionable insights into the industry's future, with practical recommendations for AI integration, ensuring competitiveness in a rapidly evolving market.