Digital Twin for Maintenance and Smart Manufacturing: The Mediating Role of Replacement Maintenance in the Saudi Industrial Sector

Objectives: This study examines the role of digital twin technology in optimizing maintenance within smart manufacturing. It explores the mediating effect of replacement maintenance on the relationship between digital twins and smart manufacturing outcomes in the Saudi industrial sector, focusing on energy efficiency, waste reduction, and operational performance. Theoretical Framework: Grounded in Industry 4.0 principles, this research highlights how digital twin technology enhances predictive maintenance and resource optimization. It aligns with Saudi Arabia’s Vision 2030 by promoting advanced manufacturing solutions. Method: A quantitative approach is employed using structural equation modeling (SEM) to analyze data from 21 leading Saudi food industry companies, including Almarai, Savola, Al Mangal Foods, and Wataniya Poultry. The study evaluates digital twin-enabled maintenance strategies and their impact on manufacturing performance. Results and Discussion: The findings reveal that replacement maintenance partially mediates the relationship between digital twins and smart manufacturing. Digital twin technology enhances predictive maintenance, reduces downtime, and optimizes resource use, leading to improved energy efficiency and waste reduction. These outcomes support the broader adoption of Industry 4.0 technologies. Research Implications: The study underscores the need for further exploration of AI-driven analytics and deeper digital twin integration in smart manufacturing to improve efficiency and sustainability. Originality/Value: This research provides empirical evidence on the mediating role of replacement maintenance, offering valuable insights for businesses aiming to enhance production efficiency and align with Vision 2030 initiatives.