Track: Continuous Improvement

Abstract

Digital twins are models that emulate the operations, conduct, and execution of physical products, systems, or processes dynamically in or close to real-time. Their efficacy depends on data from various sources, such as sensors and simulations. Digital twins offer several advantages, including predictive maintenance, minimizing downtime, enhancing the lifespan of machinery, and optimizing production processes. This optimization promotes sustainability in manufacturing by curbing energy consumption, improving material efficiency, and reducing environmental waste. Despite these benefits, digital twin technology has not yet reached its full potential in manufacturing. Therefore, this study highlights how proactive maintenance and process optimization can enhance sustainability outcomes. It also emphasizes the importance of efficiently utilizing robust data analytics, data governance, and modelling techniques. To provide a thorough understanding of this ground-breaking technology, the study undertakes a systematic literature review on the associated implications of using digital twins. The review examines potential opportunities, challenges, and ethical considerations that can enhance the efficacy and sustainability of manufacturing processes.