The article discusses the integration of digital twin technology in product lifecycle management (PLM) to enhance efficiency, intelligence, and sustainability in product design, manufacturing, and service. With the rise of big data and new-generation information technologies, the big data-driven manufacturing era is approaching. However, current research on product lifecycle data mainly focuses on physical products, not virtual models. The lack of convergence between physical and virtual product spaces leads to isolated, fragmented, and stagnant data, which is not useful for manufacturing enterprises. These issues result in low efficiency, intelligence, and sustainability in product lifecycle phases. To address these challenges, converged cyber-physical data from physical and virtual products is needed. The digital twin, an integrated simulation of a product's life, uses physical models, sensor updates, and connected data to mirror the product's life. It enables convergence between physical and virtual spaces, allowing better data management and support for product lifecycle phases. The article proposes a new method for product design, manufacturing, and service driven by digital twin. It investigates the application methods and frameworks of digital twin-driven product lifecycle management and provides three case studies to illustrate its potential applications in the three phases of a product lifecycle. The study emphasizes the importance of digital twin in improving the efficiency, intelligence, and sustainability of product lifecycle management. The article is organized into sections discussing product lifecycle and related data, the concept of digital twin, its industrial applications, and case studies in each phase of the product lifecycle. The study concludes by highlighting the potential of digital twin in enhancing product lifecycle management and pointing out future research directions.The article discusses the integration of digital twin technology in product lifecycle management (PLM) to enhance efficiency, intelligence, and sustainability in product design, manufacturing, and service. With the rise of big data and new-generation information technologies, the big data-driven manufacturing era is approaching. However, current research on product lifecycle data mainly focuses on physical products, not virtual models. The lack of convergence between physical and virtual product spaces leads to isolated, fragmented, and stagnant data, which is not useful for manufacturing enterprises. These issues result in low efficiency, intelligence, and sustainability in product lifecycle phases. To address these challenges, converged cyber-physical data from physical and virtual products is needed. The digital twin, an integrated simulation of a product's life, uses physical models, sensor updates, and connected data to mirror the product's life. It enables convergence between physical and virtual spaces, allowing better data management and support for product lifecycle phases. The article proposes a new method for product design, manufacturing, and service driven by digital twin. It investigates the application methods and frameworks of digital twin-driven product lifecycle management and provides three case studies to illustrate its potential applications in the three phases of a product lifecycle. The study emphasizes the importance of digital twin in improving the efficiency, intelligence, and sustainability of product lifecycle management. The article is organized into sections discussing product lifecycle and related data, the concept of digital twin, its industrial applications, and case studies in each phase of the product lifecycle. The study concludes by highlighting the potential of digital twin in enhancing product lifecycle management and pointing out future research directions.