The paper "Digital Twin-Driven Product Design, Manufacturing, and Service with Big Data" by Fei Tao, Jiangfeng Cheng, Qinglin Qi, Meng Zhang, He Zhang, and Fangyuan Sui explores the integration of digital twins and big data to enhance product lifecycle management (PLM). The authors highlight the current limitations in PLM, such as a focus on physical products rather than virtual models, the lack of convergence between physical and virtual spaces, and the isolation and fragmentation of data. These issues lead to inefficiencies, lack of intelligence, and sustainability challenges in product design, manufacturing, and service. To address these problems, the paper proposes a new method that leverages digital twins to create converged cyber-physical data, which can better support the entire product lifecycle. Digital twins are described as integrated multi-physics, multi-scale, and probabilistic simulations that mirror the life of a product, using physical models, sensor updates, and other data. The paper discusses the concept of digital twins and their industrial applications, and investigates their potential in three key phases of the product lifecycle: design, manufacturing, and service. The authors also provide three case studies to illustrate the future applications of digital twins in each phase, demonstrating how this approach can drive more efficient, intelligent, and sustainable product design, manufacturing, and service. The paper concludes by outlining future research directions and emphasizing the importance of integrating physical and virtual data to enhance PLM.The paper "Digital Twin-Driven Product Design, Manufacturing, and Service with Big Data" by Fei Tao, Jiangfeng Cheng, Qinglin Qi, Meng Zhang, He Zhang, and Fangyuan Sui explores the integration of digital twins and big data to enhance product lifecycle management (PLM). The authors highlight the current limitations in PLM, such as a focus on physical products rather than virtual models, the lack of convergence between physical and virtual spaces, and the isolation and fragmentation of data. These issues lead to inefficiencies, lack of intelligence, and sustainability challenges in product design, manufacturing, and service. To address these problems, the paper proposes a new method that leverages digital twins to create converged cyber-physical data, which can better support the entire product lifecycle. Digital twins are described as integrated multi-physics, multi-scale, and probabilistic simulations that mirror the life of a product, using physical models, sensor updates, and other data. The paper discusses the concept of digital twins and their industrial applications, and investigates their potential in three key phases of the product lifecycle: design, manufacturing, and service. The authors also provide three case studies to illustrate the future applications of digital twins in each phase, demonstrating how this approach can drive more efficient, intelligent, and sustainable product design, manufacturing, and service. The paper concludes by outlining future research directions and emphasizing the importance of integrating physical and virtual data to enhance PLM.