92%的企业卡在半路上：数字孪生为什么难落地？

Core Insights - Digital twin technology is evolving from a technical concept to a core capability in complex manufacturing industries such as aerospace, automotive, and consumer electronics, addressing challenges like product complexity, market speed, and sustainability pressures [1][3] - A significant gap exists between the ideal and reality of digital twin implementation, with only about 8% of companies achieving deep integration of digital twins across product lifecycle and production processes, while 92% remain at a superficial visualization stage [1][3] Group 1: Digital Twin Misconceptions - Many companies mistakenly view digital twins as advanced 3D graphics rather than as a comprehensive system that encompasses design, simulation, verification, optimization, manufacturing, and service [3][8] - The lack of integration leads to isolated systems and fragmented data, hindering the ability to support a closed-loop collaboration from design to manufacturing and operation [1][3] Group 2: Structural Challenges - The traditional manufacturing process often identifies issues late, resulting in rework, delays, and increased costs; digital twins allow for early identification of potential risks through virtual testing [3][4] - Companies face structural challenges in implementing digital twins, as many operate with disconnected material lists (BOMs) across different domains, leading to inefficiencies and errors [8][9] Group 3: Siemens' Approach - Siemens' Nanjing factory exemplifies the successful application of digital twins, where the planning and construction processes were completed within a digital twin environment, allowing for real-time data feedback and continuous performance improvement [4][11] - The concept of a "digital thread" is crucial for integrating tools, data, processes, and systems, enabling seamless collaboration across design, simulation, manufacturing, and service [9][11] Group 4: Execution Engine - To convert data into business value, companies need an "execution engine" to support the entire process of modeling, simulation, testing, and feedback, with Siemens' Simcenter playing a key role in this aspect [14][15] - Simcenter integrates engineering simulation, performance prediction, and virtual validation, allowing for predictive verification and real-time feedback throughout the product lifecycle [15][17] Group 5: Real-World Applications - Digital twins have been successfully applied in various industries, such as battery manufacturing, where optimization led to a 22% improvement in cooling performance and a 50% reduction in design time [17] - In the automotive sector, companies like VinFast have demonstrated rapid adaptability, achieving a production capacity increase to 55,000 units per month through Siemens' digital twin solutions [18] Group 6: Future of Digital Twins - Comprehensive digital twins represent a new production logic and organizational capability, reshaping product design paradigms and defining core competitiveness for future industrial enterprises [19][20] - The transition from "tool stacking" to "system restructuring" signifies that the future belongs to organizations that can leverage data-driven evolution and simulation-supported decision-making [19]