内容来源：https://www.technologyreview.com/2025/11/19/1128067/scaling-innovation-in-manufacturing-with-ai/

内容总结：

【制造业迎AI革命 数字孪生技术推动产业智能化升级】
在微软与英伟达的技术支持下，人工智能正为制造业注入全新活力。通过融合数字孪生、云计算、边缘计算和工业物联网等前沿技术，AI助力工厂从被动应对问题转向全系统主动优化，推动生产效率与商业效益双提升。

数字孪生技术通过构建与实体设备、生产线甚至整座工厂完全同步的虚拟模型，使工程师能在虚拟环境中精准模拟、优化真实生产流程。微软制造业与移动出行行业全球首席技术官英德拉尼尔·瑟卡指出：“AI驱动的数字孪生标志着制造业的重大进化，实现了对整个生产线的实时全景洞察，而非仅关注单台设备。”

以灌装生产线为例，数字孪生系统可将车间实时数据、企业二维信息与三维沉浸式建模整合为统一视图，有效减少高达40%的停机损失。工业AI企业Sight CEO乔恩·索贝尔透露，通过对微停滞和质量指标的追踪，企业可精准实施改进措施，在不影响现有运营的前提下挽回数百万美元产能损失。

当前AI应用正加速普及。瑟卡预估近半数制造商已部署AI生产系统，较2024年《麻省理工科技评论》调研的35%实现显著增长。年收入超百亿美元的大型企业中，这一比例更高达77%。索贝尔强调：“制造业海量数据为AI提供了绝佳应用场景，这个曾被视作数字技术追随者的行业，正意外成为AI实践的领跑者。”

（本文由《麻省理工科技评论》定制内容团队独立完成，未经编辑部参与制作）

中文翻译：

赞助内容
人工智能助推制造业创新规模化发展
人工智能的融合应用正使工厂运营迈向现代化，助力制造商收获更卓越的商业成果。

本文由Microsoft与NVIDIA联合策划

制造业正在经历一场深刻的系统升级。当人工智能与数字孪生、云计算、边缘计算及工业物联网等现有技术深度融合，工厂运营团队得以从被动孤立的问题处理模式，转向主动前瞻的全系统优化新阶段。

数字孪生技术——即对设备、生产线、工艺流程乃至整座工厂进行精准物理复刻的虚拟模型——使工作人员能够对复杂的现实环境进行测试、优化与情境化分析。制造商正运用这一技术构建纤毫毕现的工厂环境仿真系统。

"人工智能驱动的数字孪生标志着制造业未来的重大演进，实现了整条生产线的实时可视化监控，而不仅是单台设备。"微软制造业与移动出行行业全球首席技术官Indranil Sircar表示，"这使制造商能够突破局部监测的局限，获取更宏观的洞察。"

以灌装生产线数字孪生为例，该系统可将一维车间遥测数据、二维企业数据与三维沉浸式建模整合为统一的全生产线操作视图，从而提升效率并减少高昂的停机损失。工业人工智能企业Sight Machine联合创始人兼首席执行官Jon Sobel估计，许多高速运转的行业面临高达40%的停机率。这家与微软和英伟达合作的企业专注于将复杂数据转化为可操作的洞察。通过数字孪生追踪微停滞与质量指标，企业能够精准实施改进调整，在不停滞现有运营的前提下挽回以往损失的数百万生产效率。

人工智能开辟了新机遇。Sircar预估目前近半数制造商正在生产环节部署人工智能。据《麻省理工科技评论》洞察2024年度报告显示，这一比例较此前35%的受访企业实现人工智能应用落地有明显提升。该报告指出，年营收超百亿美元的大型制造商进展尤为显著，已有77%部署了人工智能应用场景。

"制造业蕴藏着海量数据，是人工智能应用的绝佳场景。"Sobel坦言，"这个曾被视作数字技术与人工智能领域后进者的行业，或许正占据引领发展的最佳位置。这确实出人意料。"

本内容由《麻省理工科技评论》定制内容部门Insights制作，未经过编辑部采编。所有内容均经由人类作者、编辑、分析师及插画师完成调研、设计与撰写，包括问卷编写及数据收集工作。可能使用的人工智能工具仅限经过严格人工审核的辅助生产流程。

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英文来源：

Sponsored
Scaling innovation in manufacturing with AI
AI integration modernizes factory operations and enables manufacturers to achieve greater business results.
In partnership withMicrosoft and NVIDIA
Manufacturing is getting a major system upgrade. As AI amplifies existing technologies—like digital twins, the cloud, edge computing, and the industrial internet of things (IIoT)—it is enabling factory operations teams to shift from reactive, isolated problem-solving to proactive, systemwide optimization.
Digital twins—physically accurate virtual representations of a piece of equipment, a production line, a process, or even an entire factory—allow workers to test, optimize, and contextualize complex, real-world environments. Manufacturers are using digital twins to simulate factory environments with pinpoint detail.
“AI-powered digital twins mark a major evolution in the future of manufacturing, enabling real-time visualization of the entire production line, not just individual machines,” says Indranil Sircar, global chief technology officer for the manufacturing and mobility industry at Microsoft. “This is allowing manufacturers to move beyond isolated monitoring toward much wider insights.”
A digital twin of a bottling line, for example, can integrate one-dimensional shop-floor telemetry, two-dimensional enterprise data, and three-dimensional immersive modeling into a single operational view of the entire production line to improve efficiency and reduce costly downtime. Many high-speed industries face downtime rates as high as 40%, estimates Jon Sobel, co-founder and chief executive officer of Sight Machine, an industrial AI company that partners with Microsoft and NVIDIA to transform complex data into actionable insights. By tracking micro-stops and quality metrics via digital twins, companies can target improvements and adjustments with greater precision, saving millions in once-lost productivity without disrupting ongoing operations.
AI offers the next opportunity. Sircar estimates that up to 50% of manufacturers are currently deploying AI in production. This is up from 35% of manufacturers surveyed in a 2024 MIT Technology Review Insights report who said they have begun to put AI use cases into production. Larger manufacturers with more than $10 billion in revenue were significantly ahead, with 77% already deploying AI use cases, according to the report.
“Manufacturing has a lot of data and is a perfect use case for AI,” says Sobel. “An industry that has been seen by some as lagging when it comes to digital technology and AI may be in the best position to lead. It’s very unexpected.”
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
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