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Industrial Intelligence: The next growth opportunity for manufacturing

工业智能：制造业的下一个增长机遇

SponsoredProvided byMicrosoft Every manufacturer has its own industrial intelligence. Some is documented, and some resides in the knowledge of experienced workers. Consider an experienced operator preparing to restart a line after a changeover. The machine settings matter, but so does knowing how a particular material behaves, what happened during the previous shift, and when an apparently minor variation deserves a closer look. A procedure can describe the sequence, but successful execution depends on the judgment that comes from experience and the organization’s accumulated know-how. That combination is industrial intelligence: a manufacturer’s accumulated know-how, operating practices, production data, and understanding of its own business. The opportunity now is to make more of that intelligence available wherever decisions are made, then connect it to the physical world of manufacturing. The strategic destination is not more agents, but rather to turn the existing business intelligence into an engine for profitable growth.

每一家制造商都拥有属于自己的工业智能。有些被记录在案，有些则存在于经验丰富的员工的知识储备中。试想一位经验丰富的操作员在换线后准备重启生产线。机器设置固然重要，但了解特定材料的特性、上一班次发生的情况，以及何时需要对看似微小的变化进行仔细检查也同样关键。流程可以描述操作顺序，但成功的执行取决于源于经验的判断和组织积累的专业知识。这种结合就是工业智能：即制造商积累的专业知识、操作实践、生产数据及其对自身业务的理解。现在的机遇在于，无论在何处做出决策，都要让更多的智能发挥作用，并将其与制造业的物理世界连接起来。战略目标不是增加更多的智能体，而是将现有的商业智能转化为盈利增长的引擎。

Individual knowledge can be translated into organizational advantage Expertise has limited reach when it stays with one person, inside one system, or within one plant. Leaders now must determine how to extend that reach without stripping away the context that makes the knowledge useful. For example, an engineer evaluating a process change needs more than a production record. They need to understand the circumstances behind it: the equipment configuration, the quality requirements, and the trade-offs an operator made. Connecting those elements can help people apply experience beyond the place where it originated. When individual knowledge becomes widely accessible, innovation can spread to the maintenance workshop, the planning office, the engineering team, and the production floor. More people can contribute because more people can work with what the organization knows. But access to knowledge is only part of the opportunity. Manufacturing ultimately is physical, and it depends on materials, machines, and products.

个人知识可以转化为组织优势。当专业知识仅停留于个人、单一系统或单一工厂时，其影响力是有限的。领导者现在必须确定如何在不剥离使知识发挥作用的背景信息的前提下，扩大这种影响力。例如，评估工艺变更的工程师需要的不仅仅是生产记录。他们需要了解其背后的情况：设备配置、质量要求以及操作员所做的权衡。将这些要素连接起来，可以帮助人们将经验应用到其起源地之外的地方。当个人知识变得广泛可及，创新就能传播到维修车间、规划办公室、工程团队和生产车间。更多的人可以做出贡献，因为更多的人能够利用组织所掌握的知识。但获取知识只是机遇的一部分。制造业归根结底是物理性的，它依赖于材料、机器和产品。

When intelligence participates in action Physical AI describes systems that can perceive conditions, reason about them, and take action within the physical world. By connecting machines and processes, manufacturers get a clearer view of what is happening on the factory floor. Agents can then reason on top of that information to identify patterns, predict problems, and determine the best course of action. Physical AI brings business intelligence into the physical process, allowing machines and systems to respond to changing conditions. Consider a robotic-handling operation encountering variation in the parts arriving at its station. Vision can help the system assess a part’s position. A model informed by the material and task can help determine an appropriate movement. Connected controls can then execute an adjustment within validated limits. The value is not merely seeing the variation; it is responding appropriately.

当智能参与行动时，“物理人工智能”（Physical AI）描述的是能够感知环境、进行推理并在物理世界中采取行动的系统。通过连接机器和流程，制造商可以更清晰地了解工厂车间正在发生的事情。智能体随后可以在这些信息的基础上进行推理，以识别模式、预测问题并确定最佳行动方案。物理人工智能将商业智能引入物理流程，使机器和系统能够响应不断变化的条件。以机器人搬运作业为例，当其工位接收到的零件出现差异时，视觉系统可以帮助评估零件的位置。基于材料和任务的模型有助于确定适当的动作。随后，互联的控制系统可以在验证的范围内执行调整。其价值不仅仅在于看到差异，而在于做出适当的响应。

The same principle applies to quality operations. When Toyota Industries connected previously fragmented paint shop data, engineering and operations teams were given the context needed to understand defect drivers. The company reports that analysis cycles fell from five days to under four hours, while its pilot reduced relevant paint defects by about 25%. The breakthrough was not simply identifying another signal; it was helping people understand why defects were occurring and respond before the outcome was locked in. This is the type of situation where industrial intelligence has become essential. Data may reveal what is happening, but industrial intelligence provides the context for what should happen, which constraints cannot be breached, and when uncertainty requires human judgment. Simulation offers a way to examine proposed actions before applying them to equipment. Manufacturers can explore how a change might affect a process in a digital environment. That does not remove the need for physical validation, but it creates room to learn without treating the production line as the first experiment.

同样的原则也适用于质量运营。当丰田工业公司（Toyota Industries）将此前碎片化的喷漆车间数据连接起来时，工程和运营团队获得了理解缺陷驱动因素所需的背景信息。该公司报告称，分析周期从五天缩短至不到四小时，其试点项目使相关的喷漆缺陷减少了约25%。这一突破不仅仅是识别了另一个信号，而是帮助人们理解了缺陷产生的原因，并在结果锁定之前做出响应。这就是工业智能变得至关重要的典型情况。数据可以揭示正在发生什么，但工业智能提供了关于“应该发生什么”、哪些约束条件不能逾越，以及何时需要人类判断来应对不确定性的背景信息。仿真提供了一种在将拟议行动应用于设备之前进行检验的方法。制造商可以在数字环境中探索变更可能对流程产生的影响。这并不能消除物理验证的必要性，但它创造了学习空间，而无需将生产线作为第一次实验。

Responsible deployment, therefore, begins with operational discipline. Leaders need to establish clear authority or what systems may change, reliable connections to equipment, defined safety boundaries, and a record of decisions and actions. People must be able to recognize uncertainty, intervene, and bring equipment to a safe state. Trust rests on those practical arrangements, not on confidence in a model alone. More capable machines, more capable people Manufacturing has long combined human ingenuity with increasingly capable tools. Physical AI extends that relationship. Intelligent equipment can take on suitable repetitive or physically demanding tasks, while agent-assisted operations can help people navigate information-intensive decisions and changing conditions. The aim is not to make machines smarter; it is to see what people can build when knowledge can travel, intelligence can act, and ambition has more room to become reality.

因此，负责任的部署始于运营纪律。领导者需要建立明确的权限或变更系统，确保与设备的可靠连接，定义安全边界，并记录决策和行动。人们必须能够识别不确定性、进行干预，并将设备恢复到安全状态。信任建立在这些实际安排之上，而不是仅仅依赖于对模型的信心。更强大的机器，更强大的人才。制造业长期以来一直将人类的智慧与日益强大的工具相结合。物理人工智能扩展了这种关系。智能设备可以承担合适的重复性或体力要求高的任务，而智能体辅助操作可以帮助人们处理信息密集型决策和不断变化的条件。其目的不是让机器变得更聪明，而是要看看当知识可以传播、智能可以行动、雄心壮志有更多空间变为现实时，人们能够创造出什么。

Rockwell Automation illustrates the point. At its Singapore plant, technicians use a maintenance copilot that combines manufacturing data, machine manuals, and knowledge captured from experienced workers. This system has reduced the time required for new workers to learn how to troubleshoot hundreds of machines from nine months to three. An engineer could explore more process alternatives before committing to a change. An operator could help improve the system’s response rather than repeatedly managing the same exception. This type of progress requires investment in equipment but also in people: training, time to learn, and playing a meaningful role in shaping how systems work. Machines become more capable so people can become more capable. Industrial Intelligence is the answer to sustainable growth Industrial intelligence connects what a manufacturer knows: the experience of its people, the signals from its op

罗克韦尔自动化（Rockwell Automation）就说明了这一点。在其新加坡工厂，技术人员使用一种维护副驾驶系统，该系统结合了制造数据、机器手册以及从经验丰富的员工那里获取的知识。该系统将新员工学习如何排除数百台机器故障所需的时间从九个月缩短到了三个月。工程师可以在进行变更之前探索更多的工艺替代方案。操作员可以帮助改进系统的响应，而不是反复处理相同的异常情况。这种进步不仅需要对设备进行投资，还需要对人员进行投资：包括培训、学习时间，以及在塑造系统工作方式方面发挥有意义的作用。机器变得更强大，从而使人变得更强大。工业智能是实现可持续增长的答案。工业智能连接了制造商所掌握的一切：员工的经验、来自其运营的信号。