为什么制造商重新思考工程数据的构建与购买
How Manufacturers Meet the Growing Demand for Engineering Data
每个需要专用软件的制造商最终都会遇到同样的问题：构建它，还是购买它。
在很多领域，这个问题已基本解决：到2026年，很少有组织从头开始构建CRM工具。对于包括工程、CAD和制造业在内的许多领域，许可还是创建定制解决方案的决定远未“解决”。
在本系列中，我们探讨了为什么工程数据已成为战略资产、工程数据瓶颈的成本，以及为什么使用CAD座位通常不是向需要信息的人提供信息的正确方式。最后一篇文章以一个问题结束：一旦制造商决定解决这些问题，他们是自己构建解决方案，还是引入外部技术？本文直接探讨了这个问题。
本文是我们原始报告系列的第四篇预览“ 制造商如何满足工程数据日益增长的需求 ”。这份报告现已发布，您可以 在这里访问它。
查看此博客系列的前三篇文章：
为什么这个决策对工程数据来说看起来不同
虽然构建或购买的问题在企业软件中并不新鲜，但CAD和仿真数据给其带来了自己的复杂性。这些数据以几十种格式存在，嵌入着知识产权，文件大小通常大到通用软件从未打算处理。管理客户记录的系统根本不需要理解几何、容差或网格结构的复杂数据，正如处理工程数据的系统所必须的那样。
这一区别是为什么通常没有现成产品能够完全满足特定制造商需求的重要原因。在其他情况下，系统可能做得过多，使得对于在专门领域运营的组织来说成本过高。这些因素使得制造业、工程和其他类似应用程序的定制开发比大多数业务领域更具吸引力。
购买的理由
尽管拥有定制产品很不错，但对于很多组织来说，购买是合理的。购买通常与构建的方向相反，适用于那些已被充分理解且不会触及工程过程独特性的难题。正确阅读和翻译CAD格式、以可用速度渲染大型装配，或将网格转换为下游工具可以使用的东西，都是现实的技术问题，而这些问题通常已经由供应商解决。
购买提供了一个可靠且已被验证的组件，而不是将内部工程时间花在重新学习别人已经学过的经验教训上，从而释放时间用于真正让产品与众不同的工作。简而言之，购买使组织能够利用解决他们并非首个面临问题的大量资源和市场测试。
两者兼顾的理由：真的有人只选择一方吗？
虽然“构建与购买”是一个很好的标题（我们毕竟也用了它），但如果你与足够多的制造商交谈，公司范围的构建或购买政策开始感觉像是一个神话。更有趣的问题是，这是否在整个行业中确实如此，还是仅仅从外部看起来如此，因为大多数组织不会对外宣传他们构建和许可的部分。
“选择一个方向并不简单。真正的问题在于团队是否将其视为一个整体决策，还是确实在能力上真正权衡，每种方法究竟有多少次落在不同的答案上。”
– Eric Vinchon，Tech Soft 3D产品战略高级总监
这比听起来更难从外部回答，正是这种模式很容易假定且难以在不直接询问制造商的情况下真正验证。
人工智能如何融入其中
因为这是一个2026年的讨论，值得考虑人工智能辅助开发如何影响构建与购买的决策。AI辅助开发使工程团队更容易编写定制软件，这引发了一个明显的问题，是否在构建与购买的计算中，数学发生了根本性的变化。
理解CAD格式、准确保留几何或解释仿真结果仍然需要它始终需要的专业知识，无论是否有AI辅助。更快的代码是否足以显著改变这种平衡，或者只是增加了一个需要权衡的新变量，是值得深入探讨的，不能仅仅假设有答案。
做出正确的决策
这些都没有指出一个普遍答案，这就是重点。那些成功应对这一挑战的制造商往往将其视为一个真正的决定，故意做出并随着需求变化而重新评估，而不是一个他们一次陷入并再也不质疑的默认设置。
那么，制造商实际上是如何决定何时构建和何时购买的呢？学习的最佳方法是直接聆听那些做出这些选择的人。完整报告， 制造商如何满足工程数据日益增长的需求， 现已发布。
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5. AI Could Tip the Balance Toward Building
AI came up as a potential shift in the build-buy balance far more often than Tech-Clarity expected, mostly because of what it’s doing to development itself. AI-assisted coding, or “vibe coding”, lowers the perceived cost of building custom software and expands access to “citizen developers” outside of IT into the process.
An automotive technology strategy leader said AI-driven engineering “could change the whole ecosystem.”
While the development process is changing, those AI-built applications still run into the same 3D data problems that make building hard today: multi-CAD assemblies, kernel formats that vendors update regularly, and PMI and metadata that neutral formats like STEP can lose in translation. The report expects AI coding to raise the importance of SDKs for data translation, visualization, and simulation, since coding agents and copilots can pull them into custom applications to bring them up to commercial grade.
“选择一个方向并不简单。真正的问题在于团队是否将其视为一个整体决策，还是确实在能力上真正权衡，每种方法究竟有多少次落在不同的答案上。”
– Eric Vinchon，Tech Soft 3D产品战略高级总监
AI’s pull isn’t all in one direction. The report cites concerns that AI tools could expose sensitive data or learn from proprietary processes, which may push some participants to build more themselves, while others expect to buy more just to keep up with demand for AI capabilities.
The exact nature of AI effects on software development, the build versus buy debate, and engineering applications as a whole is still murky, but the scale of its impacts will be significant.
What Comes Next for Engineering Data Applications
If AI does make building more common, more of these applications will be written by teams without deep CAD expertise, and more of the weight will fall on the components underneath them. To learn more about some of the best building blocks for engineering software on the market, get in touch with Tech Soft 3D toolkits serving CAD data access, visualization, CAE, AI, and more
The full qualitative report goes further than we have here, with specific examples of what manufacturers have recently chosen to build and to buy, how participants expect the balance to shift, and Tech-Clarity’s recommendations for making the call.
For those interested in exploring what Tech Soft 3D Toolkits and end-user applications can do for you, contact us.
The full report, How Manufacturers Meet the Growing Demand for Engineering Data, is available now.
