在制造现场，几乎所有人都认同一个共识：问题越早发现，代价越低。但现实往往相反——流程表格做 了，评审也开了，问题却依然在量产后集中爆发。很多企业并非不重视风险，而是陷入了一个熟悉的困 境：一方面，FMEA 被认为"非常重要"；另一方面，它又因为工作量巨大、更新困难，逐渐沦为一次性 文件，脱离了实际变化。 设备在换、工艺在调、人员在变、供应商在切，但风险分析却停留在某个历史时点。结果是，真正由变 更引发的问题，反而最容易被忽略。正是在这样的背景下，DRBFM 开始受到关注。它不试图面面俱 到，而是把注意力集中在"发生了什么变化""这些变化可能带来什么偏差"，通过前期策划和有针对性的 讨论，把风险拦截在缺陷发生之前。 本文将结合实践，系统梳理DRBFM 的价值、适用方式以及落地时需要注意的关键问题。 为什么很多 FMEA 最后"看起来很完整，却没什么用" 失效模式及影响分析（FMEA），本来是一种非常有价值的方法。它的初衷，是在问题真正发生之前， 把潜在风险系统地找出来、挡在前面。但在实际工作中，很多企业对 FMEA 的感受却并不理想，常见 的问题主要集中在以下几个方面。 DRBFM，可以理解为一种更务实的风险分 ...

Core Viewpoint - The article discusses the challenges and potential of integrating AI, particularly large language models (LLMs), into the Failure Mode and Effects Analysis (FMEA) process, emphasizing the need for a systematic approach to enhance efficiency while maintaining professional judgment [1][4][12]. Group 1: Challenges in Traditional FMEA - FMEA is often seen as crucial but is cumbersome due to scattered information and reliance on manual analysis, leading to inefficiencies and potential omissions [1][2]. - The traditional FMEA process has not fundamentally changed despite advancements in industry standards, continuing to depend heavily on human analysis and documentation [2][3]. Group 2: AI Integration Potential - New AI technologies, especially LLMs, can efficiently process and organize large volumes of textual information, prompting a reevaluation of whether FMEA must rely solely on human effort [1][2]. - LLMs excel at understanding and structuring complex text, which can alleviate the burden of data organization in FMEA, allowing experts to focus on decision-making [2][4]. Group 3: Systematic Approach for AI + FMEA - A structured methodology is necessary to effectively integrate AI into the FMEA process, ensuring that professional judgment is not compromised while reducing manual workload [4][12]. - The proposed "AI + FMEA framework" breaks down the FMEA process into five clear steps, from information collection to integrating results into existing information systems [5][6]. Group 4: Practical Implementation - Emphasizing the design of information systems is crucial; FMEA should be part of the enterprise knowledge system rather than a one-time task [7][10]. - The framework aims to transform scattered experiences into a sustainable system capability, enhancing FMEA's role as a long-term management tool [7][12]. Group 5: Validation of AI's Effectiveness - The effectiveness of AI in FMEA should be validated through real-world data, such as user comments, to assess its practical value [8][9]. - Initial findings indicate that LLMs can quickly identify potential issues but should not replace expert judgment in final assessments [9][12]. Group 6: Long-term Sustainability - Successful implementation of AI in FMEA requires careful consideration of data security, model training, and ongoing validation in real industrial contexts [12][10]. - The focus should be on how to effectively utilize AI rather than whether to use it, ensuring a clear division of labor between AI and human experts [12][10].