Industrial big data and artificial intelligence are propelling a new era of manufacturing, smart manufacturing. Although these driving technologies have the capacity to advance the state of the art in manufacturing, it is not trivial to do so. Current benchmarks of quality, conformance, productivity, and innovation in industrial manufacturing have set a very high bar for machine learning algorithms. A new concept has recently appeared to address this challenge: Quality 4.0. This name was derived from the pursuit of performance excellence during these times of potentially disruptive digital transformation. The hype surrounding artificial intelligence has influenced many quality leaders take an interest in deploying a Quality 4.0 initiative. According to recent surveys, however, 80–87% of the big data projects never generate a sustainable solution. Moreover, surveys have indicated that most quality leaders do not have a clear vision about how to create value of out these technologies. In this manuscript, the process monitoring for quality initiative, Quality 4.0, is reviewed. Then four relevant issues are identified (paradigm, project selection, process redesign and relearning problems) that must be understood and addressed for successful implementation. Based on this study, a novel 7-step problem solving strategy is introduced. The proposed strategy increases the likelihood of successfully deploying this Quality 4.0 initiative.

工业大数据和人工智能正在推动制造业的新时代，即智能制造。尽管这些驱动技术具有推动制造技术发展的能力，但这样做并非易事。当前工业制造中质量，一致性，生产率和创新性的基准为机器学习算法设定了很高的标准。最近出现了一个新概念来应对这一挑战：Quality 4.0。这个名称源于在潜在的颠覆性数字转换时代对卓越性能的追求。围绕人工智能的炒作影响了许多质量领导者对部署Quality 4.0的兴趣倡议。但是，根据最近的调查，80-87％的大数据项目从未产生过可持续的解决方案。此外，调查表明，大多数质量负责人对于如何为这些技术创造价值没有清晰的愿景。在此手稿中，回顾了质量计划Quality 4.0的过程监视。然后，确定了四个相关问题（范式，项目选择，流程重新设计和再学习问题），这些问题必须得到理解和解决才能成功实施。在此研究的基础上，提出了一种新颖的七步问题解决策略。建议的策略增加了成功部署此Quality 4.0计划的可能性。