Driven by ongoing migration for Industry 4.0, the increasing adoption of artificial intelligence, big data analytics, cloud computing, Internet of Things, and robotics have empowered smart manufacturing and digital transformation. However, increasing applications of machine learning and data science (DS) techniques present a range of procedural issues including those that involved in data, assumptions, methodologies, and applicable conditions. Each of these issues may increase difficulties for implementation in practice, especially associated with the manufacturing characteristics and domain knowledge. However, little research has been done to examine and resolve related issues systematically. Gaps of existing studies can be traced to the lack of a framework within which the pitfalls involved in implementation procedures can be identified and thus appropriate procedures for employing effective methodologies can be suggested. This study aims to develop a five-phase analytics framework that can facilitate the investigation of pitfalls for intelligent manufacturing and suggest protocols to empower practical applications of the DS methodologies from descriptive and predictive analytics to prescriptive and automating analytics in various contexts.

在工业4.0不断迁移的推动下，人工智能，大数据分析，云计算，物联网和机器人技术的日益普及为智能制造和数字化转型提供了动力。但是，机器学习和数据科学（DS）技术的日益广泛的应用提出了一系列程序问题，包括涉及数据，假设，方法和适用条件的程序问题。这些问题中的每一个都可能增加在实践中实施的难度，尤其是与制造特性和领域知识相关的困难。但是，很少有研究来系统地检查和解决相关问题。现有研究的空白可以归因于缺乏一个框架，在该框架中可以识别实施程序中的陷阱，因此可以建议采用有效方法的适当程序。这项研究旨在开发一个五阶段分析框架，该框架可以促进对智能制造陷阱的调查，并提出协议，以支持DS方法论在各种情况下从描述性和预测性分析到规范性和自动化分析的实际应用。