Industry 4.0 has transformed manufacturing with real-time plant data collection across operations and effective analysis is crucial to unlock the full potential of Internet-of-Things (IoT) sensor data, integrating IoT with Artificial Intelligence (AI) techniques, such as Machine Learning (ML) and Deep Learning (DL). They can provide powerful predictions but anticipating issues is not enough. Manufacturing companies must prioritize avoiding inefficiencies, thereby developing improvement strategies from an Operational Excellence perspective. Here, the interpretability dimension of AI-based models could support a complete understanding of the reasons behind the outcomes, making ML and DL models transparent, and allowing the identification of the causal linkages between the inputs and outputs of the system. Within this context, this study aims first to deliver a comprehensive overview of the existing applications of Advanced Analytics techniques leveraging IoT data in manufacturing environments to then analyze their interpretability implications, referring to the interpretability as the description of the link between the independent and dependent variables in a way that is understandable to humans. Different gaps in terms of lack of full data enhancement are highlighted, providing directions for future research.

中文翻译：

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制造运营中物联网数据的机器学习应用及其可解释性影响：系统文献综述

工业 4.0 通过跨运营的实时工厂数据收集改变了制造业，有效的分析对于释放物联网 (IoT) 传感器数据的全部潜力至关重要，将物联网与机器学习等人工智能 (AI) 技术相集成（ML）和深度学习（DL）。它们可以提供强有力的预测，但仅预测问题是不够的。制造公司必须优先考虑避免效率低下，从而从卓越运营的角度制定改进策略。在这里，基于人工智能的模型的可解释性维度可以支持对结果背后原因的完整理解，使机器学习和深度学习模型透明，并允许识别系统输入和输出之间的因果关系。在此背景下，本研究首先旨在全面概述先进分析技术在制造环境中利用物联网数据的现有应用，然后分析其可解释性影响，将可解释性称为自变量和因变量之间联系的描述以人类可以理解的方式。强调了缺乏全面数据增强方面的不同差距，为未来的研究提供了方向。