Abstract
Industry 4.0 radically alters manufacturing organization and management, fostering collection and analysis of increasing amounts of data. Advanced data analytics, such as machine learning (ML), are essential for implementing Industry 4.0 and obtaining insights regarding production, better decision support, and enhanced manufacturing quality and sustainability. ML outperforms traditional approaches in many cases, but its complexity leads to unclear bases for decisions. Thus, acceptance of ML and, concomitantly, Industry 4.0, is hindered due to increasing requirements of fairness, accountability, and transparency, especially in sensitive-use cases. ML does not augment organizational knowledge, which is highly desired by manufacturing experts. Causal discovery promises a solution by providing insights on causal relationships that go beyond traditional ML’s statistical dependency. Causal discovery has a theoretical background and been successfully applied in medicine, genetics, and ecology. However, in manufacturing, only experimental and scattered applications are known; no comprehensive overview about how causal discovery can be applied in manufacturing is available. This paper investigates the state and development of research on causal discovery in manufacturing by focusing on motivations for application, common application scenarios and approaches, impacts, and implementation challenges. Based on the structured literature review, four core areas are identified, and a research agenda is proposed.
Highlights
Digitalization, often called as Industry 4.0, is radically changing the way manufacturing is conducted [1]
Machine learning (ML), a subset of artificial intelligence (AI) and a currently dominant method for implementing AI, can address the following three important aspects of data [4]: (1) ML approaches can learn nonlinear and complex relationships; (2) these approaches address the problem of generalization; and (3) these approaches do not impose any restrictions on the input variables and their distributions
These factors and the previously mentioned limiting aspects of traditional ML can be addressed through causal discovery, which goes beyond statistical dependency and focuses on cause-and-effect relationships [30]
Summary
Digitalization, often called as Industry 4.0, is radically changing the way manufacturing is conducted [1]. Digital transformation enables manufacturers to increasingly collect and analyze industrial data from different stages of the manufacturing cycle, from the low-level production engineering processes to the holistic perspective of the lifecycle management processes. Technologies and concepts such as Cyber–Physical Systems, Internet of Things, Blockchain, Additive Manufacturing, and AI have been identified as the core technologies of Industry 4.0 [26]. By nature, some of the tasks in manufacturing domains, such as reliability assessment, are focused on describing and testing causal theories about the system rather than predictive models based on observational data alone [29] These factors and the previously mentioned limiting aspects of traditional ML can be addressed through causal discovery, which goes beyond statistical dependency and focuses on cause-and-effect relationships [30]
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