Closing the Gap between Smart Manufacturing Applications and Data Management

Abstract

Smart manufacturing refers to the intensified collaboration of machines, products, and people throughout the manufacturing and the supply chain. This facilitates innovative products, services, business models, and processes. Smart manufacturing is premised on emerging technologies such as cloud computing, mobile computing, the Internet of Things, data analytics, and artificial intelligence. A plethora of companies struggles with the implementation of corresponding applications. In research and practice, we see general data management approaches with primary attention on building architectures that are not tailored to fit a particular domain/ application scenario. However, a robust data management concept is vital, as smart manufacturing decisively depends on data. To address this substantial deficit, we conduct a comprehensive literature review, an expert workshop, and semistructured expert interviews with one of the leading German automotive manufacturers. The result is a catalog of requirements and a framework for data management that fosters the implementation of smart manufacturing applications.

Data Quality Affecting Big Data Analytics in Smart Factories: Research Themes, Issues and Methods

“…Thereafter, Sundarraj et al [28] reviewed different types of data governance in the context of SF for ensuring DQ. Additionally, Emanuel et al [8] identified data management requirements on the SM implementation, including establishing DQ standards and continuous DQ check for the IS integration, based on DQ dimensions. The researchers included DQ management as a component in their proposed data management framework for SM applications.…”

“…We utilised eight databases as the initial resources for the search as advised by relevant review studies in this field [8,9,22,23]. The eight databases used in this review are: ACM Digital Library, ASME Digital Collection, Emerald Insight, IEEE Xplore, ScienceDirect, Scopus, Taylor & Francis Online, and Web of Science.…”

“…As DQ for BDA in the SF context is an essential issue in interdisciplinary research areas such as IS and IM fields [30][31][32] where contributions are published in a wide variety of outlets (see Figure 2), we also used these outlets as sources to conduct a manual search in this SLR. Additionally, we included a list of references provided in related studies on DQ in SF [8,9,23,28], to identify relevant articles that might be missing in our search. To enrich the data sample for analysis, we further used the search engine Google Scholar to conduct backward (namely, identifying new papers from the reference list of the included articles) and forward (namely, finding citations to the articles) reviews [11].…”

“…DQ, referring to both 'fitness for use' and 'conformance to requirements' [7] has been studied during the last three decades, while addressing DQ remains as a significant challenge in performing BDA in the SF context. The field effort has provided support to improve DQ by developing data management frameworks [8] and DQ control tools [9] for BDA. However, this body of research has paid limited attention to defining DQ that is a multidimensional problem.…”