Abstract
The digital transformation generates huge amounts of heterogeneous data across the industrial value chain, from simulation data in engineering, over sensor data in manufacturing to telemetry data on product use. Extracting insights from these data constitutes a critical success factor for industrial enterprises, e. g., to optimize processes and enhance product features. This is referred to as industrial analytics, i. e., data analytics for industrial value creation. Industrial analytics is an interdisciplinary subject area between data science and industrial engineering and is at the core of Industry 4.0. Yet, existing literature on industrial analytics is fragmented and specialized. To address this issue, this paper presents a holistic overview of the field of industrial analytics integrating both current research as well as industry experiences on real-world industrial analytics projects. We define key terms, describe typical use cases and discuss characteristics of industrial analytics. Moreover, we present a conceptual framework for industrial analytics that structures essential elements, e. g., data platforms and data roles. Finally, we conclude and highlight future research directions.
About the author
Dr.-Ing. Christoph Gröger studied information systems at the Universities of Stuttgart and Manchester. He completed his doctorate in computer science on the topic of “advanced manufacturing analytics” at the Graduate School of Excellence advanced Manufacturing Engineering (GSaME) at the University of Stuttgart. Currently, Christoph is enterprise architect for data analytics at Bosch and a senior technical professional in Bosch’s global data strategy team. His field of expertise is industrial analytics with a current focus on data platforms and data strategies. For his achievements in the area of industrial analytics, he was awarded junior fellow of the German Informatics Society (GI) in 2020.
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