A Maturity Model for the Classification of RealWorld Applications of Data Analytics in the Manufacturing Environment

Thomas Pschybilla (), Daniel Baumann, Wolf Wenger (), Dirk Wagner, Stephan Manz and Thomas Bauernhansl ()
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Thomas Pschybilla: TRUMPF GmbH + Co. KG
Daniel Baumann: TRUMPF GmbH + Co. KG
Wolf Wenger: Baden-Württemberg Cooperative State University
Dirk Wagner: TRUMPF GmbH + Co. KG
Stephan Manz: TRUMPF GmbH + Co. KG
Thomas Bauernhansl: University of Stuttgart

A chapter in Operations Research Proceedings 2018, 2019, pp 67-73 from Springer

Abstract: Abstract As digitalization continuously gets established in manufacturing, increasing amounts of data are being generated. This change opens up various possibilities to utilize these data to improve production processes by supporting decision-making. Data analytics advances the acquisition of knowledge from data and, thus, improves decision-making in manufacturing and related processes such as maintenance. Identifying the current maturity of data analytics in the manufacturing environment reveals potential and builds the basis for future developments. This paper presents a theory-driven maturity model for the classification of data analytics use cases in the context of data analytics in manufacturing. Furthermore, the model aims to offer a subcategorization of the vast and complex topic of data analytics for manufacturing purposes. The model is applied to an example of Smart Services at TRUMPF GmbH + Co. KG. This case highlights the major potential of predictive data analytics and first ideas towards prescriptive data analytics are presented.

Keywords: Data mining; Predictive analytics; Prescriptive analytics (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:spr:oprchp:978-3-030-18500-8_10

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DOI: 10.1007/978-3-030-18500-8_10

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