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Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning

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Abstract

One way to improve manufacturing processes is to look at the data and information involved and how this information is put to use (Hicks et al. 2006). As stated by Albinoet al. (2002), the successful coordination of a manufacturing process is mostly based on a successful handling of information to support process management and other tasks involved. With today’s advanced ICT it becomes possible to process, transfer and store large amounts of data and information for a reasonable price (Krcmar 2005). But too much information can be a threat for improved process quality as it can e.g., distract from the main issues/causalities or lead to delayed or wrong conclusions about appropriate actions (Lang 2007). Jansen-Vullers et al. (2003) emphasize the importance of the availability of the right information for quality during manufacturing processes. Hence the question is: What is the right and relevant information in the case of distributed manufacturing process chains and high tech industrial products?

Manufacturing creates wealth

(Prof. Ronald G. Askin, Arizona State University (USA) during the INCOM 2012 Keynote speech Wednesday, May 23, 2012 in Bucharest, Romania).

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Authors and Affiliations

  1. Department of ICT Applications for Production, BIBA—Bremer Institut für Produktion und Logistik GmbH, Bremen, Germany

    Thorsten Wuest

  2. Department of Production Engineering, University of Bremen, Bremen, Germany

    Thorsten Wuest

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  1. Thorsten Wuest
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Wuest, T. (2015). Introduction. In: Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning. Springer Theses. Springer, Cham. https://doi.org/10.1007/978-3-319-17611-6_1

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  • DOI: https://doi.org/10.1007/978-3-319-17611-6_1

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  • Print ISBN: 978-3-319-17610-9

  • Online ISBN: 978-3-319-17611-6

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