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Erschienen in:
Industrial Knowledge Management Tools Applied to Engineering Education Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

On Applicability of Big Data Analytics in the Closed-Loop Product Lifecycle: Integration of CRISP-DM Standard

verfasst von : Elaheh Gholamzadeh Nabati, Klaus-Dieter Thoben

Erschienen in: Product Lifecycle Management for Digital Transformation of Industries

Verlag: Springer International Publishing

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Abstract

The product use data can have an important role in closed-loop product lifecycle management (CL-PLM), where information feedbacks from the use data can contribute to improve the product design and performance. The product usage data can nowadays be collected easier than before, with the aid of sensors and technologies embedded in products. However, the collected data can have complex characteristics. They come from various sources, have different formats and high volume. In order to improve the product lifecycle processes with these data, discussing the use of data analysis in the product lifecycle is necessary. Analyzing the data with such characteristics has been also considered in the context of big data analytics. In this paper an approach for standardization of the process of usage data analysis based on a standard called Cross Industry Standard Process for Data Mining (CRISP-DM), is introduced and its potential integration in CL-PLM is investigated. The reference steps of analyzing usage data are identified. They cover the processes between data generation until feeding back the knowledge of use to the product design phase.

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Vorheriges Kapitel Comparing PLM and BIM from the Product Structure Standpoint
Nächstes Kapitel Big Data Analytics as Input for Problem Definition and Idea Generation in Technological Design
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Metadaten
Titel
On Applicability of Big Data Analytics in the Closed-Loop Product Lifecycle: Integration of CRISP-DM Standard
verfasst von
Elaheh Gholamzadeh Nabati
Klaus-Dieter Thoben
Copyright-Jahr
2016
Buch
Product Lifecycle Management for Digital Transformation of Industries

Print ISBN: 978-3-319-54659-9

Electronic ISBN: 978-3-319-54660-5

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-54660-5_41

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