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Warehousing Manufacturing Data

A Holistic Process Warehouse for Advanced Manufacturing Analytics

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Data Warehousing and Knowledge Discovery (DaWaK 2012)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7448))

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Abstract

Strong competition in the manufacturing industry makes efficient and effective manufacturing processes a critical success factor. However, existing warehousing and analytics approaches in manufacturing are coined by substantial shortcomings, significantly preventing comprehensive process improvement. Especially, they miss a holistic data base integrating operational and process data, e. g., from Manufacturing Execution and Enterprise Resource Planning systems. To address this challenge, we introduce the Manufacturing Warehouse, a concept for a holistic manufacturing-specific process warehouse as central part of the overall Advanced Manufacturing Analytics Platform. We define a manufacturing process meta model and deduce a universal warehouse model. In addition, we develop a procedure for its instantiation and the integration of concrete source data. Finally, we describe a first proof of concept based on a prototypical implementation.

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Author information

Authors and Affiliations

  1. Institute of Parallel and Distributed Systems, University of Stuttgart, Universitätsstrasse 38, 70569, Stuttgart, Germany

    Christoph Gröger, Johannes Schlaudraff, Florian Niedermann & Bernhard Mitschang

Authors
  1. Christoph Gröger
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  2. Johannes Schlaudraff
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  3. Florian Niedermann
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  4. Bernhard Mitschang
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Editor information

Editors and Affiliations

  1. ICAR-CNR and University of Calabria, via P. Bucci 41C, 87036, Rende (CS), Italy

    Alfredo Cuzzocrea

  2. Hewlett Packard Labs, 1501 Page Mill Road, MS 1142, 94304, Palo Alto, CA, USA

    Umeshwar Dayal

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© 2012 Springer-Verlag Berlin Heidelberg

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Gröger, C., Schlaudraff, J., Niedermann, F., Mitschang, B. (2012). Warehousing Manufacturing Data. In: Cuzzocrea, A., Dayal, U. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2012. Lecture Notes in Computer Science, vol 7448. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32584-7_12

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  • DOI: https://doi.org/10.1007/978-3-642-32584-7_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32583-0

  • Online ISBN: 978-3-642-32584-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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