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Testing for Data Quality Assessment: ACase Study from the Industry 4.0 Perspective

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Advances in Computational Collective Intelligence (ICCCI 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1463))

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Abstract

Driven by the significant improvement of technologies and applications into smart manufacturing, this paper describes a way of analyzing and evaluating the quality of real-world industrial data. More precisely, it focuses on developing a method for determining the quality of production data and performing analysis of quality in terms of KPIs, such as OEE index and its sub-indicators, i.e. availability, quality rate and efficiency. The main purpose of the work is to propose a method that allows determine the quality of the data used to calculate production efficiency scores. In addition to the requirements imposed upon properly selected measures, we discuss possibilities of verifying the validity and reliability of these sub-indicators in relation to major production losses. The method for data quality assessment, developed in terms of the provided real data gathered from the factory shop-floor monitoring and management systems, was tested for its correctness. Our research has shown that an analysis of the quality of production data can reveal strengths and weaknesses in the production process. Finally, based on our single-unit intrinsic case study results, we discuss results learned on data quality assessment from an industry perspective and provide recommendations in this area.

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Acknowledgments

The initial research for this paper was supported in part by the National Centre for Research and Development under contract no. POIR 01.01.01-00-0687/17-00. The preparation of this paper was funded by the Wrocław University of Science and Technology under block grant no. 8211104160/K45.

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Authors and Affiliations

  1. Department of Applied Informatics, Wroclaw University of Science and Technology, Wroclaw, Poland

    Dariusz Król

  2. Faculty of Computer Science and Management, Wroclaw University of Science and Technology, Wroclaw, Poland

    Tomasz Czarnecki

Authors
  1. Dariusz Król
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  2. Tomasz Czarnecki
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Corresponding author

Correspondence to Dariusz Król .

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Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Krystian Wojtkiewicz

  2. VU Amsterdam, Amsterdam, The Netherlands

    Jan Treur

  3. University of the West of England, Bristol, UK

    Elias Pimenidis

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Marcin Maleszka

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Król, D., Czarnecki, T. (2021). Testing for Data Quality Assessment: ACase Study from the Industry 4.0 Perspective. In: Wojtkiewicz, K., Treur, J., Pimenidis, E., Maleszka, M. (eds) Advances in Computational Collective Intelligence. ICCCI 2021. Communications in Computer and Information Science, vol 1463. Springer, Cham. https://doi.org/10.1007/978-3-030-88113-9_6

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  • DOI: https://doi.org/10.1007/978-3-030-88113-9_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-88112-2

  • Online ISBN: 978-3-030-88113-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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