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Enriching analytics models with domain knowledge for smart manufacturing data analysis

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  • Heng Zhang
  • Utpal Roy
  • Yung-Tsun Tina Lee

Abstract

Today, data analytics plays an important role in Smart Manufacturing decision making. Domain knowledge is very important to support the development of analytics models. However, in today's data analytics projects, domain knowledge is only documented, but not properly captured and integrated with analytics models. This raises problems in interoperability and traceability of the relevant domain knowledge that is used to develop analytics models. To address these problems, this paper proposes a methodology to enrich analytics models with domain knowledge. To illustrate the proposed methodology, a case study is introduced to demonstrate the utilisation of the enriched analytics model to support the development of a Bayesian Network model. The case study shows that the utilisation of an enriched analytics model improves the efficiency in developing the Bayesian Network model.

Suggested Citation

  • Heng Zhang & Utpal Roy & Yung-Tsun Tina Lee, 2020. "Enriching analytics models with domain knowledge for smart manufacturing data analysis," International Journal of Production Research, Taylor & Francis Journals, vol. 58(20), pages 6399-6415, October.
    • Handle: RePEc:taf:tprsxx:v:58:y:2020:i:20:p:6399-6415
    DOI: 10.1080/00207543.2019.1680895
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    Cited by:

    1. Patrick Link & Miltiadis Poursanidis & Jochen Schmid & Rebekka Zache & Martin Kurnatowski & Uwe Teicher & Steffen Ihlenfeldt, 2022. "Capturing and incorporating expert knowledge into machine learning models for quality prediction in manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 2129-2142, October.

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