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Manufacturing data analytics using a virtual factory representation

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  • Sanjay Jain
  • Guodong Shao
  • Seung-Jun Shin

Abstract

Large manufacturers have been using simulation to support decision-making for design and production. However, with the advancement of technologies and the emergence of big data, simulation can be utilised to perform and support data analytics for associated performance gains. This requires not only significant model development expertise, but also huge data collection and analysis efforts. This paper presents an approach within the frameworks of Design Science Research Methodology and prototyping to address the challenge of increasing the use of modelling, simulation and data analytics in manufacturing via reduction of the development effort. The use of manufacturing simulation models is presented as data analytics applications themselves and for supporting other data analytics applications by serving as data generators and as a tool for validation. The virtual factory concept is presented as the vehicle for manufacturing modelling and simulation. Virtual factory goes beyond traditional simulation models of factories to include multi-resolution modelling capabilities and thus allowing analysis at varying levels of detail. A path is proposed for implementation of the virtual factory concept that builds on developments in technologies and standards. A virtual machine prototype is provided as a demonstration of the use of a virtual representation for manufacturing data analytics.

Suggested Citation

  • Sanjay Jain & Guodong Shao & Seung-Jun Shin, 2017. "Manufacturing data analytics using a virtual factory representation," International Journal of Production Research, Taylor & Francis Journals, vol. 55(18), pages 5450-5464, September.
    • Handle: RePEc:taf:tprsxx:v:55:y:2017:i:18:p:5450-5464
    DOI: 10.1080/00207543.2017.1321799
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    Cited by:

    1. Toyosi Ademujimi & Vittaldas Prabhu, 2024. "Model-Driven Bayesian Network Learning for Factory-Level Fault Diagnostics and Resilience," Sustainability, MDPI, vol. 16(2), pages 1-22, January.
    2. Semeraro, Concetta & Aljaghoub, Haya & Abdelkareem, Mohammad Ali & Alami, Abdul Hai & Dassisti, Michele & Olabi, A.G., 2023. "Guidelines for designing a digital twin for Li-ion battery: A reference methodology," Energy, Elsevier, vol. 284(C).
    3. Xin Tong & Qiang Liu & Shiwei Pi & Yao Xiao, 2020. "Real-time machining data application and service based on IMT digital twin," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1113-1132, June.

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