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Decision guidance methodology for sustainable manufacturing using process analytics formalism

Author

Listed:
  • Guodong Shao

    (National Institute of Standards and Technology)

  • Alexander Brodsky

    (George Mason University)

  • Seung-Jun Shin

    (National Institute of Standards and Technology)

  • Duck Bong Kim

    (National Institute of Standards and Technology)

Abstract

Sustainable manufacturing has significant impact on a company’s business performance and competitiveness in today’s world. A growing number of manufacturing industries are initiating efforts to address sustainability issues; however, to achieve a higher level of sustainability, manufacturers need methodologies for formally describing, analyzing, evaluating, and optimizing sustainability performance metrics for manufacturing processes and systems. Currently, such methodologies are missing. This paper introduces a systematic decision-guidance methodology that uses the sustainable process analytics formalism (SPAF) developed at the National Institute of Standards and Technology. The methodology provides step-by-step guidance for users to perform sustainability performance analysis using SPAF, which supports data querying, what-if analysis, and decision optimization for sustainability metrics. Users use data from production, energy management, and a life cycle assessment reference database for modeling and analysis. As an example, a case study of investment planning for energy management systems has been performed to demonstrate the use of the methodology.

Suggested Citation

  • Guodong Shao & Alexander Brodsky & Seung-Jun Shin & Duck Bong Kim, 2017. "Decision guidance methodology for sustainable manufacturing using process analytics formalism," Journal of Intelligent Manufacturing, Springer, vol. 28(2), pages 455-472, February.
    • Handle: RePEc:spr:joinma:v:28:y:2017:i:2:d:10.1007_s10845-014-0995-3
    DOI: 10.1007/s10845-014-0995-3
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    References listed on IDEAS

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    1. Smith, Leigh & Ball, Peter, 2012. "Steps towards sustainable manufacturing through modelling material, energy and waste flows," International Journal of Production Economics, Elsevier, vol. 140(1), pages 227-238.
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    Cited by:

    1. Liang Hou & Roger J. Jiao, 2020. "Data-informed inverse design by product usage information: a review, framework and outlook," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 529-552, March.

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