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Journal of Intelligent Manufacturing

, Volume 28, Issue 2, pp 455–472 | Cite as

Decision guidance methodology for sustainable manufacturing using process analytics formalism

  • Guodong Shao
  • Alexander Brodsky
  • Seung-Jun Shin
  • Duck Bong Kim
Article

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.

Keywords

Decision guidance Process analytics Sustainable manufacturing Optimization Energy consumption 

Notes

Acknowledgments

The authors thank NIST Sustainable Manufacturing program testbed project team for the test case discussion, especially Program Manager, Sudarsan Rachuri, for his valuable input and Abdullah Alrazgan, a graduate student from George Mason University, for his effort on SPAF compiler development. The work represented here was partially funded through cooperative agreement #70NANB12H277 between George Mason University and NIST.

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Copyright information

© Springer Science+Business Media New York (outside the USA) 2014

Authors and Affiliations

  • Guodong Shao
    • 1
  • Alexander Brodsky
    • 2
  • Seung-Jun Shin
    • 1
  • Duck Bong Kim
    • 1
  1. 1.Systems Integration Division, Engineering LaboratoryNational Institute of Standards and TechnologyGaithersburgUSA
  2. 2.Department of Computer ScienceGeorge Mason UniversityFairfaxUSA

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