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Decision guidance methodology for sustainable manufacturing using process analytics formalism

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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.

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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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Authors and Affiliations

  1. Systems Integration Division, Engineering Laboratory, National Institute of Standards and Technology, 100 Bureau Drive, MS 8260, Gaithersburg, MD, 20899-8260, USA

    Guodong Shao, Seung-Jun Shin & Duck Bong Kim

  2. Department of Computer Science, George Mason University, 4400 University Drive, MS 4A5, Fairfax, VA, 22030-4444, USA

    Alexander Brodsky

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  1. Guodong Shao
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Correspondence to Seung-Jun Shin.

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Shao, G., Brodsky, A., Shin, SJ. et al. Decision guidance methodology for sustainable manufacturing using process analytics formalism. J Intell Manuf 28, 455–472 (2017). https://doi.org/10.1007/s10845-014-0995-3

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