Decision guidance methodology for sustainable manufacturing using process analytics formalism

Guodong Shao, Alexander Brodsky, Seung-Jun Shin () and Duck Bong Kim
Additional contact information
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

Journal of Intelligent Manufacturing, 2017, vol. 28, issue 2, No 12, 455-472

Abstract: 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 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s10845-014-0995-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:28:y:2017:i:2:d:10.1007_s10845-014-0995-3

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-014-0995-3

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().