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


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.


Decision guidance Process analytics Sustainable manufacturing Optimization Energy consumption 



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.


  1. AMPL. (2011). Using AMPL/MINOS. White paper. Stanford Business Software Inc. Accessed 29 June 2014.
  2. AMPL. (2014). AMPL: A mathematical programming language. Web page. AMPL optimization. Accessed 19 July 2014.
  3. Berglund, J. K., Michaloski, J. L., Leong, S. K., Shao, G., Riddick, F. H., Arinez, J., & Biller, S. (2011). Energy efficiency analysis for a casting production system. In Proceedings of 2011 winter simulation conference, (pp. 1060–1071). Phoenix, USA.Google Scholar
  4. Brodsky, A., & Nash, H. (2005). CoJava: A unified language for simulation and optimization. Principles and practice of constraint programming-CP 2005. Lecture Notes in Computer Science, 3709, 877. doi: 10.1007/11564751_115.
  5. Brodsky, A., & Wang, S. X. (2008a). Decision-guidance management system (DGMS): Seamless integration of data acquisition, leaning, prediction, and optimization. In Proceedings of 41st annual Hawaii international conference on system sciences (pp. 71–81). Waikoloa, USA.Google Scholar
  6. Brodsky, A., Luo, J., & Nash, H. (2008b). CoReJava: Learning functions expressed as object-oriented programs. In Proceedings of international conference on machine learning and applications forum (pp. 368–375). San Diego, USA.Google Scholar
  7. Brodsky, A., Bhot, M., Chandrashekar, M., Egge, N. E., & Wang, X. S. (2009). A decisions query language (DQL): High-level abstraction for mathematical programming over databases. In Proceedings of the 2009 ACM SIGMOD international conference on management of data (pp. 1059–1062). Providence, USA. doi: 10.1145/1559845.1559981.
  8. Brodsky, A., Egge, N., & Wang, X. (2011). Reusing relational queries for intuitive decision optimization. In Proceedings of 44th Hawaii international conference on system sciences. Kauai, USA. doi: 10.1109/HICSS.2011.360.
  9. Brodsky, A., Shao, G., & Riddick, F. (2014). Process analytics formalism for decision guidance in sustainable manufacturing. Journal of Intelligent Manufacturing. doi: 10.1007/s10845-014-0892-9.
  10. DOC. (2014). How does commerce define sustainable manufacturing. Web page. Department of Commerce. Accessed 9 July 2014.
  11. EIA. (2014a). International energy outlook 2013. Web page. Energy Information Administration. Accessed 8 July 2014.
  12. EIA. (2014b). June 2014 monthly energy review. Resource document. Energy Information Administration. Accessed 20 July 2014.
  13. Fujitsu. (2011). Fujitsu offers energy-saving green infrastructure solution. Web page. Fujitsu. Accessed 1 July 2014.
  14. Gill, Philip E., Murray, W., & Saunders, M. (2008). User’s guide for SNOPT version 7: Software for large-scale nonlinear programming. White paper. University of California San Diego. Accessed 9 June 2014.
  15. GM. (2013). Innovation: Environment. Web page. General motors. Accessed 2 July 2014.
  16. Heilala, J., Saija, V., Tonteri, H., Montonen, J., Johansson, B., & Stahre, L. (2008). Simulation-based sustainable manufacturing system design. In Proceedings of 2008 winter simulation conference (pp. 1922–1930). Austin, USA.Google Scholar
  17. IBM. (2014). CPLEX optimizer. Web page. IBM. Accessed 9 July 2014.
  18. ISO/IEC. (2011). Information technology—database languages-SQL—Part 1: Framework (SQL/Framework). Resource document. International Standards Organization and International Electrotechnical Commission. Accessed 18 July 2014.
  19. Johansson, M., Leong, S., Lee, Y., Riddick, F., Shao, G., Johansson, B., & Klingstam, P. (2007). A test implementation of the core manufacturing simulation data specification. In Proceedings of 2007 winter simulation conference (pp. 1673–1681). Washington, DC, USA.Google Scholar
  20. Katherasan, D., Elias, J., Sathiya, P., & Haq, A. N. (2014). Simulation and parameter optimization of flux cored arc welding using artificial neural network and particle swarm optimization algorithm. Journal of Intelligent Manufacturing, 25(6), 67–76. doi: 10.1007/s10845-012-0675-0.CrossRefGoogle Scholar
  21. Kim, D., Shin, S., Shao, G., & Brodsky, A. (2014). A decision guidance framework for sustainability performance analysis of manufacturing processes. NIST interagency/internal report (NISTIR)-7984.Google Scholar
  22. Last, M., Danon, G., Biderman, S., & (2009). Optimizing a batch manufacturing process through interpretable data mining models. Journal of Intelligent Manufacturing, 20(5), 523–534.Google Scholar
  23. Law, A., & Kelton, W. (2000). Simulation modeling and analysis. Boston: McGraw-Hill Education.Google Scholar
  24. Naeem, M. A., Dias, D. J., Tibrewal, R., Chang, R. C., & Tiwari, M. K. (2013). Production planning optimization for manufacturing and remanufacturing system in stochastic environment. Journal of Intelligent Manufacturing, 24(4), 717–728.CrossRefGoogle Scholar
  25. Peng, T., Xu, X., & Wang, L. (2014). A novel energy demand modeling approach for CNC machining based on function blocks. Journal of Manufacturing Systems, 33(1), 196–208.CrossRefGoogle Scholar
  26. Philips. (2012). Philips sustainability statements. Web page. Philps. Accessed 4 July 2014.
  27. Rachuri, S. (2010). Metrics, standards, and infrastructure for sustainable manufacturing. Presentation slides. NIST workshop on sustainable manufacturing. Accessed 28 July 2014.
  28. Ridwan, F., Xu, X., & Liu, G. (2012). A framework for machining optimization based on STEP-NC. Journal of Intelligent Manufacturing, 23(3), 423–441.CrossRefGoogle Scholar
  29. Rockwell Automation. (2014). Capabilities: Sustainable production. Web page. Rockwell automation. Accessed 9 July 2014.
  30. SAIC. (2011). SAIC to present at autovation on utility metering, monitoring, and control systems. Web page. Science Applications International Corporation. Accessed 27 July 2014.
  31. Shao, G., Brodsky, A., Ammann, P., & McLean, C. (2009). Parameter validation using constraint optimization for modeling and simulation. In Proceedings of the industrial simulation conference 2009 (pp. 323–327). Loughborough, UK.Google Scholar
  32. Shao, G., Bengtsson, N., & Johansson, B. (2010). Interoperability for simulation of sustainable manufacturing. In Proceedings of 2010 spring simulation multi-conference, Orlando, USA. doi: 10.1145/1878537.1878595.
  33. Skoog, A. (2009). Methods for input data management: Reducing the time-consumption in discrete event simulation. Gothenburg, Sweden: Chalmers University of Technology.Google Scholar
  34. Smith, L., & Ball, P. (2012). Steps towards sustainable manufacturing through modelling material, energy and waste flows. International Journal of Production Economics, 140(1), 227–238. doi: 10.1016/j.ijpe.2012.01.036.CrossRefGoogle Scholar
  35. SMLC. (2011). Implementing 21st century smart manufacturing. White paper. Smart manufacturing leadership coalition. Accessed 19 July 2014.
  36. Solding, P., Petku, D., & Mardan, N. (2009). Using simulation for more sustainable production systems—methodologies and case studies. International Journal of Sustainable Engineering, 2(2), 111–122. doi: 10.1080/19397030902960994.CrossRefGoogle Scholar
  37. Tari, H. M., & Söderström, M. (2002). Modelling of thermal energy storage in industrial energy systems the method development of MIND. Applied Thermal Engineering, 22(11), 1195–1205. doi: 10.1016/S1359-4311(02)00044-3.CrossRefGoogle Scholar
  38. Wang, G., Wang, Y., & Zhao, J. (2012). Process optimization of the serial-parallel hybrid polishing machine tool based on artificial neural network and genetic algorithm. Journal of Intelligent Manufacturing, 23(3), 365–374.CrossRefGoogle Scholar
  39. Yang, L., Deuse, J., & Jiang, P. (2013). Multi-objective optimization of facility planning for energy intensive companies. Journal of Intelligent Manufacturing, 24(6), 1095–1109.CrossRefGoogle Scholar

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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