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A decision-guidance framework for sustainability performance analysis of manufacturing processes

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Abstract

Life cycle assessment (LCA) frameworks are widely used to assess the sustainability of manufacturing processes. Although they have several advantages such as systematic estimation and efficiency, they have significant limitations due to a lack of functionality to perform sustainability analysis. Specifically, they do not fully support dynamic and diverse characteristics of manufacturing processes nor cover technical details for the further analysis, such as simulation, prediction, and optimization. In addition, they do not provide a unified modeling environment in which to perform various sustainability analysis tasks. In this paper, a decision-guidance framework has been presented to improve sustainability in manufacturing processes while addressing the deficiencies in existing LCA frameworks. The proposed framework consists of six phases: goal and scope definition, data collection, model generation, sustainability performance analysis, interpretation, and decision support and guidance, which is designed in terms of functionality, usability, flexibility/reusability, and interoperability. To demonstrate the use of the framework, a case study of a turning process has been performed.

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References

  1. Bordt M (2009) Presentation on the OECD sustainable manufacturing toolkit at the Sustainability and U.S. Competiveness Summit, Washington, D.C., October 8

  2. Duflou JR, Kellens K, Dewulf W (2011) Unit process impact assessment for discrete part manufacturing: a state of the art. CIRP J Manuf Sci Technol 4(2):129–135

    Article  Google Scholar 

  3. SMLC (Smart manufacturing Leadership Coalition) (2011) Implementing 21st century smart manufacturing. Workshop Summary Report, https://smart-process-manufacturing.ucla.edu/about/news/Smart%20Manufacturing%206_24_11.pdf

  4. Zhao F, Sutherland J, Handwerker C, Choi JK, Kim H, Ramani K, Ramanujan D, Bernstein WZ, Thurston D (2010) Integrated sustainable life cycle design: a review. J Mech Des 32(9):091004–091004-15

    Google Scholar 

  5. CO2PE! Cooperative effort on process emissions in manufacturing website, http://www.mech.kuleuven.be/co2pe

  6. EcoInvent. Ecoinvent Centre, Swiss Centre for Life Cycle Inventories, available from http://www.ecoinvent.ch

  7. Hauschild M, Jeswiet J, Alting L (2005) From life cycle assessment to sustainable production: status and perspectives. CIRP Ann Manuf Technol 54(2):1–21

    Article  Google Scholar 

  8. Kellens K, Dewulf W, Overcash M, Hauschild M, Duflou JR (2011) Methodology for systematic analysis and improvement of manufacturing unit process life cycle inventory (UPLCI) part 1: methodology description. Int J Life Cycle Ass 17(1):69–78

    Article  Google Scholar 

  9. Kellens K, Dewulf W, Overcash M, Hauschild M, Duflou JR (2012) Methodology for systematic analysis and improvement of manufacturing unit process life cycle inventory (UPLCI)—part 2: case studies. Int J Life Cycle Ass 17(2):242–251

    Article  Google Scholar 

  10. Pusavec F, Krajnik P, Kopac J (2010) Transitioning to sustainable production—part I: application on machining technologies. J Clean Prod 18(2):174–184

    Article  Google Scholar 

  11. Pusavec F, Kramar D, Krajnik P, Kopac J (2010) Transitioning to sustainable production—part II: evaluation of sustainable machining technologies. J Clean Prod 18(12):1211–1221

    Article  Google Scholar 

  12. Wang Q, Liu F, Li C (2013) An integrated method for assessing the energy efficiency of machining workshop. J Clean Prod 52:122–133

    Article  MathSciNet  Google Scholar 

  13. Arena M, Azzone G, Conte A (2013) A streamlined LCA framework to support early decision making in vehicle development. J Clean Prod 41:105–113

    Article  Google Scholar 

  14. Yilmaz O, Anctil A, Karanfil T (2014) LCA as a decision support tool for evaluation of best available techniques (BATs) for cleaner production of iron casting. J Clean Prod Available online

  15. Egbue O, Wang E, Eseonu C (2014) A lean life cycle framework for assessing product sustainability. Proceedings of the 2014 Industrial and Systems Engineering Research Conference, Montreal, Canada

  16. Jawahir IS, Dillon Jr OW (2007) Sustainable manufacturing processes: new challenges for developing predictive models and optimization techniques. In: Proceedings of the First International Conference on Sustainable Manufacturing SM1, Montreal, Canada, pp. 1–15

  17. Vinodh S, Jayakrishna K, Kumar V, Dutta R (2014) Development of decision support system for sustainability evaluation: a case study. Clean Technol Environ Policy 16(1):163–174

    Article  Google Scholar 

  18. Hermann BG, Kroeze C, Jawjit W (2007) Assessing environmental performance by combining life cycle assessment, multi-criteria analysis and environmental performance indicators. J Clean Prod 15(18):1787–1796

    Article  Google Scholar 

  19. Jiang Z, Zhang H, Sutherland JW (2012) Development of an environmental performance assessment method for manufacturing process plans. Int J Adv Manuf Technol 58(5–8):783–790

    Article  Google Scholar 

  20. Avram O, Stroud I, Xirouchakis P (2011) A multi-criteria decision method for sustainability assessment of the use phase of machine tool systems. Int J Adv Manuf Technol 53(5–8):811–828

    Article  Google Scholar 

  21. Lu T, Gupta A, Jayal AD, Badurdeen F, Feng SC, Dillon OW, Jawahir IS (2010) A framework of product and process metrics for sustainable manufacturing. Proceedings of the Eighth International Conference on Sustainable Manufacturing, November 22–24, Abu Dhabi

  22. Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, Chichester

    MATH  Google Scholar 

  23. Chandrasekaran M, Muralidhar M, Krishna CM, Dixit US (2010) Application of soft computing techniques in machining performance prediction and optimization: a literature review. Int J Adv Manuf Technol 46(5–8):445–464

    Article  Google Scholar 

  24. Bohringer C, Jochem PEP (2007) Measuring the immeasurable—a survey of sustainability indices. Ecol Econ 63:1–8

    Article  Google Scholar 

  25. Brodsky A, Shao G, Riddick F (2014) Process analytics formalism for decision guidance in sustainable manufacturing. J Intell Manuf. doi:10.1007/s10845-014-0892-9

    MATH  Google Scholar 

  26. Roy CJ, Oberkampf WL (2011) A comprehensive framework for verification, validation, and uncertainty quantification in scientific computing. Comput Method Appl M 200(25–28):2131–2144

    Article  MATH  MathSciNet  Google Scholar 

  27. Shin SJ (2010) Development of framework for green productivity enhancement and its application to machining system. Pohang University of Science and Technology. Ph.D. thesis

  28. SimaPro, http://www.simapro.co.uk/

  29. Hentenryck PV (1999) The OPL optimization programming language. January 8, 1999, MIT Press

  30. Goedkoop M, Spriensma R (2001) The Eco-indicator99: a damage oriented method for life cycle impact assessment: methodology annex. PRé Consultant B.V., pp. 1–144

  31. Kalpakjian S, Schmid S (2013) Manufacturing engineering & technology. Education, Pearson

    Google Scholar 

  32. Myers RH, Anderson-Cook CM (2009) Response surface methodology: process and product optimization using designed experiments. John Wiley & Sons

  33. IBM ILOG CPLEX Optimization Studio, http://www-01.ibm.com/software/integration/optimization/cplex-optimization-studio/

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Kim, D.B., Shin, SJ., Shao, G. et al. A decision-guidance framework for sustainability performance analysis of manufacturing processes. Int J Adv Manuf Technol 78, 1455–1471 (2015). https://doi.org/10.1007/s00170-014-6711-9

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  • DOI: https://doi.org/10.1007/s00170-014-6711-9

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