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Framework to evaluate the performance and sustainability of a disperse productive system

  • Edson H. Watanabe
  • Robson M. da Silva
  • Mauricio F. Blos
  • Fabricio Junqueira
  • Diolino J. Santos Filho
  • Paulo E. Miyagi
Original Paper
  • 54 Downloads

Abstract

In general, the performance of productive systems considers the efficient use of technological transformation resources (such as machines and raw materials), information processing, and handling/transportation operations. However, there are no normalized criteria or rules to evaluate the performance of a productive system in the context of sustainability. Thus, this paper introduces an approach to identify and evaluate the performance indicators related to the sustainability of productive systems, specifically for geographically dispersed cases, i.e., dispersed productive system (DPS), in which the processes are in a distributed and dispersive architecture. The proposed approach is based on a framework aimed to measure sustainability key performance indicators (SuKPIs) that evaluate the sustainability of a system. The framework considers the ANSI/ISA-95 standard, and the sustainability assessment methodology considers the balance of sustainability indicators, which depend on economic, environmental, social, and technological aspects. The Petri net and derived techniques are used to model and to verify the main functionalities of the proposed framework, and also to monitor the productive processes of DPS for data acquisition of the SuKPIs. An application example is also presented to show the feasibility and validity of the proposal.

Keywords

Framework Disperse productive system Sustainability evaluation Petri net Sustainable indicator 

Notes

Acknowledgements

We are grateful to the Brazilian government agencies CNPq (R$ 4.400,00), CAPES (R$ 2.200,00), and FAPESP (R$ 4.400,00) for sponsoring our research.

Supplementary material

References

  1. 1.
    Amrina E, Yusof SM (2011) Key performance indicators for sustainable manufacturing evaluation in automotive companies. In: IEEE international conference on industrial engineering and engineering management, Singapore, pp 1093–1097Google Scholar
  2. 2.
    Bagheri B, Yang S, Kao HA, Lee J (2015) Cyber-physical systems architecture for self-aware machines in Industry 4.0 environment. IFAC PapersOnLine 48(3):1622–1627CrossRefGoogle Scholar
  3. 3.
    Cambridge dictionary online (2017). http://dictionary.cambridge.org/pt/dicionario/essential-british-english/. Accessed 12 Jan 2017
  4. 4.
    Cannata A, Taisch M (2010) Introducing energy performances in production management: towards energy efficient manufacturing. IFIP Adv Inf Commun Technol 338:168–175CrossRefGoogle Scholar
  5. 5.
    Chen D, Thiede S, Schudeleit T, Herrmann C (2014) A holistic and rapid sustainability assessment tool for manufacturing SMEs. CIRP Ann Manuf Technol 63(1):437–440CrossRefGoogle Scholar
  6. 6.
    Colombo AW, Karnouskos S, Bangemann T (2013) A system of systems view on collaborative industrial automation. In: IEEE international conference on industrial technology, Cape Town, pp 1968–1975Google Scholar
  7. 7.
    Colombo AW, Karnouskos S, Kaynak O, Shi Y, Yin S (2017) Industrial cyber physical systems—a backbone of the fourth Industrial revolution. IEEE Ind Electron Mag 11(1):6–16CrossRefGoogle Scholar
  8. 8.
    da Silva RM, Blos MF, Junqueira F, Santos Filho DJ, Miyagi PE (2014) A service and holonic control architecture to the reconfiguration of dispersed manufacturing system. IFIP Adv Inf Commun Technol 423:111–118CrossRefGoogle Scholar
  9. 9.
    da Silva RM, Junqueira F, Santos Filho DJ, Miyagi PE (2016) Control architecture and design method of reconfigurable manufacturing systems. Control Eng Pract 49:87–100CrossRefGoogle Scholar
  10. 10.
    Davidsson P, Delamar F, Wicklund J (2006) Entrepreneurship and the Growth of Firms. Edward Edward Elgar Publishing Limited, Cheltenham. ISBN-10: 1 84542 575 8Google Scholar
  11. 11.
    Elkington J (1997) Cannibals with forks—the triple bottom line of 21st century business. Capstone Publishing Ltd, OxfordGoogle Scholar
  12. 12.
    Esmaeilian B, Behdad S, Wang B (2016) The evolution and future of manufacturing: a review. J Manuf Syst 39:79–100CrossRefGoogle Scholar
  13. 13.
    Ferreira L, Putnik G, Cunha M, Putnik Z, Castro H, Alves C, Shah V, Varela MLR (2013) Cloudlet architecture for dashboard in cloud and ubiquitous. Proc CIRP 12:366–371CrossRefGoogle Scholar
  14. 14.
    Goldstone JA (2002) Efflorescences and economic growth in world history: rethinking the “rise of the west” and the industrial revolution. J World Hist 13(2):323–389CrossRefGoogle Scholar
  15. 15.
    ISO (2004) Environmental management—life cycle assessment ISO14041: 2004,—goal and scope definition and inventory analysis, 2004Google Scholar
  16. 16.
    ISO (2010) International Organization for Standardization ISO22400-1, automation systems and integration—key performance indicator, part 1: overview, concepts and terminologyGoogle Scholar
  17. 17.
    ISO (2014) International Organization for Standardization ISO22400-2, automation systems and integration—key performance indicator (KPIs), part 2: definitions and descriptionsGoogle Scholar
  18. 18.
    Jayal AD, Badurdeen F, Dillon OW Jr, Jawahir IS (2010) Sustainable manufacturing: modeling and optimization challenges at the product, process and system levels. CIRP J Manuf Sci Technol 2:144–152CrossRefGoogle Scholar
  19. 19.
    Jeong HY, Park JH, Lee JD (2014) The cloud storage model for manufacturing system in global factory automation. In: 28th international conference on advanced information networking and applications workshops. Victoria, BC, pp 895–899Google Scholar
  20. 20.
    Joung CB, Carrel J, Sarkar P, Feng SC (2013) Categorization of indicators for sustainable manufacturing. Ecol Ind 24:148–157CrossRefGoogle Scholar
  21. 21.
    Junqueira F, Miyagi PE (2009) Modelagem e simulação distribuída de sistema produtivo baseados em rede de Petri. SBA Controle & Automação Sociedade Brasileira de Automática 20(1):1–19CrossRefGoogle Scholar
  22. 22.
    Kondoh S, Mishima N, Hotta Y, Watari K, Kurita T, Masui K (2008) Evaluation and re-design method of manufacturing processes. In: 10th international design conference. Dubrovnik, Croatia, pp 1167–1174Google Scholar
  23. 23.
    McDonough W, Braungart M (1998) The next industrial revolution. Atl Mon 282(4):82–92Google Scholar
  24. 24.
    Mell P, Glance T (2011) Definition of cloud computing—US Department of Commerce. Special Publication, pp 145–800Google Scholar
  25. 25.
    Mello JIG, Junqueira F, Miyagi PE (2010) Towards modular and coordinated manufacturing systems oriented to services. Dyna 77(163):201–210Google Scholar
  26. 26.
    Miyagi PE (2001) Controle Programável—Fundamentos do Controle de Sistemas a Eventos Discretos. (In Portuguese) Editora Edgard Blücher, São Paulo, BrasilGoogle Scholar
  27. 27.
    Murata T (1989) Petri nets—properties, analysis and applications. Proc IEEE 77(4):541–580CrossRefGoogle Scholar
  28. 28.
    NIST (2012) Cloud computing and sustainability: the environmental benefits of moving to the cloud, cyber-physical systems: situation analysis of current trends, technologies, and challenges. National Institute of Standards and Technology, ColumbiaGoogle Scholar
  29. 29.
    O’Brien C (1999) Sustainable production—a new paradigm for a new millennium. Int J Prod Econ 60–61:1–7CrossRefGoogle Scholar
  30. 30.
    OECD (2001) Organization for economic co-operation and development—sustainable development: critical issues. OECD Publishing, ParisGoogle Scholar
  31. 31.
    OECD (2011) Organization for economic co-operation and development—sustainable manufacturing toolkit—seven steps to environmental excellence, start-up guide. OECD Publishing, ParisGoogle Scholar
  32. 32.
    Senge PM, Carstedt G, Porter PL (2001) Innovating our way to the next industrial revolution. MIT Sloan Manag Rev 42(2):24–38Google Scholar
  33. 33.
    Silva M (2013) Half a century after Carl Adam Petri’s Ph.D. thesis: a perspective on the field. Ann Rev Control 37(2):191–219CrossRefGoogle Scholar
  34. 34.
    Sundmaeker H, Guillemin P, Fries P, Woelffle S (2013) Vision and challenges for realising the internet of things. CERP-IoT cluster of European research projects on the internet of thingsGoogle Scholar
  35. 35.
    Tan HX, Yeoa Z, Nga R, Tjandraa TB, Song B (2015) A sustainability indicator framework for Singapore small and medium-sized manufacturing enterprises. Proc CIRP 29:132–137CrossRefGoogle Scholar
  36. 36.
    Tracey M, Vonderembse MA, Lim JS (1999) Manufacturing technology and strategy formulation: keys to enhancing competitiveness and improving performance. J Oper Manag 17(4):411–428CrossRefGoogle Scholar
  37. 37.
    Veleva V, Hart M, Greiner T, Crumbley C (2001) Indicators of sustainable production. J Clean Prod 9(5):447–452CrossRefGoogle Scholar
  38. 38.
    Verrier B, Rose B, Caillaud E, Remita H (2013) Combining organizational performance with sustainable development issues the lean and green project benchmarking repository. J Clean Prod 85:83–93CrossRefGoogle Scholar
  39. 39.
    Wang XV, Xun WX (2013) An interoperable solution for cloud manufacturing. Robot Comput Integr Manuf 29:232–247CrossRefGoogle Scholar
  40. 40.
    WCED (1987) World commission on environment and development—our common future. Oxford University, Oxford, p 383Google Scholar
  41. 41.
    Wollschlaeger M, Sauter T, Jasperneite J (2017) The future of industrial communication: automation networks in the era of the internet of things and industry 4.0. IEEE Ind Electron Mag 11(1):17–27.  https://doi.org/10.1109/MIE.2017.2649104,ISSN:1932-4529 CrossRefGoogle Scholar
  42. 42.
    Wu D, Greer MJ, Rosen DW, Scharefer D (2013) Cloud manufacturing: strategic vision and state-of-the-art. J Manuf Syst 32(4):564–579CrossRefGoogle Scholar
  43. 43.
    Wu D, Rosen DW, Wang L, Scharefer D (2015) Cloud-based design and manufacturing: a new paradigm in digital manufacturing and design innovation. Comput Aided Des 59:1–14CrossRefGoogle Scholar
  44. 44.
    Zhang X, Badurdeen F, Rouch K, Jawahir IS (2013) On improving the product sustainability of metallic automotive components by using the total life-cycle approach and the 6R methodology. In: The 11th global conference on sustainable manufacturing—innovative solutions. Berlin, GermanyGoogle Scholar

Copyright information

© The Brazilian Society of Mechanical Sciences and Engineering 2018

Authors and Affiliations

  1. 1.Escola Politécnica da Universidade de São PauloSão PauloBrazil
  2. 2.Instituto Federal de Santa CatarinaJoinvilleBrazil
  3. 3.University of the State of Bahia (UNEB)SalvadorBrazil
  4. 4.Santa Cecília University-UNISANTASantosBrazil

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