Advertisement

International Journal of Fuzzy Systems

, Volume 20, Issue 3, pp 901–912 | Cite as

The Application of Mamdani Fuzzy Inference System in Evaluating Green Supply Chain Management Performance

  • Ehsan PourjavadEmail author
  • Arash Shahin
Article

Abstract

Qualitative criteria for assessing green supply chain management (GSCM) performance are influenced by uncertainty, essentially due to the vagueness intrinsic to the evaluation of qualitative factors. This paper aims to decrease the uncertainty which is caused by human judgments in the process of GSCM performance evaluation employing linguistic terms and degrees of membership. In this study, a fuzzy set theory approach has been proposed for handling the linguistic imprecision and the ambiguity of human being’s judgment. It also pioneers applying the fuzzy inference system for evaluating GSCM performance of companies in terms of green criteria. In the proposed model, human reasoning has been modeled with fuzzy inference rules and has been set in the system, which is an advantage when compared to the models that combine fuzzy set theory with multi-criteria decision-making models. To highlight the real-life applicability of the proposed model, an empirical case study has been conducted. Findings reveal the usefulness of the proposed model in evaluating the performance of companies according to GSCM criteria with human linguistic terms. Findings also indicate that green design and green manufacturing dimensions have the highest impact on company performance. The robustness of the proposed FIS model has been proved with different defuzzification methods.

Keywords

Green supply chain management (GSCM) Green criteria Fuzzy inference system (FIS) Mamdani Evaluation Performance 

References

  1. 1.
    Azevedo, S.G., Carvalho, H., Machado, V.C.: The influence of green practices on supply chain performance: a case study approach. Transp. Res. E-Logist. 47, 850–871 (2011)CrossRefGoogle Scholar
  2. 2.
    Govindan, K., Soleimani, H., Kannan, D.: Reverse logistics and closed-loop supply chain: a comprehensive review to explore the future. Eur. J. Oper. Res. 240, 603–626 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Zhu, Q., Sarkis, J.: Relationships between operational practices and performance among early adopters of green supply chain management practices in Chinese manufacturing, enterprises. J. Oper. Manag. 22, 265–289 (2004)CrossRefGoogle Scholar
  4. 4.
    Shen, L., Olfat, L., Govindan, K., Khodaverdi, R., Diabat, A.: A fuzzy multi criteria approach for evaluating green supplier’s performance in green supply chain with linguistic preferences. Resour. Conserv. Recycl. 74, 170–179 (2013)CrossRefGoogle Scholar
  5. 5.
    Büyüközkan, G., Cifci, G.: A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers. Expert Syst. Appl. 39, 3000–3011 (2012)CrossRefGoogle Scholar
  6. 6.
    Tseng, M.L., Chiu, A.S.F.: Evaluating firm’s green supply chain management in linguistic preferences. J. Clean. Prod. 40, 22–31 (2013)CrossRefGoogle Scholar
  7. 7.
    Petrovic, D.V., Tanasijevi, M., Mili, V., Lili, N., Stojadinovi, S., Svrkota, I.: Risk assessment model of mining equipment failure based on fuzzy logic. Expert Syst. Appl. 41(18), 8157–8164 (2014)CrossRefGoogle Scholar
  8. 8.
    Mamdani, E.H., Assilian, S.: An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man Mach. Stud. 7, 1–13 (1975)CrossRefzbMATHGoogle Scholar
  9. 9.
    Zhu, Q., Sarkis, J., Geng, Y.: Green supply chain management in China: pressures, practices and performance. Int. J. Oper. Prod. Manag. 25, 449–468 (2005)CrossRefGoogle Scholar
  10. 10.
    Bai, C., Sarkis, J.: Integrating sustainability into supplier selection with grey system and rough set methodologies. Int. J. Prod. Econ. 124, 252–264 (2010)CrossRefGoogle Scholar
  11. 11.
    Ageron, B., Gunasekaran, A., Spalanzani, A.: Sustainable supply management: an empirical study. Int. J. Prod. Econ. 140, 168–182 (2012)CrossRefGoogle Scholar
  12. 12.
    Sharfman, M., Shaft, T., Anex, R.: The road to cooperative supply-chain environmental management: trust and uncertainty among proactive firms. Bus. Strategy Environ. 18, 1–13 (2009)CrossRefGoogle Scholar
  13. 13.
    Beamon, B.M.: Environmental and sustainability ethics in supply chain management. Sci. Eng. Ethics 11, 221–234 (2005)CrossRefGoogle Scholar
  14. 14.
    Salam, M.: Corporate social responsibility in purchasing and supply chain. J. Bus. Ethics 85, 335–370 (2009)CrossRefGoogle Scholar
  15. 15.
    Murphy, P.R., Poist, R.F.: Green logistics strategies: an analysis of usage patterns. Transp. J. 40, 5–16 (2000)Google Scholar
  16. 16.
    Cruz, J.M., Matsypura, D.: Supply chain networks with corporate social responsibility through integrated environmental decision-making. Int. J. Prod. Res. 47, 621–648 (2009)CrossRefzbMATHGoogle Scholar
  17. 17.
    Gunther, E., Scheibe, L.: The hurdle analysis. A self-evaluation tool for municipalities to identify, analyze and overcome hurdles to green procurement. Corp. Soc. Responsib. Environ. Manag. 13, 61–77 (2006)CrossRefGoogle Scholar
  18. 18.
    Sarkis, J.: A strategic decision framework for green supply chain management. J. Clean. Prod. 11, 397–409 (2003)CrossRefGoogle Scholar
  19. 19.
    Chen, C.C., Shih, H.S., Shyur, H.J., Wu, K.S.: A business strategy selection of green supply chain management via an analytic network process. Comput. Math Appl. 64, 2544–2557 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Wang, F., Lai, X., Shi, N.: A multi-objective optimization for green supply chain network design. Decis. Support Syst. 51, 262–269 (2011)CrossRefGoogle Scholar
  21. 21.
    Jamshidi, R., Fatemi Ghomi, S.M.T., Karimi, B.: Multi-objective green supply chain optimization with a new hybrid memetic algorithm using the Taguchi method. Sci. Iran. 19, 1876–1886 (2012)CrossRefGoogle Scholar
  22. 22.
    Lin, R.J.: Using fuzzy DEMATEL to evaluate the green supply chain management practices. J. Clean. Prod. 40, 32–39 (2013)CrossRefGoogle Scholar
  23. 23.
    Mathiyazhagan, K., Diabat, A., Al-Refaie, A., Xu, L.: Application of analytical hierarchy process to evaluate pressures to implement green supply chain management. J. Clean. Prod. 107, 229–236 (2015)CrossRefGoogle Scholar
  24. 24.
    Yuce, B., Mastrocinque, E.: A hybrid approach using the Bees algorithm and fuzzy-AHP for supplier selection. In: Handbook of Research on Advanced Computational Techniques for Simulation-Based Engineering. (2015)Google Scholar
  25. 25.
    Hsu, C.W., Hu, A.H.: Green supply chain management in the electronic industry. Int. J. Sci. Technol. 5, 205–216 (2008)Google Scholar
  26. 26.
    Walker, H., Di Sisto, L., McBain, D.: Drivers and barriers to environmental supply chain management practices, lessons from the public and private sector. J. Purch. Supply Manag. 14, 69–85 (2008)CrossRefGoogle Scholar
  27. 27.
    Diabat, A., Govindan, K.: An analysis of the drivers affecting the implementation of green supply chain management. Resour. Conserv. Recycl. 55, 659–667 (2011)CrossRefGoogle Scholar
  28. 28.
    Mangla, S., Madaan, J., Chan, F.T.S.: Analysis of flexible decision strategies for sustainability-focused green product recovery system. Int. J. Prod. Res. 51, 3443–3462 (2013)CrossRefGoogle Scholar
  29. 29.
    Mangla, S., Madaan, J., Sarma, P.R.S., Gupta, M.P.: Multi-objective decision modeling using Interpretive Structural Modeling (ISM) for Green Supply Chains. Int. J. Logist. Syst. Manag. 17, 125–142 (2014)CrossRefGoogle Scholar
  30. 30.
    Luthra, S., Garg, D., Haleem, A.: Green supply chain management: implementation and performance–a literature review and some issues. J. Adv. Manag. Res. 11, 20–46 (2014)CrossRefGoogle Scholar
  31. 31.
    Shang, K.C., Lu, C.S., Li, S.: A taxonomy of green supply chain management capability among electronics-related manufacturing firms in Taiwan. J. Environ. Manage. 91, 1218–1226 (2010)CrossRefGoogle Scholar
  32. 32.
    Eltayeb, T.K., Zailani, S., Ramayah, T.: Green supply chain initiatives among certified companies in Malaysia and environmental sustainability: investigating the outcomes. Resour. Conserv. Recycl. 55, 495–506 (2011)CrossRefGoogle Scholar
  33. 33.
    Min, H., Galle, W.P.: Green purchasing practices of US firms. Int. J. Oper. Prod. Manag. 21, 1222–1238 (2001)CrossRefGoogle Scholar
  34. 34.
    Deif, A.M.: A system model for green manufacturing. J. Clean. Prod. 19, 1553–1559 (2011)CrossRefGoogle Scholar
  35. 35.
    Zadeh, L.A.: Fuzzy sets. Inform. Control 8, 338–353 (1965)CrossRefzbMATHGoogle Scholar
  36. 36.
    Soltani, A., Haji, R.: A project scheduling method based on fuzzy theory. J. Ind. Syst. Eng. 1, 70–80 (2007)Google Scholar
  37. 37.
    Zimmermann, H.J.: Fuzzy Set Theory and Its Applications. Kluwer Academic, Dordrecht (1991)CrossRefzbMATHGoogle Scholar
  38. 38.
    Lin, M., Chen, C.: Application of fuzzy models for the monitoring of ecologically sensitive ecosystems in a dynamic semi-arid landscape from satellite imagery. Eng. Comput. 27, 5–19 (2010)CrossRefzbMATHGoogle Scholar
  39. 39.
    Chen, C.Y., Lin, J., Lee, W., Chen, C.W.: Fuzzy control for an oceanic structure: a case study in time-delay TLP system. J. Vib. Control 16, 147–160 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  40. 40.
    Chen, C.: Stability conditions of fuzzy systems and its application to structural and mechanical systems. Adv. Eng. Softw. 7, 624–629 (2006)CrossRefGoogle Scholar
  41. 41.
    Chen, C.: Application of fuzzy-model-based control to nonlinear structural systems with time delay: an LMI method. J. Vib. Control 16, 1651–1672 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  42. 42.
    Lin, J., Chen, C., Peng, C.: Potential hazard analysis and risk assessment of debris flow by fuzzy modeling. Nat. Hazards 64, 273–282 (2012)CrossRefGoogle Scholar
  43. 43.
    Liu, K., Ko, C., Fan, C., Chen, C.: Combining risk assessment, life cycle assessment and multi-criteria decision analysis to estimate environmental aspects in EMS. Int. J. Life Cycle Assess. 17, 845–862 (2013)CrossRefGoogle Scholar
  44. 44.
    Pourjavad, E., Mayorga, R.V.: A comparative study and measuring performance of manufacturing systems with Mamdani fuzzy inference System. J. Intell. Manuf. (2017). doi: 10.1007/s10845-017-1307-5 Google Scholar
  45. 45.
    Balal, E., Cheu, R.L., Sarkodie-Gyan, T.: A binary decision model for discretionary lane changing move based on fuzzy inference system. Transp. Res. C-Emerg. 67, 47–61 (2016)CrossRefGoogle Scholar
  46. 46.
    Guimaraes, A.C.F., Lapa, C.M.F.: Effects analysis fuzzy inference system in nuclear problems using approximate reasoning. Ann. Nucl. Energy 31, 107–115 (2004)CrossRefGoogle Scholar
  47. 47.
    Bocaniala, C.D., Jose, S.D.C., Vasile, P.: A novel fuzzy classification solution for fault diagnosis. J. Intell. Fuzzy Syst. 15, 195–205 (2004)zbMATHGoogle Scholar
  48. 48.
    Kothamasu, R., Huang, S.H.: Adaptive Mamdani fuzzy model for condition-based maintenance. Fuzzy Sets Syst. 158, 2715–2733 (2007)MathSciNetCrossRefGoogle Scholar
  49. 49.
    Wang, L.X.: Adaptive fuzzy systems and control Design and stability analysis. University of California at Berkeley, PTR Prentice Hall (1993)Google Scholar
  50. 50.
    Altrock, C.V.: Fuzzy Logic and Neuro fuzzy-Applications in Business and Finance. Prentice Hall, New Jersey (1995)Google Scholar
  51. 51.
    Pedrycz, W., Gomide, F.: Fuzzy systems engineering—toward human-centric computing. Wiley, New Jersey (2007)Google Scholar
  52. 52.
    Orji, I.J., Wei, S.: An innovative integration of fuzzy-logic and systems dynamics in sustainable supplier selection: a case on manufacturing industry. Comput. Ind. Eng. 88, 1–12 (2015)CrossRefGoogle Scholar
  53. 53.
    Pourjavad, E., Shirouyehzad, H.: Analyzing maintenance strategies by FANP considering RAM criteria; a case study. Int. J. Logist. Syst. Manag. 18, 302–321 (2014)CrossRefGoogle Scholar
  54. 54.
    Sivanandam, S., Sumathi, S., Deepa, S.: Introduction to Fuzzy Logic Using MATLAB. Springer, Berlin (2007)CrossRefzbMATHGoogle Scholar
  55. 55.
    Sharma, R.K., Kumar, D., Kumar, P.: Systematic failure mode effect analysis (FMEA) using fuzzy linguistic modelling. Int. J. Qual. Reliab. Manag. 22, 986–1004 (2005)CrossRefGoogle Scholar
  56. 56.
    Zhu, Q., Sarkis, J., Lai, K.H.: Green supply chain management: pressures, practices and performance within the Chinese automobile industry. J. Clean. Prod. 15, 1041–1052 (2007)CrossRefGoogle Scholar
  57. 57.
    Diabat, A., Khodaverdi, R., Olfat, L.: An exploration of green supply chain practices and performances in an automotive industry. Int. J. Adv. Manuf. Technol. 68, 949–961 (2013)CrossRefGoogle Scholar
  58. 58.
    Govindan, K., Khodaverdi, R., Vfadarnikjoo, A.: Intuitionist fuzzy based DEMATEL method for developing green practices and performances in a green supply chain. Expert Syst. Appl. 42, 7207–7220 (2015)CrossRefGoogle Scholar
  59. 59.
    Chandima Ratnayake, R.M.: Application of a fuzzy inference system for functional failure risk rank estimation: RBM of rotating equipment and instrumentation. J. Loss Prev. Process 29, 216–224 (2014)CrossRefGoogle Scholar

Copyright information

© Taiwan Fuzzy Systems Association and Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Industrial Systems Engineering, University of ReginaReginaCanada
  2. 2.Department of ManagementUniversity of IsfahanIsfahanIran

Personalised recommendations