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.
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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)
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)
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)
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)
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)
Tseng, M.L., Chiu, A.S.F.: Evaluating firm’s green supply chain management in linguistic preferences. J. Clean. Prod. 40, 22–31 (2013)
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)
Mamdani, E.H., Assilian, S.: An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man Mach. Stud. 7, 1–13 (1975)
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)
Bai, C., Sarkis, J.: Integrating sustainability into supplier selection with grey system and rough set methodologies. Int. J. Prod. Econ. 124, 252–264 (2010)
Ageron, B., Gunasekaran, A., Spalanzani, A.: Sustainable supply management: an empirical study. Int. J. Prod. Econ. 140, 168–182 (2012)
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)
Beamon, B.M.: Environmental and sustainability ethics in supply chain management. Sci. Eng. Ethics 11, 221–234 (2005)
Salam, M.: Corporate social responsibility in purchasing and supply chain. J. Bus. Ethics 85, 335–370 (2009)
Murphy, P.R., Poist, R.F.: Green logistics strategies: an analysis of usage patterns. Transp. J. 40, 5–16 (2000)
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)
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)
Sarkis, J.: A strategic decision framework for green supply chain management. J. Clean. Prod. 11, 397–409 (2003)
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)
Wang, F., Lai, X., Shi, N.: A multi-objective optimization for green supply chain network design. Decis. Support Syst. 51, 262–269 (2011)
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)
Lin, R.J.: Using fuzzy DEMATEL to evaluate the green supply chain management practices. J. Clean. Prod. 40, 32–39 (2013)
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)
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)
Hsu, C.W., Hu, A.H.: Green supply chain management in the electronic industry. Int. J. Sci. Technol. 5, 205–216 (2008)
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)
Diabat, A., Govindan, K.: An analysis of the drivers affecting the implementation of green supply chain management. Resour. Conserv. Recycl. 55, 659–667 (2011)
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)
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)
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)
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)
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)
Min, H., Galle, W.P.: Green purchasing practices of US firms. Int. J. Oper. Prod. Manag. 21, 1222–1238 (2001)
Deif, A.M.: A system model for green manufacturing. J. Clean. Prod. 19, 1553–1559 (2011)
Zadeh, L.A.: Fuzzy sets. Inform. Control 8, 338–353 (1965)
Soltani, A., Haji, R.: A project scheduling method based on fuzzy theory. J. Ind. Syst. Eng. 1, 70–80 (2007)
Zimmermann, H.J.: Fuzzy Set Theory and Its Applications. Kluwer Academic, Dordrecht (1991)
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)
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)
Chen, C.: Stability conditions of fuzzy systems and its application to structural and mechanical systems. Adv. Eng. Softw. 7, 624–629 (2006)
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)
Lin, J., Chen, C., Peng, C.: Potential hazard analysis and risk assessment of debris flow by fuzzy modeling. Nat. Hazards 64, 273–282 (2012)
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)
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
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)
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)
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)
Kothamasu, R., Huang, S.H.: Adaptive Mamdani fuzzy model for condition-based maintenance. Fuzzy Sets Syst. 158, 2715–2733 (2007)
Wang, L.X.: Adaptive fuzzy systems and control Design and stability analysis. University of California at Berkeley, PTR Prentice Hall (1993)
Altrock, C.V.: Fuzzy Logic and Neuro fuzzy-Applications in Business and Finance. Prentice Hall, New Jersey (1995)
Pedrycz, W., Gomide, F.: Fuzzy systems engineering—toward human-centric computing. Wiley, New Jersey (2007)
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)
Pourjavad, E., Shirouyehzad, H.: Analyzing maintenance strategies by FANP considering RAM criteria; a case study. Int. J. Logist. Syst. Manag. 18, 302–321 (2014)
Sivanandam, S., Sumathi, S., Deepa, S.: Introduction to Fuzzy Logic Using MATLAB. Springer, Berlin (2007)
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)
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)
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)
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)
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)
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Pourjavad, E., Shahin, A. The Application of Mamdani Fuzzy Inference System in Evaluating Green Supply Chain Management Performance. Int. J. Fuzzy Syst. 20, 901–912 (2018). https://doi.org/10.1007/s40815-017-0378-y
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DOI: https://doi.org/10.1007/s40815-017-0378-y