Structural Modeling in Green Supply Chain Management Practices under Chains of Qualitative Indices
Keywords:
Supply Chain Management (SCM), Modeling, Fuzzy Theory, Green Practices, Qualitative FrameworkAbstract
The successfully execution of any business is today's dynamic era is possible by integrating the zone of
environmental thinking in the business activities. Green Supply Chain Management (GSCM) is a significant
view, which needs to be integrated with core business proceedings for the sake of executing sustainable business
in today's market life. GSCM is the incorporation of environmental thinking into diverse arena of the Supply
Chain Management (SCM). Thus, in this study, the authors presented a framework for modeling GSCM
practices by considering the chains of qualitative Indices. The implication of fuzzy sets theory along with
DEMATEL (Decision-Making Trial and Evaluation Laboratory) technique under the domain of GSCM is
presented in this work. Modeling based on momentous decisive factor responsible for driving the GSCM
network is presented in this study. The authors shaped an approach for addressing the green practices under
uncertainty and impreciseness for the manufacturing firms. The major intension of the proposed work is to
enlarge the views of the researchers and readers towards developing a mathematical framework for
implementing GSCM by the concerned firms. An index based on fuzzy information is presented and the
computational effort for modeling the green practices is presented, so that the managers can easily understand
the proposed work and can model green practices in their decision making
Downloads
References
Fallahpour, A., Udoncy Olugu, E., Nurmaya Musa, S., Yew Wong, K., & Noori, S. (2017). A decision support
model for sustainable supplier selection in sustainable supply chain management. Journal of Computers &
Industrial Engineering, 105, 391-410.
Fontela, E., & Gabus, A. (1976). The DEMATEL Observer, DEMATEL 1976 Report. Switzerland, Geneva,
Battelle Geneva Research Center.
Hamdan, S., & Cheaitou, A. (2017). Supplier selection and order allocation with green criteria: An MCDM and
multi-objective optimization approach. Computers & Operations Research, 81, 282-304.
Kannan, D., Khodaverdi, R., Olfat, L., Jafarian, A., & Diabat, A. (2013). Integrated fuzzy multi criteria decision
making method and multi-objective programming approach for supplier selection and order allocation in a green
supply chain. Journal of Cleaner Production, 47, 355-367.
Keshavarz, G. M., Amiri, M., Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2017). A new multi-criteria
model based on interval type-2 fuzzy sets and EDAS method for supplier evaluation and order allocation with
environmental considerations. Journal of Computers & Industrial Engineering, 112, 156-174.
Journal of Graphic Era University
Vol. 7, Issue 1, 10-17, 2019
ISSN: 0975-1416 (Print), 2456-4281 (Online)
Klir, G. J., & Yuan, B. (1995). Fuzzy sets and fuzzy logic: theory and applications. 4, Prentice-Hall, Englewood
Cliffs.
Kusi-Sarpong, S., Bai, C., Sarkis, J., & Wang, X. (2015). Green supply chain practices evaluation in the mining
industry using a joint rough sets and fuzzy TOPSIS methodology. Resources Policy, 46(1), 86-100.
Lee, H. S, Tzeng, G. H, Yeih, W., Wang, Y. J., & Yang, S. C (2013). Revised DEMATEL: resolving the
infeasibility of DEMATEL. Applied Mathematical Modelling, 37(10-11), 6746–6757.
Leekwijck, V., & Kerre, E. E. (1999). Defuzzification: criteria and classification, Journal of Fuzzy Sets and
Systems, 108(2), 159-178.
Malviya, R. K., & Kant, R., (2016). Hybrid decision making approach to predict and measure the success
possibility of green supply chain management implementation. Journal of Cleaner Production, 135, 387-409.
Sahu, A. K., Sahu, A. K., & Sahu, N. K. (2017). Benchmarking of advanced manufacturing machines based on
fuzzy-TOPSIS method. In Theoretical and Practical Advancements for Fuzzy System Integration (pp. 309-350).
IGI Global.
Sahu A. K., Sahu, N. K., & Sahu, A. K. (2016a). Application of integrated TOPSIS in ASC index: partners
benchmarking perspective. Benchmarking: An International Journal, 23(3), 540-563.
Sahu A. K., Sahu, N. K., & Sahu, A. K. (2016b). Appraisal of partner enterprises under GTFNS environment in
agile SC. International Journal of Decision Support System Technology, 8(3), 1-19.
Sahu A. K., Sahu, N. K., & Sahu, A. K. (2016c). Application of modified MULTI-MOORA for CNC machine
tool evaluation in IVGTFNS environment: an empirical study. International Journal of Computer Aided
Engineering and Technology, 8(3), 234–259.
Sahu A. K., Narang, H. K., & Rajput, M.S. (2018). A Grey-DEMATEL approach for implicating e-waste
management practice: modeling in context of Indian scenario. Grey Systems: Theory and Application, 8(1), 84-
Sahu, S. K., Datta, S., Patel, S. K., & Mahapatra. S. S. (2013). Industrial supply chain performance
benchmarking using fuzzy grey relation method. Global Journal of Management and Business Studies, 3(7),
-784.
Sari, K. (2017). A novel multi-criteria decision framework for evaluating green supply chain management
practices. Computers & Industrial Engineering, 105, 338-347.
Tseng, M. L., Lin, R.. J., Lin, Y. H., Chen, R. H., & Tan, K. (2014). Close-loop or open hierarchical structures
in green supply chain management under uncertainty. Expert Systems with Applications, 41(7), 3250-3260.
Tzeng, G. H., Chiang, C. H., & Li, C.W (2007). Evaluating intertwined effects in e-learning programs: a novel
hybrid MCDM model based on factor analysis and DEMATEL. Expert Systems with Applications, 32(4),
–1044.
Wu, W. W., & Lee, Y. T (2007). Developing global manager’s competencies using the fuzzy DEMATEL
method. Expert Systems with Applications, 32(2), 499–507.
Yang, J. L., & Tzeng, G. H (2011). An integrated MCDM technique combined with DEMATEL for a novel
cluster-weighted with ANP method. Expert Systems with Applications, 38(3), 1417–1424.
Zadeh, L. A. (1965). Fuzzy sets, Information and Control, 8, 338-353.
Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning.
Information Sciences, 8(3), 199-249.
