Companies are attaching more and more importance to sustainable supply chain management (SSCM) as which makes the right strategy measures for companies. Due to the complexity of external environmental factors and internal structure, sustainable supply chain management is vulnerable to various risks. The purpose of this paper is to present a new two-stage approach for determining the best practitioner in Iran Pars Special Economic Energy Zone based on the sustainable supply chain risk management (SSCRM). The best and worst method (BWM) is used to determine the weight of risk factors. Then the method of linguistic value soft set is used to assess the impact of risk factors on each company’s sustainable supply chain which is a multiple attribute decision making problem with language preference in the second stage. Consequently, the ranking results of sustainable supply chain of each enterprise are obtained. This study contributes to finding the key risk factors of SSCRM. Evaluating these companies SSCRM with preference information, the best practitioner can obtain. The combination of BWM and linguistic value soft set approach provides a new nonparametric theoretical method and tool for this kind of decision-making problems with this background. At the same time, the conclusions of this study have guiding significance for the implementation of industrial supply chain. Limitations of the study along with future research directions are also presented.
AdeseunM.A., AnosikeA.I., ReyesJ.A.G. and Al-TalibM., Supply chain risk perception: Understanding the gap between theory and practice, IFAC-PapersOnLine51(11) (2018), 1701–1706.
2.
AhiP. and SearcyC., A comparative literature analysis of definitions for green and sustainable supply chain management, Journal of Cleaner Production52 (2013), 329–341.
3.
KarimahW.N., AhmadW., RezaeiJ., SadaghianiS. and TavasszyL.A., Evaluation of the external forces affecting the sustainability of oil and gas supply chain using best worst method, Journal of Cleaner Production153 (2017), 242–252.
4.
AhmadiH.B., SarpongS.K. and RezaeiJ., Assessing the social sustainability of supply chains using best worst method, Resources, Conservation and Recycling126 (2017), 99–106.
5.
AmrinaE. and VilsiA.L., Key performance indicators for sustainable manufacturing evaluation in cement industry, Procedia CIRP26 (2015), 19–23.
6.
BadiI. and BallemM., Supplier selection using rough bwm-mairca model: A case study in pharmaceutical supplying in libya, Decision Making: Applications in Management and Engineering1 (2018), 2560–6018.
7.
BaiL., LiY., DuQ. and XuY., A fuzzy comprehensive evaluation model for sustainability risk evaluation of ppp projects, Sustainability9 (2017), 1890.
8.
BiK., HuangP. and YeH., Risk identification, evaluation and response of low-carbon technological innovation under the global value chain: A case of the chinese manufacturing industry, Technological Forecasting and Social Change100 (2015), 238–248.
9.
ÇağmanN. and EnginoğluS., Soft matrix theory and its decision making, Computers & Mathematics with Applications59(10) (2010), 3308–3314.
10.
Chardine-BaumannE. and Botta-GenoulazV., A framework for sustainable performance assessment of supply chain management practices, Computers & Industrial Engineering76 (2014), 138–147.
11.
DorliH., Hansen ErikG. and SchalteggerS., Strategies in sustainable supply chain management: An empirical investigation of large german companies, Corporate Social Responsibility and Environmental Management20 (2012), 205–218.
12.
PamuçarS.S.D. and StevićŇ., A new model for determining weight coefficients of criteria in mcdm models: Full consistency method (fucom), Symmetry10 (2018), 393.
13.
GunasekaranA., ChildeS., DubeyR. and PapadopoulosT., Skills needed in supply chain-human agency and social capital analysis in third party logistics, Management Decision56(1) (2018), 143–159.
14.
KaraM.E. and FiratS.O, Supplier risk assessment based on best-worst method and k-means clustering: A case study, Sustainability10 (2018), 1066.
15.
GiannakisM. and PapadopoulosT., Supply chain sustainability: A risk management approach, International Journal of Production Economics171 (2016), 455–470.
16.
YangH., Analysis of challenges and opportunities in china’s manufacturing supply chain management, Shopping Modernization19 (2018), 10–11.
17.
HaoJ. and YueL., Summary of performance evaluation of sustainable supply chain, Journal of Shenyang Aerospace University06 (2018), 1–13.
18.
JavadM.O.M., DarvishiM. and JavadA.O.M., Green supplier selection for the steel industry using bwm and fuzzy topsis: A case study of khouzestan steel company, Sustainable Futures2 (2020), 100012.
19.
TurskisE.K., KeršulieneZ. and ZavadskasV., Selection of rational dispute resolution method by applying new step-wise weight assessment ratio analysis (swara), Journal of Business Economics and Management11 (2010), 243–258.
20.
LiuL., SongW. and HanW., How sustainable is smart pss? an integrated evaluation approach based on rough bwm and todim, Advanced Engineering Informatics43 (2020), 101042.
21.
LiuP. and LiY., An extended multimoora method for probabilistic linguistic multi-criteria group decision-making based on prospect theory, Computers & Industrial Engineering136 (2019), 528–545.
22.
LiuP. and LiY., Multi-attribute decision making method based on generalized maclaurin symmetric mean aggregation operators for probabilistic linguistic information, Computers & Industrial Engineering131 (2019), 282–294.
23.
LiuP. and TengF., Probabilistic linguistic todim method for selecting products through online product reviews, Information Sciences485 (2019), 441–455.
24.
LiuP., ZhuB., WangP. and ShenM., An approach based on linguistic spherical fuzzy sets for public evaluation of shared bicycles in china, Engineering Applications of Artificial Intelligence87 (2020), 103295.
25.
LuthraS., GovindanK., KannanD., ManglaS.K. and GargC.P., An integrated framework for sustainable supplier selection and evaluation in supply chains, Journal of Cleaner Production140 (2017), 1686–1698.
26.
MathivathananD., KannanD. and Noorul HaqA., Sustainable supply chain management practices in indian automotive industry: A multi-stakeholder view, Resources Conservation and Recycling128 (2018), 284–305.
27.
MaviaR.K., GohbM. and MavicN.K., Supplier selection with shannon entropy and fuzzy topsis in the context of supply chain risk management, Procedia –Social and Behavioral Sciences235 (2016), 216–225.
28.
MoeinzadehP. and HajfathalihaA., A combined fuzzy decision making approach to supply chain risk assessment, International Scholarly and Scientific Research & Innovation3 (2009), 1631–1647.
29.
MolodtsovD., Soft set theory—first results, 37 19–31. (1999). Global optimization, control, and games, III.
30.
MolodtsovD., Soft set theoryfirst results, Computers & Mathematics with Applications37 (1999), 19–31. 02
31.
JiangY., FengY., NyamahE. and EnchillE., Agri-food supply chain performance: an empirical impact of risk, Management Decision55(5) (2017), 872–891.
32.
OliveiraJ.B., JinM., LimaR.S., KobzaJ.E. and MontevechiJ.A.B., The role of simulation and optimization methods in supply chain risk management: Performance and review standpoints, Simulation Modelling Practice and Theory92 (2019), 17–44.
33.
QaziA., DicksonA., QuigleyJ. and GaudenziB., Supply chain risk network management: A bayesian belief network and expected utility based approach for managing supply chain risks, International Journal of Production Economics196 (2018), 24–42.
34.
ReefkeH. and SundaramD., Key themes and research opportunities in sustainable supply chain management - identification and evaluation, Omega66 (2017), 195–211.
RostamzadehR., GhorabaeeM.K., GovindanK., EsmaeiliA. and NobarH.B.K., Evaluation of sustainable supply chain risk management using an integrated fuzzy topsis- critic approach, Journal of Cleaner Production175 (2018), 651–669.
37.
ShiL., WuK.-J. and TsengM.-L., Improving corporate sustainable development by using an interdependent closed-loop hierarchical structure, Resources Conservation and Recycling119 (2017), 24–35.
38.
SongW., MingX. and LiuH.-C., Identifying critical risk factors of sustainable supply chain management: A rough strength-relation analysis method, Journal of Cleaner Production143 (2017), 100–115.
39.
StefanS. and MartinM., Core issues in sustainable supply chain management ? a delphi study, Business Strategy and the Environment17 (2008), 455–466.
40.
SuhiS.A., EnayetR., HaqueT., AliS.M., MoktadirMd.A. and PaulS.K., Environmental sustainability assessment in supply chain: An emerging economy context, Environmental Impact Assessment Review79 (2019), 106306.
41.
SunB., MaW. and LiX., Linguistic value soft set-based approach to multiple criteria group decisionmaking, Applied Soft Computing58 (2017), 285–296.
42.
WangZ. and SarkisJ., Investigating the relationship of sustainable supply chain management with corporate financial performance, International Journal of Productivity and Performance Management62 (2013), 871–888.
43.
WittstruckD. and TeutebergF., Understanding the success factors of sustainable supply chain management: Empirical evidence from the electrics and electronics industry, Corporate Social Responsibility and Environmental Management19 (2012), 141–158.
44.
XuM., CuiY., HuM., XuX., ZhangZ., LiangS. and QuS., Supply chain sustainability risk and assessment, Journal of Cleaner Production225 (2019), 857–867.
45.
XuX. and HuixiangZ., Sustainable supply chain management and circular economy capability: Based on the perspective of institutional pressure, Systems Engineering Theory and Practice37 (2017), 1793–1804.
46.
XuZ., Deviation measures of linguistic preference relations in group decision making, Omega33(3) (2005), 249–254.
47.
WuJ., YanB. and WangF., Cvar-based risk assessment and control of the agricultural supply chain, Management Decision57(7) (2019), 1496–1510.
48.
ZhangJ., ChenX. and FangC., Transmission of a supplier’s disruption risk along the supply chain: a further investigation of the chinese automotive industry, Production Planning & Control29 (2018), 773–789.
49.
ŇiňovićM. and PamucarD., New model for determining criteria weights: Level based weight assessment (Ibwa) model, Decision Making: Applications in Management and Engineering2 (2019), 126–137.
