A Dynamic Decision Support System

Sustainable Production and Consumption 27 (2021) 905–920

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Sustainable Production and Consumption

journalhomepage:www.elsevier.com/locate/spc

A Dynamic Decision Support System for Sustainable Supplier Selection in Circular Economy

Behrouz Alavi

^a

, Madjid Tavana

^b,c,∗

, Hassan Mina

^d

aDepartment of Industrial Engineering, Sharif University of Technology, Tehran, Iran

bBusiness Systems and Analytics Department, Distinguished Chair of Business Analytics, La Salle University, Philadelphia, PA 19141, USA

cBusiness Information Systems Department, Faculty of Business Administration and Economics, University of Paderborn, D-33098 Paderborn, Germany

dSchool of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran

a rt i c l e i nf o

Article history:

Received 9 December 2020 Revised 5 February 2021 Accepted 9 February 2021 Available online 12 February 2021 Keywords:

Sustainable circular supplier selection decision support system

best-worst method fuzzy inference system machine learning

a b s t r a c t

Supplierselectionisanimportantandchallengingprobleminsustainablesupplychainmanagement.We proposeadynamicdecision supportsystem(DSS)forsustainable supplierselectionincircular supply chains.Unlike thelineartake-make-waste-disposeproductionsystems,circular supplychainsarenon- linearmake-waste-recycleproductionsystemswithzero-wastevision.TheproposedDSSallowsusersto customizeandweighttheireconomic,social,andcircularcriteriawithafuzzybest-worstmethod(BWM) andselectthemostsuitablesupplierwiththefuzzyinferencesystem(FIS).Machinelearningisusedto maintainandsynthesize thecriteriascoresfor thesuppliersafter eachsupplierselection engagement.

We presentacase study atapetrochemicalholdingcompanywith acontrollinginterestoverseveral subsidiarycompaniestodemonstratetheapplicabilityoftheproposedapproach.

© 2021InstitutionofChemicalEngineers.PublishedbyElsevierB.V.Allrightsreserved.

1. Introduction

The increasing awareness of customers and stockholders towards environmental concerns and sustainability coupled with competitive advantage have forced organizations to adopt green and sustainable business practices (Mardan et al., 2019; Ghayebloo et al., 2015). The first step indesigning a sustainable supply chain is to collaborate with sustainable suppliers posi- tioned in the upstream chain, impacting the downstream chain (Yu et al., 2019). Sustainable supplier selection requires a triple bottom line (i.e., economic, environmental, and social) consid- eration (Govindan et al., 2013). In addition to enhancing supply chain sustainability, selecting the right sustainable supplier has a significant impact on cost reduction, quality enhancement, and supplychainefficiency(Kannanetal.,2020).

Fallahpour et al. (2017) have argued the selection of suitable suppliersinsupplychainmanagementisacomplextaskrequiring a comprehensiveevaluationprocess,oftenunderuncertaincondi- tions.Alargenumberofstudieshaveproposedstand-aloneorhy- brid models forsustainability assessment and supplier selection.

∗Corresponding author at: Business Systems and Analytics Department, Distin- guished Chair of Business Analytics, La Salle University, Philadelphia, PA 19141, United States.

E-mail addresses: behrouzalavi@alum.sharif.edu (B. Alavi), tavana@lasalle.edu (M. Tavana), hassan.mina@ut.ac.ir (H. Mina).

Despitethese efforts, little attentionhasbeen paid to thedevel- opmentof acomprehensiveframework forcircularsupplier eval- uationandselection.Accordingly,thefollowingresearchquestions remainunanswered:

a) Whicheconomic,social, andcircularcriteriaaremostsuitable forsupplierselectioninacircularsupplychain?

b) Whichapproach ismostsuitable fora comprehensiveandin- clusiveevaluationofsustainable suppliersina circularsupply chain?

Beforetheavailabilityofnewdigitaltechnologiessuchascloud computing,machine learning,andtheinternet ofthings,an anal- ysisof a vast amount of datawas difficult,often producing lim- itedandunpersuasive results(Liuetal.,2018).Theadventofnew technologies and digital systems such as machine learning has provided decision-makers with ready access to information any- time and anywhere (Cavalcante et al., 2019). In this study, ma- chinelearning,thebest-worstmethod(BWM),andfuzzyinference system(FIS) are effectively integratedwithin a dynamicdecision supportsystem(DSS)forsustainable supplierselectionincircular supply chains. The users selectrelevant criteria from a database of economic, social, and circular criteria. The importance weight ofcriteriaisdeterminedwiththefuzzyBWM,andthefinalscore of the suppliers is calculated with the FIS method. The decision modeliscomposedofahierarchicalstructureto presenttheeco- nomic,social,andcircularityaspects oftheproblemandtherele- https://doi.org/10.1016/j.spc.2021.02.015

2352-5509/© 2021 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

B. Alavi, M. Tavana and H. Mina Sustainable Production and Consumption 27 (2021) 905–920

Nomenclature

w_b Theweightofthebestcriterion ww Theweightoftheworstcriterion w_j Theweightofthecriterionj w_{b j} Thebest-to-othersvector w_jw Theothers-to-worstvector

γ

^{∗ Auxiliaryvariable}

(^wl_b,w^m_b,w^u_b) ^{Thefuzzyweightsofthebestcriterion} (^wlw,w^m_w,w^u_w) ^{Thefuzzyweightsoftheworstcriterion} (^wl_{b j},w^m_{b j},w^u_{b j}) ^Thefuzzybest-to-othersvector

(^wl_jw,w^m_jw,w^u_jw) ^Thefuzzyothers-to-worstvector

CI Consistencyindex

CR Consistencyratio

vantevaluationcriteriaandalternativesuppliers.Theanalytichier- archyprocess(AHP)isacommonpairwisecomparisonmethodfor determining thecriteriaweights inmulti-criteriadecisionmaking (MCDM).TheBWM,similarly,usespairwisecomparisonstodeter- mine thecriteriaweights inMCDM.ThisstudyusesBWM,which requires fewer pairwise comparisons and produces more consis- tent results (Rezaei, 2015,2016).Thus, BWMcalculates the crite- riaweightsandthesuppliers’economic,circular,andsocialscores.

Other methods used for developing criteria weights are a direct estimation.However,adirectestimationofcriteriaweightsisarbi- traryandoftensubjecttorandomandsubjectivedecision-makers’

valuation. A nonlinear relationship is dominant betweenthe cri- teria andthe final scores; therefore, rule-based methods such as FIS area suitable methodforobtainingthe suppliers’final scores in this study (Jain et al., 2020). There are two methods for de- signingFIS: theMamdaniapproach(MamdaniandAssilian,1975) andSugenoapproach(Sugeno,1985).Mamdani’sapproachutilizes knowledge modeling inDSSs, butSugeno’s approach isgrounded in mathematical modeling.In addition,the output ofa Mamdani FISisderivedfromnonlinearfunctions,buttheoutputofaSugeno FISisproducedfromaconstantoralinearfunction.InSugenoFIS, unlike MamdaniFIS,it is not possible to define themembership functions for the output variables. Hence, In this paper, we use a MamdaniFIS to designour DSS.The reasonwe use FISin this study is fivefold: (i) mathematical concepts within FIS reasoning are simple, (ii) FIS is flexible, (iii)it is easy to modify a FIS just byaddingordeletingrules,(iv)FISisbuiltonexpertiseandrelies ontheknow-howoftheoneswhounderstandthesystem,and(v) FIS can be blended withother systems such a machine learning embeddedintheproposedDSStoupdateandsynthesizehistorical data.

We (i) propose a practical and user-friendly DSS with cus- tomization capabilitiesforsustainable supplier selection incircu- lar supply chains; (ii) apply a dynamic DSS forsustainable sup- plier selection using the fuzzy BWM and FIS methods in an in- tegrated framework;(iii) use machine learning to maintain sup- plier information synthesize historical data,and (iv) validate the proposed DSSthroughimplementationinapetrochemicalholding company withacontrollinginterestover18othercompanies.The remainder ofthispaper isorganizedas follows.In section 2,we present the relevant literature on supplier selection criteria and methods.Section3presentstheproposedframework.InSection4, we present a case study to demonstrate the applicability of the proposed framework. In Section 5, we concludewithourconclu- sionsandfutureresearchdirections.

2. Literaturereview

The selection of relevant criteriaandsuitable selection meth- ods are two critical prerequisites for successful sustainable sup- plierselection.Accordingly,theliteraturereview presentedinthis study consists of two sections in line with the research ques- tions. Section 2.1 provides a review of the literature on triple bottom line criteria used in supplier selection problems, and Section2.2presentsareview ofthemethodsusedinthesustain- ablesupplierselectionliterature.

2.1. Supplierselectioncriteriainsustainablesupplychains

One of the major challenges in supplier selection is identify- ing relevant and suitable criteriafor inclusion in the assessment andselectionprocess (Xueetal., 2018).Themostcommonlyused criteriaforsupplierselectioncanbecategorizedintothreegroups ofeconomic, environmental, andsocial criteria(Fallahpour etal., 2017).Kannanetal.(2020)proposedanewcategorizationofeco- nomic, social, and circularcriteria forsupplier evaluation, where the circular production system is a special manifestation of the environmental criteriathat pertains to the supplier evaluationin the circularorclosed-loop supply chains (Govindan etal., 2020).

Cost, quality, flexibility, and service are among the most widely used economic criteria in the literature (Jain and Singh, 2020; Liu et al., 2019; Lo et al., 2018). Dickson (1966), one of the pi- oneers in supplier selection, has proposed 23 criteria for sup- plier selection based on the preferences of the purchasing man- agers. Noci (1997) considered environmental concerns and intro- duceda ratingsystemto evaluate suppliers.Environmental man- agement systems, green technology, green packaging, and pol- lution control are some of the widely used environmental cri- teria in the literature relating to sustainable supplier selection (HaeriandRezaei, 2019;Mishra etal.,2019;Qazvinietal.,2019).

Đali´c etal.(2020) usedseven environmentalcriteria:greencom- petencies,recycling,environmentalimage,environmentalmanage- ment system, environmentally friendly products, resource con- sumption, and pollution control for supplier evaluation in green supplychains.Minaetal.(2021)developedahybridcircularsup- plierselectionmethodtoevaluateandranksuppliersinthepetro- chemical industry using delivery, quality, circular, and capability criteria.

The increasing attention to social issues hasled to the emer- gence of sustainable supply chain management with economic, social, and environmental considerations (Luthra et al., 2017).

Khan et al. (2018) embarked on supplier evaluation in the au- tomotive industry by considering economic, social, and environ- mental factors. They considered social commitment, health and safety, information disclosure, employer rights, and employment practices associal criteriain their study. A newmethod entitled full consistency method for evaluating sustainable suppliers was proposedbyDurmi´c(2019).Theproposedmethodconsideredeco- nomic,social,andenvironmentaldimensionsintheevaluationpro- cess.Safetyandhealthatwork,employees’rights,disclosinginfor- mation,andrespectforrightsandpolicieswereamongthecriteria usedinhis study.Kannanetal.(2020) proposedan approachfor supplierselection inthe wire-and-cableindustry usingeconomic, social, andenvironmental criteria. The social criteriaproposed in their research are job creation, health andsafety systems, rights of stockholders, rights of employees, and information disclosure.

Amirietal.(2021)embarkedonsupplierevaluationintheautomo- tive industryandused fuzzyBWMandeconomic,social,anden- vironmentalcriteria to consider employee satisfaction, after-sales service,easeofcommunication,andemployee traininganddevel- opmentassocialsub-criteria.

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Conventional or forward supply chains convert raw materials intofinalproductsinalinearfashionwithnoregardtotheend-of- life (EOL)products;however,sustainableorreversesupplychains operatein acircularfashion byusingEOL products asrawmate- rials. Wangetal.(2019)identifiedclosed-loop,green,andsocially responsible supply chainsasthree main categoriesofsustainable supplychains.Althoughthesetermshavebeenusedinterchange- ablyintheliterature,thereareimportantdifferencesamongthem (González-Sánchezetal.,2020).Whilegreenandsociallyresponsi- blesupplychainsengagemanufacturersandcustomerstopromote cooperationthatwillresultinenvironmentalandsocialgains,cir- cular or closed-loop supply chains pursue remanufacturing and zero-wastepractices.Circularsupplychainsareself-sustainingpro- duction systems designedtoreduce productionwasteand return EOL products to the production cycle (Genovese et al., 2017). In thisstudy,weconsidereconomic,social,andcircularenvironmen- tal factors toconcentrate onthe remanufacturing andzero-waste benefitsofcircularsupplychains.Table1presentsthemostwidely usedcriteriaforsustainablesupplierselection.

2.2. Supplierselectionmethodsinsustainablesupplychains

One of the most significant advances in sustainable supply chain management has been the development of various green and socially responsible supplier evaluation and selection meth- ods. The MCDMmethods are oftenused forsupplier selection in supply chainmanagement (Asadabadi, 2018).Withthe increasing attention tosustainable supplychain management, MCDMmeth- ods have been widely used to solve green and socially respon- sible supply chain problems with multiple and often conflicting criteria because of their flexibility and simplicity(Memari et al., 2019). The AHP is a popular MCDM method for solving sup- plierevaluationandselection problems(Qazvinietal.,2019).The conventional AHP requires exact values to express the decision maker’s opinionsthrougha seriesofpairwise comparisons.How- ever,real-worldproblemsofteninvolveambiguitiesanduncertain- ties. Thefuzzy AHP, basedon Zadeh’sfuzzy settheory (1965), is developed toovercomethisweaknessinsustainablesupplychain management (Ecer, 2020; Gold and Awasthi, 2015; Mani et al., 2014). Other MCDM methods, such as analytic network process (ANP) (Giannakis etal.,2020; Tavanaetal., 2017) andfuzzyANP (Bakeshlou et al., 2017; Mina et al., 2014), the decision making trialandevaluationlaboratory(DEMATEL)(Gören,2018),thetech- nique of order preference similarity to the ideal solution (TOP- SIS) (Sureeyatanapas et al., 2018) and fuzzy TOPSIS (Rani et al., 2020; Liet al., 2019; Yu et al., 2019), the VIseKriterijumska Op- timizacija IKompromisno Resenje (VIKOR), alsoknown asmulti- criteriaoptimization andcompromisesolution method(Feietal., 2019), and fuzzy VIKOR (Liu et al., 2019; Awasthi and Govin- dan., 2016), andthe dataenvelopmentanalysis(DEA)(Dobos and Vörösmarty, 2019) and fuzzy DEA (Tavassoli et al., 2020) are amongthemostwidelyusedmethodsinsustainablesupplychain management. The BWM is a new MCDM method proposed by Rezaei (2015). Rezaei et al. (2016) proposed a BWM-based ap- proach forsupplier selection in the foodindustry by considering both traditionalandsustainable criteria. EachMCDMmethod has uniquestrengthsandweaknesses.

ManyhybridMCDMmethodshavebeenproposedtocapitalize on the strengths and avoid the weaknesses of the singleMCDM methods. The followingpresents some ofthestudies which have used hybridMCDMmethodsforsustainableorgreensupplier se- lection. Luthraetal.(2017)usedahybridMCDMmethodforsus- tainable supplierselection.Theycomputedtheweights ofthecri- teriaandsub-criteriawithAHPandrankedthesupplierswiththe VIKORmethod.Azimifardetal.(2018)suggestedahybridAHPand TOPSIS methodfor sustainable supplier selection in the steel in-

dustry.Banaeianetal.(2018)usedagreensupplierselectionprob- lem in the agri-food industry to compare the results from three methods,i.e.,TOPSIS,VIKOR,andgreyrelationalanalysisinafuzzy environment. Kusi-Sarpong et al. (2019) proposed a hybrid ap- proachby integrating theBWM andVIKORfor supplier selection incircularsupplychains.TheBWMwasusedtodeterminetheim- portanceweightoftheevaluationcriteria, andtheVIKORmethod wasusedtorankthesuppliers.Govindanetal.(2020)proposedan integratedfuzzyANPandfuzzyDEMATELforsupplierevaluationin thecircularsupplychains. Theycalculatedtheweights ofthecri- teriaandthenconsideredtheinterdependenciesamongthemwith thefuzzyDEMATEL methodtorankthe suppliersinthe automo- tive industry.Kannan etal.(2020) proposed a practical approach forsustainable supplierselectioninthecircularsupplychainsus- ing MCDMmethods. Theyused fuzzyBWMto weighthecriteria inthewire-and-cable industryandthe intervalVIKOR methodto rankthesuppliers.

In addition to MCDM methods, artificial intelligence meth- ods are also widely used for supplier evaluation and selection problems. Tahriri et al. (2014) developed an integrated fuzzy Delphi and FIS method for supplier evaluation. Amindoust and Saghafinia (2017) introduced a practical framework for supplier evaluation ina sustainable supply chain using FIS. Theydemon- stratedtheeffectivenessandefficiencyoftheirproposedapproach in the textile industry. Similarly, an efficient FIS-based approach wasproposed by Ghadimi etal.(2017)for evaluatingthe suppli- ersintheautomotiveindustrybyconsideringtheeconomic,social, andenvironmentalsustainabilityfactors.JainandSingh(2020)de- velopedanapproachforsupplierselectionbasedonFIStoevaluate suppliersinthelarge-scaleironandsteelindustry.

Moreover, the combination of MCDM methods and arti- ficial intelligence has received particular attention in recent years. Amindoust (2018) presented a novel method for eval- uating resilient and sustainable suppliers using FIS and DEA.

Minaetal.(2021)proposed ahybridMCDMandFISapproachfor circularsupplierselection.Theyfirstcalculatedtheweightsofsub- criteriausingfuzzyAHPandascoreforeachsupplierusingTOP- SIS.Finally,theyusedFIStoevaluateandselectthemostsuitable suppliers. Table 2 presents some of the most recent and widely usedmulti-criteria supplierselectionmethods insustainablesup- plychainmanagement.

2.3. Best-worstmethod

The BWM proposed by Rezaei (2015) operates based on the systematic pairwise comparison of multiple criteria. Initially, the best and the worst criteria are identified by the decision-maker.

Thedecision-makerthencomparesthebestandtheworstcriteria withtheothercriteriaintwosubsequentsteps.Ascaleconsisting ofpointsfrom1 to9 isused todetermine thepairwise compar- isons’strengths.Next,anoptimizationproblemisformulatedusing thetwopairwisecomparisonsetsasinputs.Theweightsofcriteria areidentifiedastheoptimumresultofthisoptimizationproblem.

Thesmallnumberandthestrongconsistencyofpairwisecompar- isonsaretheuniquefeaturesoftheBWM.Forexample,AHPneeds n(ⁿ−1)/2pairwisecomparisonsforformulatingapairwisematrix betweenn,which isindicative ofthe numberof criteria. Incon- trast, theBWM only requires2n−3 pairwise comparisons. Thus, with the increase in “n,” a greater efficiency is gained from the BWMcomparedtocompetingmethodssuchasAHP(Rezaei,2015).

3. Problemstatementandtheproposedapproach

Supplier selection is a challenging problem in manufacturing.

Mostmanufacturingcompaniesaremoremindfulofthischalleng- ing problemsince supplierslie atthestartingpoint ofthechain.

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Table 1

The sustainable criteria for supplier selection.

Criteria

Fallahpour etal.

(2017)

Luthra etal.

(2017)

Arabsheybani etal.(2018)

Awasthi etal.

(2018)

Azimifard etal.

(2018) Gören (2018)

Kannan (2018)

Vahidi etal.

(2018) Abdel- Baset etal.

(2019) Ahmadi and Amin (2019)

Alikhani etal.

(2019)

Guarnieri and Trojan (2019)

Li etal.

(2019) Liu etal.

(2019) Memari etal.

(2019)

Mohammed etal.

(2019)

Pishchulov etal.

(2019) Yu etal.

(2019) Govindan etal.

(2020) Jia etal.

(2020) Kannan etal.

(2020) Stevi´c etal.

(2020)

Cost(E1) √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √

Quality(E2) √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √

On-timedelivery (E3)

√ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √

Reputation(E4) √ √ √ √ √ √

Flexibility(E5) √ √ √ √ √ √ √ √ √

Technological capacity(E6)

√ √ √ √ √ √ √ √ √ √ √ √ √ √

R&D(E7) √ √ √ √

Responsiveness(E8) √ √ √ √

Financialcapability (E9)

√ √ √ √ √ √

Productivity(E10) √ √

Serviceefficiency (E11)

√ √ √ √

Risk(E12) √ √ √

Attentiontoenergy consumptionin productionand recyclingproducts (C1)

√ √ √ √ √ √ √ √ √ √ √

Attentiontoair pollutionin productionand recyclingproducts (C2)

√ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √

Producingproducts usingrecyclableraw materials(C3)

√ √ √ √ √ √ √ √ √ √ √ √

Utilizingcleanand greentechnologyin productionand recyclingproducts (C4)

√ √ √ √ √ √ √

( continued on next page )

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Table 1 ( continued )

Criteria Fallahpour

etal.

(2017)

Luthra etal.

(2017)

Arabsheybani etal.(2018)

Awasthi etal.

(2018)

Azimifard etal.

(2018) Gören (2018)

Kannan (2018)

Vahidi etal.

(2018) Abdel- Baset etal.

(2019) Ahmadi and Amin (2019)

Alikhani etal.

(2019)

Guarnieri and Trojan (2019)

Li etal.

(2019) Liu etal.

(2019) Memari etal.

(2019)

Mohammed etal.

(2019)

Pishchulov etal.

(2019) Yu etal.

(2019) Govindan etal.

(2020) Jia etal.

(2020) Kannan etal.

(2020) Stevi´c etal.

(2020)

Employing eco-friendly materialsfor packagingproducts (C5)

√ √ √ √ √ √ √ √ √

Respecting environmental regulationsand standards(C6)

√ √ √ √ √ √ √ √ √

Wastemanagement (C7)

√ √ √ √ √ √ √ √ √ √ √ √ √ √

Environmental managementsystem (C8)

√ √ √ √ √ √ √ √ √ √

Managingreturned products(C9)

√ √

Reverselogistics (C10)

√ √ √ √ √ √

Humanrights(S1) √ √ √ √ √ √ √ √ √ √ √ √ √

Product responsibility(S2)

√ √

Occupationalhealth andsafety managementsystem (S3)

√ √ √ √ √ √ √ √ √ √ √ √ √ √

Information disclosure(S4)

√ √ √ √ √ √ √ √

Socialcommitment (S5)

√ √ √ √ √

Ethicalissues(S6) √ √ √ √

Employment practices(S7)

√ √ √

Socialmanagement (S8)

√ √

Attentiontothe childandforced laborproblem(S9)

√ √ √

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Table2 Multi-criteriadecision-makingmethodsforsustainablesupplierselection. MCDM Method Girubha etal. (2016) Govindan and Sivaku- mar(2016) Ahmadi etal. (2017)Falahpour etal.(2017) Hamdan and Cheaitou (2017) Gupta and Barua (2017) Kumar etal. (2017) Luthra etal. (2017) Wan etal. (2017) Awasthi etal. (2018) Cheraghalipour andFarsad (2018)Gören (2018) Lo etal. (2018)Mohammed etal.(2018) Abdel- Baset etal. (2019) Alikhani etal. (2019) Dobos and Vörös- marty (2019) Govindan etal. (2019)Mohammed etal.(2019) Moheb- Alizadeh and Handfield (2019) Qazvini etal. (2019) Rashidi and Cullinane (2019) Wu etal. (2019) Chen etal. (2020) Gargand Sharma (2020) Govindan etal. (2020) Kannan etal. (2020) AHP/fuzzy AHP

√√√√√√√ ANP/fuzzy ANP

√√√√ TOPSIS/fuzzy TOPSIS

√√√√√√√√ VIKOR√√√√√√√ BWM/fuzzy BWM

√√√√√√√ DEA√√√√ DEMATEL√√√ ELECTRE√√√√

Theirpoorselectioncanleadtodetrimentalconsequences,suchas increasedcosts,reducedproduct quality,anddecreasedefficiency inthechainatlarge.The supplierselection problemina holding companyismorecomplex,costly,andtime-consumingbecauseof thelargenumberofsubsidiariesindifferentdomains.Wepropose a dynamic DSS for sustainable supplier selection in the circular supplychains.The proposed DSSiscomposed ofa hybridMCDM model management component, a database management system poweredbymachinelearning,andauserinterfaceequippedwith avisualizationdashboard. Someoftheuniquefeaturesofthepro- posedDSSinclude:

• Thereis noneed forexpertinterventionin thecollectionand processingofuser’sjudgmentsconcerning thecriteriaweights since the system collects, processes, and interactively deter- minestheconsistencies.

• Thereisnoneedtoscoreeachsupplieroneachcriterionsince machine learningis used tomaintain andsynthesize detailed criteriascores concerning historicalsupplier selection engage- ments.

• Despiteitscomplexity,theproposedDSShasauser-friendlyin- terfacewith a visualization dashboard andfacility forcriteria selectionandcomparisonusinglinguistictermsandphrases.

Fig. 1 illustrates the structure of the proposed DSS, which is composed ofa database management system, userinterface, and modelmanagement components.The databasecomponent main- tainshistoricaldataonsupplierscoresforall criteriaandalluser engagements.Theuserinterfacemanagesallinteractions withthe user. The first user interaction step involves industry selection, where the user is required to identify a specific industry from the holding company’s portfolio of companies. The user is then askedto choosetherelevantcriteriaforhis/hersupplier selection problemfromacomprehensivesetofeconomic,social,andcircu- larcriteria. Theuserisalsorequiredtodeterminetheimportance ofhis/herselectedcriteria.The modelmanagementcomponentis composed of the fuzzy BWM and FIS modules. The fuzzy BWM moduledeterminestheimportanceweightsofthecriteria,andthe FIS ranks the potential suppliers according to the user’s prefer- encesandhistoricaldata.Fuzzylogicisusedtohandletheuncer- taintiesandambiguitiesinherentinsubjectivejudgments.Theuser is provided with a visualization dashboard to study the suppli- ers’performanceoneachcriterion.Inaddition,theoverallsupplier scoresandrankings areprovidedthroughtheuserinterfacedash- board.Allrelevantdataandscoresoneach supplierarestoredin thedatabaseandretrievedandsynthesizedusingmachinelearning duringeachsupplierselectionengagement.

Theproposedframeworkinvolvesninesteps,asfollows:

Pre-engagementstep:Inthisstep,theuserselectsaspecificin- dustrywithintheholdingcompanyinsearchofasustainablesup- plier.

Step 1. In this step, the user identifies relevant criteria from a comprehensivelist ofsustainable supplierselection criteriaavail- ableinthedatabasemanagementsystem. Iftheuserfeelsacrite- rionismissing,heorsheisprovidedwiththeopportunitytoadd themissingcriterionandupdatethesystem.Allcriteriainthesys- temareclassifiedintoeconomic,social,andcirculargroupsbased ontherelatedliterature,expertopinions,andprevious userinter- actionsandengagements.

Step2. Inthisstep,theuserisrequiredto determinetheimpor- tanceofhis/herselectedcriteriabyusingTable3aandclassifying theselectedcriteriaintofivegroups.Forexample,moderatelyim- portantcriteriaareassignedtoClass3,whileveryimportantcrite- riaareassignedtoClass5.Allsubsequentsteps aremanagedau- tomaticallybythemodelmanagementcomponentoftheDSS.

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Fig. 1. The proposed decision support system.

Table 3

Importance levels and fuzzy numbers.

3a. Importance levels and their associated classes

3b. Triangular fuzzy numbers and their associated class differences ( Tavana et al., 2020 ).

Class Importance level

1 Very low importance

2 Low importance

3 Moderate importance

4 High importance

5 Very high importance

Linguistic term Triangular fuzzy numbers Class difference

Equally important (1,1,1) 0

Slightly important (2/3,1,3/2) 1

Moderately important (3/2,2,5/2) 2

Very important (5/2,3,7/2) 3

Absolutely important (7/2,4,9/2) 4

Step 3. Inthisstep, thecriteriaclassification data collectedfrom the userinStep2is usedto identifythebestandthe worstcri- teria,constructthebest-to-othersandtheothers-to-worstvectors, identifytheclass differences,anddesignatetheappropriate fuzzy numbers according to Table 3b. The model management system first identifies thebestandworst criteria. The bestcriterion may belong to classn(

∀

ⁿ≥2), andtheother criterion maybelong to class n-1. In that case, the two criteriahave one class difference (slightly important),andthetriangularfuzzynumbers2/3,1,and 3/2 are used by the system for the pairwise comparison score.

Similarly,ifthetwocriteriabeingcomparedhavetwoclassdiffer- ences(moderatelyimportant),thetriangularfuzzynumbers3/2,2, and5/2 are usedby thesystemforthe pairwise comparison.Af- ter completingall pairwise comparisons, thefuzzy best-to-others vectorisconstructed.Similarly,thefuzzyothers-to-worstvectoris createdthroughpairwisecomparisonsbetweentheworstcriterion andtheother criteria. Forexample,iftheworst criterion belongs toclassoneandtheother criterionbelongstoclassfour,theclass difference ofthetwocriteriaisthree,and,thereby,thetriangular

fuzzynumbers5/2,3,and7/2areusedbythesystemforthepair- wisecomparison.

Step 4. In this step, the fuzzy weights of criteria are calculated using the nonlinear mathematical model proposed by Guo and Zhao (2017). The deterministic formof this modelwas first pre- sentedbyRezaei(2015)asfollows:

Min

γ

^{∗ (1.a)}

s.t.

^ww^b_j −w_{b j}

^≤

γ

^∗

∀

^{j (1.b)}

_w^w_w^{j −wjw}

^≤

γ

^∗

∀

^{j (1.c)}

j

wj=1 (1.d)

w_j>0 (1.e)

AccordingtoRezaei(2015),theoptimalweightsforcriteriare- sultwhen ^w_w^b

j =w_{b j}and_w^wj

w =w_jw. In other words,the maximum absolute differences among

|

^ww^b_j−w_{b j}

|

^and

|

w^ww^j −w_jw

|

^{should be}

lessthantheobjectivefunctiongiveninconstraints(1.b)and(1.c).

Accordingtotheconstraint(1.d),thecriteria’stotalweightshould beequalto1.Finally,constraint(1.e)guaranteesthattheobtained weightsarepositive.Guo andZhao(2017)presentedModel(1)in fuzzyformasfollows:

Min

γ

^{∗ (2.a)}

s.t.

w^l_b,w^m_b,w^u_b

w^l_j,w^m_j,w^u_j

−

w^l_{b j},w^m_{b j},w^u_{b j}

^≤

⁽ γ

^∗,

γ

^∗,

γ

^∗

) ∀

^{j (2.b)}

w^l_j,w^m_j,w^u_j

w^l_w,w^m_w,w^u_w

−

w^l_jw,w^m_jw,w^u_jw

^≤

⁽ γ

^∗,

γ

^∗,

γ

^∗

) ∀

^{j (2.c)}

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B. Alavi, M. Tavana and H. Mina Sustainable Production and Consumption 27 (2021) 905–920 Table 4

Consistency index for fuzzy best-worst method.

Linguistic terms Equally important slightly important moderately important Very important Absolutely important

A ˜ BW (1,1,1) (2/3,1,3/2) (3/2,2,5/2) (5/2,3,7/2) (7/2,4,9/2)

CI 3 3.8 5.29 6.69 8.04

j

w^l_j+4×w^m_j +w^u_j

6 =1 (2.d)

w^l_j≤w^m_j ≤w^u_j (2.e)

w^l_j≥0 (2.f)

where (^wl_j,w^m_j,w^u_j) ^{are the triangular fuzzy numbers repre-} senting the importance weights of criterion j. In addition, (^wl_{b j},w^m_{b j},w^u_{b j})^and(^wl_jw,w^m_jw,w^u_jw) ^{represent the fuzzy best-to-} othersandthefuzzyothers-to-worstvectors,respectively.Further- more,

γ

^{∗,isavariableusedtocalculatethe}consistencyratios.The optimal fuzzyweights ofcriteria are obtainedby runningModel (2)withGAMSsoftware.

Step5.Inthisstep,theconsistencyofthepairwisecomparisons is calculated. Since a rational pattern is employed for the pair- wise comparisonsin Step3, theconsistency ofpairwise compar- isons isconfirmed.However, aconsistencyratioisobtainedusing Eq.(3)andTable4toconfirmthisclaim.Consistencyratiosclose tozeroarepreferredbecausetheyrepresenthigherconsistency.

CR=

γ

^∗

CI (3)

whereCRandCIareanabbreviationofconsistencyratioandcon- sistencyindex,respectively.

Step 6. Inthisstep,the historicaldata frompreviousevaluations are retrievedfromthedatabasemanagement systemandusedto calculate a performance score for each criterion selected by the user in Step 1. A supplier score for each classification (i.e., eco- nomic, social, andcircular)isobtained asaweightedsumofthe criteriaweightstimesthescoresretrievedfromthedatabase.

Step 7. Inthis step,themembership functionsforthe input and outputvariablesareformedtobeusedinaMamdaniFIS.Theeco- nomic, social,and circular criteriaare considered the input vari- ables,andthesuppliers’scoresaretheoutputvariablesintheFIS.

Figs. 2a and 2b presentthe membership functionsfor the input andoutput variables,respectively. The numberoffuzzyinference rules isafunctionofthenumberofinput variablesandmember- ship functions. An increase in the number of membership func- tions fortheinputvariableswillincrease thenumberofrulesex- ponentiallybutmaynotimproveaccuracy.However,anincreasein thenumberofmembershipfunctionsfortheoutputvariables has no impact onthenumber ofrules,but itdoesimproveaccuracy.

Tothisend,weconsideredfivemembershipfunctionsfortheinput variablesandsevenmembershipfunctionsfortheoutputvariables to keep the number of rules manageable andavoid unnecessary complexityandprocessingtime.

Step 8. In thisstep,the fuzzy inference rules are extractedfrom expertknowledge.Indoingso,expertsareaskedtoconnectalink between the input and output variables in the FIS using an “if- then” questionnaire. Therelationship betweentheinput andout- putvariablesisformedasfollows:

Ifinput1be...andinput2be...andinput3be...thenoutputis....

Fig. 2. Membership functions 2a. Input variables 2b. Output variables.

Considering the above relationship, each of the five member- shipfunctionsisplacedintheblankspacepertainingtoinput1,in- put2,andinput3.Therefore,itispossibletowrite5³ non-iterative ruleswiththreeinputvariablesandfivemembershipfunctions.To constructtheFIS,expertsare askedtoassign asuitable member- shipfunctionfortheoutputvariablesintheblankspacespertain- ingtotheoutputs,accordingtoeachrule.

Step9. Inthisstep,aneconomic,social,andcircularscoreforeach potentialsupplierisobtainedfromStep6andinsertedintherule reviewerasaninputintheproposedFIS.Next,afinalscoreiscal- culatedforeachsupplier,andthesuppliersarerankedaccordingto theirfinalscores.Thesupplierwiththehighestscoreisselectedas themostpreferredsupplier.

Post-engagementstep:Inthisstep,theuserisaskedtoprovide three periodicalreviewsof the selectedsupplier in Step8 on all criteriafromStep1 inonemonth,threemonths,andsixmonths aftertheselectionprocessiscompleted.Thisinformationismain- tainedinthedatabaseandwillbe usedinStep5forfutureusers whomightbeconsideringthissupplieronthesamecriteria.

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B. Alavi, M. Tavana and H. Mina Sustainable Production and Consumption 27 (2021) 905–920

Fig. 3. The proposed economic, social, and circular criteria.

4. Casestudy

In this section, we present a case study at the Gulf Petro- chemicalConsortium(GPC)¹,thelargestinternationalpetrochemi- cal companyoperatinginthePersianGulf,todemonstratetheap- plicability of the proposed DSS. GPC is a holding company with a controlling interest in 18 subsidiarycompanies andmore than 45milliontonsofoutputcapacity.Thecompanyisthefirstpetro- chemical consortiumintheMiddleEastandNorth Africatocom- mit to sustainability and promotion of a circular economy. The company recentlycommissionedthefirstcarbondioxiderecovery plant to manage greenhouse gas emissions. Ammonia, methanol, urea, ethylene, sulfur, and their derivatives are among the core products produced and sold by GPC worldwide. The majority of plastics waste worldwide ends up in landfills and incinerators.

Plasco²isa chemicalrecycling companyowned byGPCtomeetthe ambitiouscirculareconomytargetsestablishedbyGPC.Plascouses chemicalrecyclingtoprocessplasticswasteintorawmaterialsfor the downstream companies andmanufacturing plants owned by GPC. Inthiscasestudy,Plasco is searchingforasustainable sup- plierforpolyethyleneglycol.

Pre-engagement step:TheuserselectedPlasco fromthelistof 18subsidiarycompaniesownedbyGPC.

Step1. In thisstep,the userretrieved allavailable criteriainthe systemforpolyethyleneglycolsupplierselection.Thesystemlisted 31criteriaclassifiedinto12economic,10circular,and9socialcri- teria(seeFig.3).After carefulconsiderationandconsultationwith thecompanyexperts,theuserselected14criteriacomposedoffive

1The name of the company is changed to protect the anonymity of the holding company.

2The name of the company is changed to protect the anonymity of this sub- sidiary company.

Table 5

Selected and classified criteria by the user.

Aspect Class

1 2 3 4 5

Economic - E6 E3, E5 E1 E2

Circular C9 - C6 C2 C3

Social S5 S4 S3 S9 S1

economic(E1,E2,E3,E5,andE6),fourcircular(C2,C3,C6,andC9), andfivesocialcriteria(S1,S3,S4,S5,andS9).

Step2. In thisstep,the useridentified theimportance ofthe 14 selectedcriteriabyusingTable3aandclassifyingthesecriteriainto fiveclasses(1through5),asshowninTable5.

Step3. Inthisstep, thebest-to-othersandothers-to-bestvectors wereformed usingtheclassificationsmadeinStep2.TheClass5 criteria(i.e.,E2, C3, andS1)are considered the bestcriteria, and thecriteriaplacedinthelowestclass(i.e.,E6,C9,andS5)arecon- sideredtheworstcriteria.Thebest-to-othersvectorsarepresented inTable 6,andthe others-to-worst vectorsare showninTable 7 usingthepatternpresentedinTable3b.

Step 4. In this step, the weights of criteria were calculated us- ing the Guo and Zhao’s (2017) model. The fuzzy best-to-others andothers-to-worstvectorswereruninGAMS softwareusingthe PATHNLPsolver. For example,for calculatingthe weights ofeco- nomics’criteria,theproposedmodelbyGuoandZhao(2017)was developedusingthefuzzybest-to-othersandothers-to-worstvec- tors presented in Table 6a andTable 7a, respectively. The model developedforcalculatingthe weightsofeconomic criteriaispre- sentedinAppendix.

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B. Alavi, M. Tavana and H. Mina Sustainable Production and Consumption 27 (2021) 905–920 Table 6

The best-to-others vectors for the economic, social, and circular criteria 6a. Economic criteria

6b. Circular criteria 6c. Social criteria

Best Others

E1 E2 E3 E5 E6

E2 (2/3,1,3/2) (1,1,1) (3/2,2,5/2) (3/2,2,5/2) (5/2,3,7/2) Best Others

C2 C3 C6 C9

C3 (2/3,1,3/2) (1,1,1) (3/2,2,5/2) (7/2,4,9/2) Best Others

S1 S3 S4 S5 S9

S1 (1,1,1) (3/2,2,5/2) (5/2,3,7/2) (7/2,4,9/2) (2/3,1,3/2)

Table 7

The others-to-worst vec- tor for the economic, so- cial, and circular 7a. Economic criteria 7b. Circular criteria 7c. Social criteria

Others Best E6 E1 (3/2,2,5/2) E2 (5/2,3,7/2) E3 (2/3,1,3/2) E5 (2/3,1,3/2) E6 (1,1,1) Others Best

C9 C2 (5/2,3,7/2) C3 (7/2,4,9/2) C6 (3/2,2,5/2) C9 (1,1,1) Others Best

S5 S1 (7/2,4,9/2) S3 (3/2,2,5/2) S4 (2/3,1,3/2) S5 (1,1,1) S9 (5/2,3,7/2)

Table 8

The optimal weights of criteria.

Criteria Fuzzy weight Defuzzified weight γ^∗

w ^l_j w ^m_j w ^u_j ^wlj+4×w₆^mj+wuj

E1 0.217 0.273 0.281 0.265 0.289

E2 0.269 0.324 0.324 0.314

E3 0.116 0.154 0.180 0.152

E5 0.116 0.154 0.180 0.152

E6 0.101 0.119 0.121 0.117

C2 0.257 0.320 0.382 0.320 0.216

C3 0.338 0.389 0.440 0.389

C6 0.162 0.183 0.235 0.188

C9 0.103 0.103 0.103 0.103

S1 0.289 0.331 0.331 0.324 0.299

S3 0.150 0.194 0.220 0.191

S4 0.087 0.114 0.131 0.113

S5 0.079 0.088 0.090 0.087

S9 0.252 0.291 0.299 0.286

Table 8 presentsthe optimalweights of criteriaandthe opti- mal value of

γ

^{∗ calculated by} implementingthe proposedmodel inGAMSsoftwareandPATHNLPsolver.

Step5. Inthisstep,Eq.(3)andTable4wereusedtocalculatethe consistencyratio(^CR=^γ_CI^∗)^{ofthepairwise}comparisonsas0.043, 0.027,and0.037fortheeconomic,circular,andsocialcriteria,re-

spectively. These near-zero ratios confirm the consistency of the pairwisecomparisons.

Step6. Inthisstep,thehistoricalpolyethyleneglycolsuppliereval- uationdatafor159sustainablesupplierson14evaluationcriteria were retrieved.Table 9presents thedata fortensupplierson 14 criteriaforthesakeofbrevity.

Next, the system calculated the weighted sum of the criteria weightsmultipliedbythesupplierscorestodeterminethesuppli- ers’ economic, circular,and social scores. The suppliersores pre- sentedinTable10wereusedastheFISinputvariables.

Step7. In thisstep,the membership functionsfortheinput and outputvariableswereformedintheMATLABR2020asoftwareus- ing fuzzyinferencesystemEditor GUItoolbox. Theeconomic, so- cial,andcircularcriteriawereconsidered theinputvariables,and the suppliers’scores were considered the output variables.Fig. 4 presentstheoverallstructure oftheFISforthesupplier selection casestudyatPlasco,andFigs.5aand5bpresentthemembership functionsfortheinputandoutputvariables.

Step8. Thefuzzyinferencerules,determinedbytheexperts,were usedtolinktheinputandoutputvariables.Thesupplierselection FIS in this case study consisted of three input variables formed fromfivemembershipfunctions.Thisrequiredobtaining125rules fromexpertknowledgein theFIS.Fig. 6presentsan overviewof therulesdefinedintheMATLABR2020a softwareusingfuzzyin- ference system Editor GUI toolbox. Fig. 7 presents the rules re- sultingfromthe relationship betweeninput andoutput variables depictedinthree dimensions.Fig.7apresentsthefuzzyinference rulesfortheoutput variableandthe economicandcircularinput variables. Fig. 7b presents the fuzzyinference rules forthe out- putvariable andthe economicand socialinput variables. Finally, Fig. 7c presents thefuzzy inference rules forthe output variable andthecircularandsocialinputvariables.

Step9. In thisstep,theinput valuesobtained inStep6 foreach supplierwereinsertedintherulereviewerboxintheFIStocalcu- late,andthefinalscoreofthesuppliersiscalculatedastheoutput.

These operationsfor Supplier 1 are shown in Fig. 8. Similar op- erationsareperformedfortheremaining ninesuppliers. Table11 presentsthefinalscoreofthesuppliersandtheirranks.

Post-engagementstep:Asshownhere, Supplier5wasselected asthemostsuitable supplierofpolyethyleneglycolforPlasco.Af- terworkingwithSupplier5,theuserisexpectedtoreturntothe systemand evaluate the selected supplier according to 14 selec- tioncriteriainStep2inonemonth,threemonths,andsixmonths.

Thesesupplierreviewdataarestoredinthesystemforfuturese- lectionengagements.

Next, we constructed four sensitivity analysis scenarios to demonstrate theproposed method’s applicability androbustness.

Each scenario considers changing the class corresponding to the bestcriterionwiththeother criteria.AsshowninTable12,these changesare enforcedintheeconomic, circular,andsocial dimen- sionssimultaneously.

Using steps 3 to 6, suppliers’ economic, circular, and social scoresarecalculatedineachscenarioandpresentedinTable13.

Finally, the suppliers’ final scores are calculated foreach sce- nariousingtheproposedFIS(Steps7and8).Thesupplierrankings arepresentedinTable14.

Table14showsthe revisedsupplier scoresandrankingsinre- sponse to the changes in the criteriaweights. These changes in- dicate a reasonablelevel of sensitivity to the criteria weights as expectedinanyrobustMCDMmodel.

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B. Alavi, M. Tavana and H. Mina Sustainable Production and Consumption 27 (2021) 905–920

Fig. 4. The proposed fuzzy inference system.

Fig. 5. Fuzzy inference system Membership functions 5 a. Membership functions for the input variables 5 b. Membership functions for the output variables.

Fig. 6. Fuzzy inference system rules.

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B. Alavi, M. Tavana and H. Mina Sustainable Production and Consumption 27 (2021) 905–920

Fig. 7. Fuzzy inference rules. 7a. Relationship between the economic, circular, and final score of supplier 7b. Relationship between the economic, social, and final score of supplier. 7c. Relationship between the circular, social, and final score of supplier.

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