Globalisation vs. Slowbalisation: a literature review of analytical models for sourcing decisions in supply chain management

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an author's

https://oatao.univ-toulouse.fr/27248

https://doi.org/10.1016/j.arcontrol.2020.04.004

Kandil, Narjes and Battaïa, Olga and Hammami, Ramzi Globalisation vs. Slowbalisation: a literature review of

analytical models for sourcing decisions in supply chain management. (2020) Annual Reviews in Control, 49.

277-287. ISSN 1367-5788

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Globalisation

vs.

Slowbalisation:

a

literature

review

of

analytical

models

for

sourcing

decisions

in

supply

chain

management

Narjes

Kandil

a,∗

_,

_Olga

_Battaïa

b

_,

_Ramzi

_Hammami

c

a Institut Supérieur de l’Aéronautique et de l’Espace, 10, avenue Édouard-Belin Toulouse - 31055 France b Kedge Business School, 680 cours Libération Talence - 33405 France

c Rennes School of Business, 2 Rue Robert d’Arbrissel, Rennes - 35065 France

Keywords: Globalisation Slowbalisation Insourcing Outsourcing Operation research Operations management

a

b

s

t

r

a

c

t

Inthecontextofthemainstreamofglobalisationandanewtrendofslowbalisation,wereviewthe ex-istingliteratureincludingbothempiricalandanalyticalpapersonthesourcingandlocationdecisionsin SupplyChains.Afterdeﬁningthedifferentsourcingstrategies,e.g.,insourcing,outsourcing,offshoringand reshoring,wepresentthedriversforeachstrategyandhowtheycanbeincorporatedintheanalytical modelsinordertohelptooptimizethetakendecisions.Wealsodiscusstheresearchperspectivesinthe ﬁeld.

Contents

1. Introduction ... 277

2. Reviewmethodology... 278

3. Driversandobstaclesforglobalisationandslowbalisation... 279

4. Analyticalmodelsforsourcingdecisionsinglobalisationvs.slowbalisationcontext... 281

4.1. Insourcingandoutsourcingdecision... 281

4.2. Outsourcingcontracts... 282

5. Discussion... 283

6. Conclusion... 286

DeclarationofCompetingInterest... 286

References ... 286

1. Introduction

Globalisationhasbeenastrategicaltrendforthepastdecades, leadingtointernationalsupplychains(SC).Inditex,thetextilegiant owningbrandslikeZaraandOyshoamongothers,workwith1866 suppliers,7235factoriesandover7000storesworlwide Inditex. Airbusengagesmorethan12000supliersaroundtheworldBean Airbussupplier.AccordingtotheWorldTradeOrganization(WTO)

∗ _{Corresponding author.}

E-mail address: kandil.narjes@gmail.com (N. Kandil).

database, the total merchandise exports reached almost 20 000 000MillionUSdollarsin2018.

Recently, though,we are probably witnessinga major change. The Economist refers to the current reshape of globalisation as “Slowbalisation”,atermﬁrstusedbytheDutchtrend-watcher Ad-jiedjBakasin2015. Theworld tradedroppedfrom61%to58% of GrossDomesticProduct (GDP)between2008 and2018. Interme-diate importsand ForeignDirect Investment (FDI)have hada2% dropinthe sameperiod.In 2018,50% ofthe FDIﬂowing inAsia camefromcountriesinAsia,and60%ofFDIinEuropecamefrom theregionGlobalisationhasfaltered(2019).

Globalisation and slowbalisation are trade-related concepts linked to the geographical expansion of the supply chains.

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side the location, the governance mode is an important aspect. Firmsnot onlyhavetodecide wheretolocatetheir activities,but also whether they prefer to keep the control and autonomy of themor to delegate them to external parties.Such decisions are namedasoutsourcing, offshoring,insourcing,reshoringand back-shoring, which are business strategies that affect the organiza-tionalstructure ofaﬁrm.Distinctdeﬁnitions canbefound inthe literature.

Contractor,Kumar,Kundu,andPedersen(2010)define outsourc-ingastheorganizational restructuringofsomeactivities eitherin thehome nation ofthe firm or abroadto external providers. On theotherhand,theyconsideroffshoring asrestructuring thefirm geographicallyfromthe home nationtoa foreign location where thesamecompanyactivitiesareperformedundereitherthe com-pany’s own subsidiary or allocated to a foreign contract vendor.

Y.LewinandW.Volberdab (2011)define offshoringas“thetransfer ofbusinessprocessesandactivitiestoforeignlocations” while stat-ing that outsourcing “refersto services that are sourced froman externalsupplier withinthe boundariesofonecountry”. Arlbjorn andMikkelsen(2014) distinguish theseconcepts from an owner-shipperspective. Theylinkoffshoring to ownedsubsidiaryout of thecountryandoutsourcingtothetransferofownershipand con-troltoa third-party.Theyconsiderinsourcingandbackshoringas relocating the productionto a facilityin another country owned by the company. Johansson and Olhager (2018) state that “ ’Off-shoring’refers to therelocation offirms’activities across the na-tionalborders ofa firm, whilethe term’backshoring’indicates a relocation back to thefirm’s home countryof origin. ’Reshoring’, whichreferstoagenericchangeoflocationwithrespecttoa pre-viousoffshore country, canincludefurther offshoring (i.e. reloca-tiontoanotheroffshorelocation)orbackshoring(i.e.relocationto thehomecountry).”

Bals, Kirchoff,and Foerstl (2016) provide a conceptual frame-workofthe reshoring/insourcingdecisions.Theydefine reshoring witha link to geographical distanceand insourcing asa type of governancemode.Theyenumeratethemotivations andresultsof eachstrategyadopted.Inthefirstpartoftheirpaper,Talamoand Sabatino(2018) presentdifferent definitions related to reshoring from the literature. The authors note that the term “reshoring” isused inthe USAwhilethe terms“back-shoring” and “back re-shoring” areused in Europe. They identifyfour reshoring proce-duresfromtheliterature:in-housereshoringwhich isthe return ofoutsourced productionas partof the company in the country oforigin,outsourcingreshoringwhichismovingtheproductionto thecountryofdestination, reshoringforoutsourcing iswhenthe productionisrepatriatedtothirdsuppliersinthesamecountryas thecompanyandreshoringforinsourcingwhichisthe internalisa-tionofthepreviouslyoutsourcedactivitiesinthedomesticunits.

Kinkel(2014)distinguishesbetweencaptivebackshoring,which isthereturnofactivities fromforeignplantsownedby the com-pany,andoutsourcebackshoring,which isthereturnofactivities fromforeignsupplierstothecompany.

As wecansee,minorsubtledifferencescanbe foundbetween the different deﬁnitions. Without loss of generality, we refer to outsourcingassubcontractingsome ofthecompany’sprocessesto athirdparty.Offshoringismovingtheprocessestoanother coun-trywhilestill having theautonomyandcontrol, it’sa geographi-cal activitywithin the company. Insourcing, isperforming an ac-tivityoraprocessin-house,whichimpliescontrolasagovernance mode, regardless of the geographical location. Reshoring, at last, meansrelocating an activityback toa geographicallyclose coun-try as the ﬁrm’s headquarters. In this case we can differentiate betweennearshoring andbackshoringwhere nearshoring implies anearcountry andbackshoring thesame country.As mentioned byFoerstl,Kirchoff,andBals(2016),thedifferencesbetweenthese strategiesareownershipandlocation.

A comprehensive review of offshoring strategies was con-ductedbyMihalache andMihalache(2016).Theypresenta cross-disciplinarysystematicreview oftheliterature concerningsix fol-lowing types of decision: (i) offshoring decision, (ii) the busi-nessactivitytooffshore,(iii)locationdecision,(iv)ownership de-cision, (v) partner choice decision, and (vi) control/coordination decision. They suggest integrative research directions for each stream.

In their exhaustive review on the outsourcing decision, Tsay, Gray, Noh, andMahoney (2018) point out the disconnection be-tween the Theory of the Firm (ToF) and the Production and Operations Management (POM) literature regarding outsourcing and insourcing decisions. Our study extends their recent review by including the outsourcing contract arrangement between the company and its suppliers, as different contracts lead to differ-ent outcomes. We also add the discussion on the centralized-decentralizedsupplychainsettings.Ina centralizedsupplychain, theproﬁtmaximization orcostminimization concernsthe whole supply chain. In a decentralized supply chain, the members decide independently, each one having a particular goal to achieve.

Inthispaper, wefocus onanalytical modelsthat mayhelp to optimizethesourcingandlocationdecisionsinSupplyChain Man-agement.WebrieﬂypresentourmethodologyinSection2.We dis-cussthedrivers andobstacles forglobalisationandslowbalisation inSection3.InSection4,wefocusontheanalyticalmodellingin two streams ofresearch:theinsourcing-outsourcing decisionand theoutsourcingcontractarrangement.Weconcludethissurveyin

Section5withourinsightsandresearchperspectives.

2. Reviewmethodology

As mentioned in the introduction, several systematic reviews regardingthedifferentsourcingdecisionshavebeenmade.Wedo notintendtoreproducetheworkthathasalreadybeendone.We rather seek to underline the fragmentation in the literature that brings the need to work within multi-disciplinary fields to bet-ter addressthe sourcingandlocation decisions.We choose todo an integrative review for thiscontribution for the following rea-son,presentedbySnyder(2019):“thepurposeofanintegrative re-view isnot to cover all articles ever published on the topicbut rather to combine perspectivesand insights fromdifferent fields or research traditions”. We make an integrative review in which wepresentpapersfromdifferentfieldsinordertosynthesisethe existing analytical models and draw the attention to new theo-retical frameworks and perspectives. We omit papers related to IT service outsourcing andmention only a few related to Third-PartyLogistics (3PL) asthey are relatedto transport outsourcing. We run an advanced search on the Web of Science Core Collec-tiondatabasewiththequery“(TS =(globalisationOR slowbalisa-tionORinsourcingORoutsourcingORoffshoringORmakebuyOR backshoringORreshoringORmake-buyORmake-or-buyOR make-vs-buyORverticalintegration)ANDWC=(Engineering,Aerospace OREngineering, IndustrialOR Engineering,Environmental OR En-gineering, Manufacturing OR Operations Research& Management Science))ANDLANGUAGE:(English)“ withinthetimespan2000– 2020, we get 3994 results.We made a first selection after read-ing theabstractsofthepapers found.Wealsoreachedadditional papers by lookingin thereferences ofthe papersstudied, which isknownasthesnow ball method.Thismethodwasaneffective andrelativelyfastwaytocollectmanyarticlesrelatedtothe sub-jectandseethedifferentanalyticalformulationstheauthorsused. Weincludediscussion papers,pressarticlesandconsultinggroup reports.

Inthenextsection,weﬁrstlydiscussthedriversandobstacles forglobalisationandslowbalisation.

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3. Driversandobstaclesforglobalisationandslowbalisation To understand the drivers andobstacles for globalisation and slowbalisation, we analyse empirical studies, articles from press journals like The Economist and also consulting group reports. Since the latteronesare related toindustrial cases, they present interesting insights on the reasons behind the sourcing decision strategy.

From thedeﬁnitions presentedintheIntroduction,wecansay that outsourcing (offshoring) isthe reversestrategy ofinsourcing (reshoring). Thismeans thattheobstacles ofonedecisionarethe driversofthereverseone.InTables1and2,wepresentthemost frequentmotivationsforeachstrategy.

Mauro, Fratocchi, Orzes, and Sartor (2018) refer to offshoring andreshoringbasedonthegeographicallocation,regardlessofthe governance mode. Theyreview the literature on the motivations forboth strategiesandclassify their findingswithin a framework separatinginternal andexternalenvironment aswellascost effi-ciencyandcustomer perceivedvalues.Inthispaper,weusea dif-ferentclassification, asweare interestedintheanalytical model-ing of thesedrivers.We distinguish financial motivations,related tothecostefficiencyincludinglabourcosts,freightcosts,logistics costs, energycosts,subsidiesandpenalties(nationalsubsidiesfor relocation, penaltiesforlateorders), paymentterms,coordination costs,exchangeraterisks,totalcostofownership,and administra-tivecosts.

The operational drivers are the ones related to quality, lead time and inventory as well as the “Made in” effect, one of the mostinfluentialdriversforreshoring.Wegroupthefollowing mo-tivationsunder the“Madein” effect: firm’sglobalreorganization, redefinition of the global SC, change in firm’s business strategy, need to increase customers’satisfaction andcustomers’ gratitude andwillingnesstopay.

Then there are labour-related motivations, other than the wages, and harder to quantify as home labour market ﬂexibil-ity,labourproductivity,lack/availabilityofskilledworkers,psychic distance and union pressures. Finally, we group the technology/ resources-related drivers, such as lack/availability of infrastruc-ture, product/process/organizational innovation (e.g. automation, lean management),raw material availability, productioncapacity, intellectualpropertyandinnovationpotential.

Globalisation has proven to be effective in cost reduction and hasresulted ininternational supplychains. Many qualitative andempirical studies presentthe drivers andadvantagesof out-sourcing and offshoring. The most frequent ones are low labor wages,cost reduction (ﬁnancial),ﬂexibility andquick responseto changes, promoting competition among supplierswhich leads to better quality and lower price, logistics facilitation (operational), preference to focus on core competencies, easiness of market penetration (strategical) and availability of workers in the host country.

Once globalisation became a widely used strategy, its draw-backs became obvious. An early study by Bettis, Bradley, and Hamel (1992)alreadymentionedthe risksrelatedwith outsourc-ing. The authors saythat once a significant sourcing relationship hasbeenestablished, Westernfirmsbecomelessandlessable or willing overtime tore-emergeasmanufacturingcompetitors due toahighlevelofearlysatisfactionwiththesupplierfirm, increas-ing internal incentives to expand the sourcing relationships and decreasingthe productandprocessdevelopmentcapabilities. The advantages ofoutsourcing according to the authorsare improve-ment in cost, product and possible market share while it does come with a decline of product andprocess technology compe-tences and skills. They say that separating design and manufac-turing is a source of competitive advantage, but it results in a worse coordination, longer lead times, slower skills and

compe-tenceaccumulation.They concludethat outsourcing should focus on areas other than core competences. According to Gilley and Rasheed(2000),outsourcingcouldresultinthelossofoverall mar-ket performance, the loss of long-run Research & Development (R&D)competitiveness,longerleadtimes,largerinventories, com-municationandcoordinationdiﬃculties,lowerdemandfulﬁllment andhighertransaction coststhanplanned.Gertler(2009) analyse theeffectofoffshoringonUSeconomybyconsideringemployment andrehiring.

The drawbacks of locating the activities far from the home countrycanbeconsidered asthedriversforreshoring/insourcing. Related tothe financial aspect, the rise of labourwages in what used to be “lowcost” countries is one ofthe main reasons why firmsrethinktheirlocation decision.Ina reportpublishedbyIAC Partners, a French consulting group, Huygevelde, Ranjbaran, and AchimistatethattheworkerswagesinChina,whichusedtobe20 timescheaperthanthoseinFrance,wereonly5timescheaperin 2017.Theyassume it willonly be 2to 3timescheaper by2022. Another financial argument is the rising cost of fuel and trans-portation. Also, when expanding the SC, the firms have to face transactioncosts andadditional hiddencosts that are hard tobe anticipatedinadvance.

Theoperational motivationsof relocatingthe productionclose orinthehomecountryarequalityissues,longerleadtimes,larger inventoriesanddisruptionrisks.Bruccoleri,Perrone,Mazzola,and Handﬁeld(2019)useAgencytheorytodevelophypothesisrelating productrecalltothesourcingstrategy.Theyconductan empirical studyinthepharmaceuticalindustrytohighlightthelinkbetween qualitydeterioration,offshoringandoutsourcing.

Someoutcomesofglobalisationmayhaveanimportantimpact onthe strategyofthe company. Among thosewe referto oppor-tunism,increasing theft ofintellectual properties andthe loss of overallperformanceandskills.Hansen,Mena,andAktas(2018) re-fertothoseandotherriskssuchastheburdenandqualityof bu-reaucracy, thegeopolitical environment andcurrency ﬂuctuations aspoliticalrisks.Theirempiricalstudyshowsthat38%ofthe vari-abilityinoffshore outsourcing ﬂows comefromsuch risks.There isalsotheneedtobringproductionclosertoR&Dformore effec-tiveness.

For resources-related motivations, some authors mention in-creasedautomationinthehomecountry(Talamo&Sabatino,2018, Guillaume,2018andArlbjorn&Mikkelsen,2014),poorIT, commu-nicationandtransportinfrastructureinthehostcountry.

Backshoring and reshoring are gaining rising attention in the literature. Ellram, Tate, and Petersen (2013) and Cohen et al. (2018) focus on sourcing from owned manufacturing facilities. They present drivers for different manufacturing location deci-sions. Stentoft, Mikkelsen, Jensen, and Rajkumar (2018) present the performance outcomes of offshoring, backshoring and local manufacturing. Interested in the return of offshored activities,

Fratocchi,Mauro,Barbieri,Nassimbeni,andZanoni(2014),Arlbjorn andMikkelsen(2014)aswellasL.Tate,M.Ellram,Schoenherr,and J.Petersen(2014)lookfordriversofthisdecisionforEuropeanand UScompanies.

Arlbjorn and Mikkelsen (2014) mention the barriers of mov-ing production back to Denmark which are the lack of informa-tionandcommunicationontheprocess,thelackofinternal com-petencies among the production staff, the lack of proper foun-dation for the decisionto insourcebut also the lack of resource allocation.

Fishman(2012)analysestheGeneralElectricexampletobetter understandthe offshoring andreshoring trends.The manufactur-ing jobs offered by the company attained 23,000 in the USA in 1973, then fell down to 1863 jobs in 2011. After explaining the movement to China and the different steps historically, the au-thorspresentsthe reshoringcase. Whentaking thisdecision, the

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team re-designed the product and eliminated 20% of the parts andcut the costsof materials by25%. Theworkinghours for as-semblywent down from 10 to 2. By reshoring to USA, the ma-terial cost and labor required went down while the quality and energyeﬃciencywentup. Theauthor thengivesother industrial examples of reshoring, stating that offshoring was motivated by lowlaborwageswhichisnotthecaseanymore.Besides,itcomes withmanyhiddencosts andqualityandleadtime problemsthat were not anticipated and make it less interesting as a sourcing strategy.

The ConsultingGroup IAC Partnersalsomentionthere-design to cost, as a way to reduce costs by reshoring, through chang-ingtheproductionprocess.Itworkedwell forATOL, aglasses in-dustry.Veloscoot,afrenchcompanymakingelectricalbikes,relied onlocalsourcingwhichresultedinbetterqualityandhigher cus-tomervalueLeredesigntocostauserivcedelareindustrialisation française.

Fratocchietal.(2016)characterizethereshoringmotivations ac-cordingtothegoal(valueandcosteﬃciency)andthelevelof anal-ysis(internalorexternal).Thenthey buildadatabaseofreshoring decisions/projectsbased on 377 cases.Afteridentifying the cases byhome country,hostcountryandindustrialsector, theyclassify themotivationsfromtheempiricalstudyaccordingtotheir frame-work.

Kinkel andMaloca(2009)relate theoffshoringdecisiontothe ﬁrm’ssizeandindustrialsector. Theirresultsindicatethatthe ac-tivities with standardised production processes and lowly quali-ﬁedlabor are moreoffshored. Theymentioncapacitybottlenecks amongthedriversofoffshoring.Theyrelatethebackshoring deci-siontoformeroffshoringone,asasolutiontothedifferent prob-lemsthatpreviouslyoccurred.

Mauro et al. (2018) conduct a multiple case study including 4 italian manufacturies from the Textile, Clothing, Leather and Footwear (TCLF) industry. What is interesting to note is that all ofthem statedoffshoring asa consequence of their competitors strategieswhichallowedthemtokeepcompetitiveprices.Another importantpoint is that the offshoring decisiondoes not include highendproducts, whereas low-endproducts arerarelyincluded inthebackshoringdecision.Themainreshoringreasonhighlighted byallofthemisthe“Madein” effect.

Anon(2012)focusesontherisinglaborwagesincoastalChina. AlthoughinlandChina offerslowerlaborscosts,itisoffsetby the poortransportation infrastructure which will resultin extending leadtime. Evenifother Asian countriessuch asVietnam andSri Lanka offer lower labor wages, they are reportedto be less eﬃ-cientandhavealower productivitythan China.Theauthorstates thatforChinatobecomemoreinternationallycompetitive,thereis aneedtomaketheDesignofproductsandnotonlythe manufac-turing.Whichbringsustoanimportantaspectoftheoutsourcing decision:whichactivitytooutsource.

Kinkel (2014) uses data from European Manufacturing Survey toanalyseGermancompanies’decisionstoreshore.Theauthor es-timatesthat 20%ofthe backshoringdecisioninGermany isa re-sponseto thechanging environment andthe lossof thelocation advantageswhile the remaining 80% are a correction of the off-shoringdecisionthatﬁrsttookplace.

TalamoandSabatino(2018)considerreshoringandits relation toresilience, bystudyingtheItaliancase.Theymentionindustrial casesofreshoringlikeBosch,Sagem,NafNaf,Caroll,Nokia,Nathan andEssilor.Then,afterdefiningresilienceastheabilitytorecover aftera crisis, they list reshoringcases in Italy by region andby industrial sector. They use a resistance index, based on changes inemployment, tocomparethedifferentregionsofItalyin3 cri-sisperiods(oil crisisin1970–1973,devaluationofLira and politi-calcrisisin1992–1995andfinancialandeconomiccrisisin2008– 2010).Whilestatingthe“Made-in” asthefirstregionforreshoring,

theauthors mentionother driverssuch asrisk,safetyprocedures andthepressureofthecountryoforigin.

TheeffectofTradeFacilitation(TF)oninternationaltradeis dis-cussed inthework ofMann(2012).The authordistinguishes be-tweenthemacroeconomicaspectrelatedtopolicymakersandthe microeconomic one related to businesses.She mentions non tar-iffs direct costs (portfacilities andlogistics costs) as well as in-formation and communication technology networks and globally linked ﬁnancial institutions, and alsothe arm’s length regulation and standards like the ones by the Internation Organization of Standarization (ISO).She refers to worksthat explicit the quality ofroadnetworks,logisticsandshippingtimeandports infrastruc-ture.

Thereisnodoubtthatthesourcingdecisionisstronglyrelated totheenvironmentalperformanceofasupplychain.Thedecision to bring the production back in-house can be motivated by re-ducing emissions. Yet, a review publishedin November 2019 by

FratocchiandStefano(2019)showsthatonly7outof33reshoring related papers andbook chapters refer to the environmental as-pectofthedecision. Intheempiricalstudypresentedby Stentoft, Mikkelsen, and Johnsen (2015), based on a survey conducted in Denmarkregardingthe localsourcing decision, noneoftheﬁrms mentionstheenvironmentalargument.Oneoftherarecorrelations wefound betweenreshoringandenvironmentalsustainabilityare atwo-page perspectiveby OrzesandSarkis(2019)that was pub-lishedin2019andaLifeCycleAssesmentofanathleisremadeby

Clarke-SatherandCobb(2019).Aimingtoquantifysocial, environ-mentalandeconomic beneﬁtsoflocalsourcing,the authoursuse asustainability indicatorincludingdirectandindirectgreenhouse gasemissions.

The environmental impact, though rarely mentioned in reshoring cases, is gaining a rising attention in the recent lit-erature. Sirilertsuwan, Ekwall, and Hjelmgren (2018) review the literature on proximity manufacturing in relation to enhancing sustainability in the clothing sector. They confirm that the cor-relation between local production and the environmental aspect is under-studied.They presenttheir findings indifferent regions, whichhighlightstheneedtoconsiderdifferentmarketsandshows that the environmental dimension in frequently analysed in the European market, rarelyin NorthAmerica andonly one paperis found for Asia. Another relevant point of these authors that we should keepinmind istheabsence ofusingdatafroma specific locationinthemodelingtechniques.

Dolgui and Proth (2013) mention the social consequences of outsourcingandoffshoring,e.g.,unemploymentanddeclineof liv-ing standards by keeping labourwages low in developing coun-tries.

BeharandVenables(2010)analysetheeffectoftransportcosts on tradeﬂows. Theauthors extendthe gravity modelof interna-tionaltradebywritingthevolumeofexportsasafunctionof in-come,policy,culturalaﬃnityandtransportcosts.Withafocuson thetransportcosts,theyexaminethetrade-off betweenspeedand reliabilityinreturnfordifferentcostsfordifferentmodesof trans-port. Theauthors presenttheaveragecosts andhandlingtime of a container in different regions of the world. They mention the growingtrendofairshippingandthecontributionofdelayin re-ducing trade. Theycollect evidence fromcross-section and time-seriesdatatolookattherelationshipbetweentransportcostand distance,infrastructure,fuelcosts,technology,tradefacilitationand technology.

The worksabove take into consideration the industrialsector, theﬁrm’ssize,the home/hostcountriesandwhichactivityis ex-ternalised/internalised. These aspects need to be incorporated in the analytical models as the outcome differs a lot according to them. In thenext section, we discussthe analytical models pro-posedintheliterature.

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4. Analyticalmodelsforsourcingdecisionsinglobalisationvs. slowbalisationcontext

The insourcingandoutsourcing decisionlieswithin an impor-tant streamofProduction andOperationManagement research.A specialissueoftheInternationalJournalofProductionResearchon outsourcing andoffshoring waspublishedin 2018 toaddress the need formore inclusivedecision-aidtools.Ishizaka,Bhattacharya, Gunasekaran, Dekkers,andPereira(2019) reviewtheeightpapers ofthisspecialissue,by clearlystatingthecontributionandfuture researchdirectionofeach.Inthissection,weanalysetheanalytical models developed inthe literature in orderto help supplychain managerstomakethemostproﬁtabledecisions.

4.1. Insourcingandoutsourcingdecision

In thissection we presentanalytical models developed inthe literatureinordertohelpthesupplychainmanagerstodecideto insourceoroutsource(makeorbuy).Theanalyticalparametersfor eachreferencearedetailedinTable3

Kim,Park, Jung,andPark(2017),LiuandNagurney (2011)and

Wang, Niu, and Guo (2013) consider cooperation and competi-tion indifferentsupplychainstructures. Theyfocuson thearm’s length regulation, quick-response production and push and pull contracting, respectively. In addition to uncertainty, these papers havecommondecisionvariables,whicharequantityandprice.

Kimetal.(2017)considercostuncertaintyinacontextof out-sourcing and offshoring. The authors consider offshoring where theretailerdecidesorderquantityindependentlyfromthe produc-tiondivision,andoutsourcingwherethereiscompetitionbetween the ﬁrm’sproductiondivision withoutsidesuppliers. They inves-tigate thechoice ofamultinational ﬁrm’sSC structurefor opera-tions(centralizedintegration,decentralizedoffshoringor outsourc-ing)changes ifbuyersandsellers ofaproductact independently, bothintheirownself-interest.

Intheirpaper,LiuandNagurney (2011)elaborate supplychain networks by allowing multiple suppliers, multiple manufacturers andmultipledemandmarketstointeractunderbothdemandand cost uncertainty. In their configuration,each manufacturer maxi-mizeshisownexpectedprofitthroughatwo-stagestochastic pro-gramming problem. The authors assume that the manufacturers are competingwitheachother andcooperatingwiththeoffshore suppliersinthefirststage.Theyusevariationalinequalitytostudy the effects of demand and cost uncertainty on outsourcing, in-house productionand salesunder competitionas well ason the supply chain’sfirmsprofits andrisk. Theyalsodetermine the ef-fectsofquick-responsein-houseproductiononthefirms’decisions, profits and risk. Kaur, Singh, andMajumdar (2018) also consider a multi-tiers SC. Theydevelop a Mixed Integer NonLinear Prob-lem (MINLP)integratedwithfuzzymulti-criteria decisionmaking (MCDM) to model the joint offshoring and outsourcing decision. Theyrankthesuppliersincaseofoutsourcing,determinethe op-timalquantity,fromwhichsuppliertobuyandwheretokeep in-ventoryincaseofoffshoring.

Wang etal. (2013) use game theory to compare the effectof different outsourcing structures and contractingarrangements on the inventory/capacity risks of a supply chain.They show which arrangement isthebestforthe OriginalEquipment Manufacturer (OEM)andunderwhichconditions.

Similar to Liu andNagurney (2011),Teng and Hsu (2017) as-sume that bothstrategies are appliedanddetermine the optimal outsourcedandproducedquantities.Buttheyconsidera determin-isticandnotastochasticdemandfunction.

Fora while now, increasedattentionhas beenbrought tothe environmentalaspectinthesourcingdecision.Oneormoreofthe following canbe included: environmentalregulations,investment

toreduceemissionsandCustomerEnvironmentalAwareness(CEA).

Zhang,Padmanabhan,andHuang(2018)andSchenker,Koesler,and Löschel(2014)basetheirworkontheassumptionofataxon im-portsthat woulddiscourageﬁrmsfromchoosingfar-away suppli-ers.

Zhang et al. (2018) develop a model to study the impact of environmental regulations on production level and supply chain structure.Itisoneofthefirstpapersontheincreasinglyoffshored pollutionfromdevelopedcountriestoemergingeconomies.To ad-dress thisproblem of “pollution heaven”, where firmschoose to offshoretopollution-friendlycountriesinordertoavoid environ-mentalfinestheywillhavetopayfortheiremissions,theauthors assumeanoffshoringtax,whichwouldincreasecostsofimported products.Theyconsiderdifferentscenarios:a benchmark produc-tiondecisionmodelwhereonlyin-housemanufacturingis consid-ered,apollution penalizationmodelwithagreentax andan off-shoringmodel.

Schenkeretal.(2014)alsoassumeabordercarbontaxintheir work.Itwouldcompensatetheoffshoringdecisiontounregulated countries.Theauthorsfocusonhowenvironmentalregulations af-fecttheinternationalsupplychains.Theyclassifytheir results ac-cordingtodifferentsectors,whichlacksinthepresentliterature.

Another environmental regulation is mentioned by citetchoi. Theauthorassumesthatunderthecarbonfootprintscheme, differ-enttaxratesareapplieddependingonthelocationofthesupplier. Hestudiesasupplychainconsistingofonemanufacturerandtwo suppliers.Theyare locatedintwodifferentcountrieswhich influ-encesthecosts andthelead time. Theauthorlooks forthe opti-malsourcingdecision. Hefirststartswithasingle-ordering sce-nariowherenoquantityadjustmentcanbemadeanddetermines theoptimalsourcingstrategywithrespecttoacertainservicelevel rate.Forthedual-orderingscenario,theretailerhasdifferent ob-jectivefunctionsineachstage:achievetheinventoryservicetarget andmaximizetheexpectedprofit.

Someauthorsconsidergametheorymodelswherethe govern-mentisone oftheplayers.Theypresentinteresting findingsfrom the government’s perspective to either maximize revenues from thepenalty,minimizeemissionsormaximizesocialwelfare.Meng, Yao,Nie,andZhao(2017)studythemake-or-buydecision un-derenvironmentallegislation,andtheimpactofagreentaxonthe firm’ssourcingstrategy andoverall carbonemissions. Theybegin withan exogenous taxscenario where they solveboth the make andthebuyregime models.Theydetermine theoptimal produc-tionquantityandthewholesalepriceofthesupplierinthecaseof outsourcing.Theyinvestigatetheimpact ofthe carbontax onthe overallemissions.Thesecondscenarioassumesanendogenoustax rate. Theauthors findout theoptimalstrategies fordifferent ob-jectivesofthegovernment(maximizethecarbontaxincome, min-imizethecarbon emissionsorboth)andunderwhich conditions thegovernmentshouldorshouldnotregulatethetax.Morepapers onthegovernment’sbehaviourwillbecitedinthenextsection.

With the common assumption of an exogenous price, Zhang, Wang, and You (2015) study how CEA impacts order quantity andchannel coordinationfora one-manufacturerandone-retailer SC. Customers’ willingness to buy greener products is a criteria that is more and more adopted in the literature. Related to the insourcing-outsourcing context, They consider 3 scenarios: cen-tralized,decentralizedanddecentralizedwithreturncontract.The manufacturerproducestraditionalandgreenproducts.Theauthors thenextendthesemodelsbyincludingacapacityconstraint.They assume thatdemand isfunction ofboth theproducts’ pricesand environmentalquality.

FacanhaandHorvath(2005)useLifeCycleAssessmentto com-parebetweenthe in-houseandoutsourcedlogistics.Theyadd an environmentalperspectiveby usingindicatorssuchasenergyuse and globalwarming potentials. They consider the life cycleof a

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typical automobile in the USA and generalize their ﬁndings to otherindustry groups. Theyconcludethat thereisa reduction in energyutilization, globalwarming potential andfatalities dueto logisticsoutsourcing.

The papers above present different analytical aspects that shouldbemoreregardedintheliterature.Whilesomedifferentiate betweencooperationandcompetitionscenarios,othersincorporate capacityconstraintsandinventoryrisks.Someinteresting analyti-calmodelsincorporateuncertaintyindemandand/orcosts, quan-tityadjustment,andlessfrequentlyleadtimeandservicelevel.We alsonote the assumption ofa bordercarbon tax that wouldadd coststotheoutsourcing/offshoringdecisionandaddressthe trans-ferofpollution.Moreauthorsshould incorporatethisassumption in order to analyse its outcomes and encourage governments to adoptitincaseitturnsouttobeeffective.

4.2.Outsourcingcontracts

Thepapersreviewedintheprevioussubsectionpresent analyt-ical models for the make or buy decision. However, if the com-panychoosestooutsourceitsproductiontoanexternalparty,the questionishowtomakethebestout ofthissourcingstrategy. In this subsection, we present models of the contract arrangement betweenSC members. Different contract parameters lead to dif-ferentoutcomes, andthat is whywe need to analytically model thedifferentconﬁgurationstoacknowledgethebestdecision.The mostwidely usedcontractsarecostsharing andrevenue sharing, wheretheSCmemberssharearatioofthecostsandrevenues, re-spectively.The two-parttariff contract isan arrangementwherea lumpsumfee ispaidinexchangeforalowerwholesalepriceset bythemanufacturertotheretailer.

We review Game Theorymodels that contribute to the coor-dinationoftheSCbyconsidering differentcontractarrangements. Wepresenta genericmodelthathasbeenadapted ineach paper accordingto thecontext.Unlesswe sayotherwise,the references belowconsideratwo-tiersSC consistingofonemanufacturerand oneretailer. Theyseektomaximize their profits.In acentralized setting,the players act simultaneously in order to maximize the wholeSCprofitasoneentity,whichcanbeunderstoodas insourc-ing.Inthedecentralizedsetting,eachplayertakeshisdecisions in-dividuallytomaximizehisownprofit.

WedenoteXthevectorofdecisionvariables,andusethe sub-scriptsrandm fortheretailerandthemanufacturer,respectively.

fi (gi),i=r,mrefer tothefunctionsofrevenues(costs).Thus,we canwrite theproﬁtfunctionsoftheretailer

randthe manufac-turer

masfollows:

1−

β

)

de-mandsensitivitytoprice.

Ji etal.(2020) consider thecap andtradeenvironmental reg-ulation, Singh et al. (2016) a carbon tax as a percentage of the netrevenueandYangetal.(2014)comparetheoutcomesof emis-siontrading,carbon taxanda combinationofboth underan all-unitwholesalequantitydiscountcontract.Thisresultsindifferent costfunctions.Ji etal.(2020)considerawholesalepriceand rev-enue sharing contract. The contributionof this work is the con-siderationoftheinverserelationshipbetweenthecapallocatedby the governmentandthe carbontradingprice. They alsoconsider thegovernmentasone ofthe playersanddeterminetheoptimal capthatmaximizesthesocialwelfare.Singhetal.(2016)consider long-term and short-term contracts, andinclude import and ex-portdutiesaswellasthetotalcostoftransshipmentintheircost functions,referredtoasTRANSinTable4.

The product greenness and customers’ environmental aware-nessareincorporatedintheanalyticalmodelsasvariables impact-ing the demand function. In the papers presented next, the de-mandislinearlydecreasinginbothretailpricepandsustainability levels,e.g,D=D0−

η

p+

μ

s.Itisassumedthattheﬁrmsmakean

investment Ito reduce the emission fromproduction. Unless we sayotherwise,theinvestmentisaquadraticcostofthe sustainabil-ityleveltoachieve,I=ks2_,_where_k_is_the_investment_coeﬃcient.

Dong, Shen, Chow, Yang, and Ng (2014) consider a stochastic demandfunctionthat onlydependsons.GhoshandShah(2015),

Ghosh and Shah (2012), Song and Gao (2018), Yang and Chen (2018) andLi,Zhang, Zhao,andLiu(2019) considerdemand asa function ofboth pands andcots function assumofproduction costs andgreeninvestment.Werefer tothecostoftrading emis-sion and holding inventory as cTRAD and cINV, respectively. cBB is thecostofreturning productsincaseofbuybackcontract.Ghosh andShah (2015)investigate thecost sharingcontract, Ghoshand Shah(2012) thetwo-parttariff contract,SongandGao (2018)the revenue sharingcontract andYang andChen (2018)compare be-tweencostsharing,revenuesharingandacombinationofboth un-deracarbontax.Exceptthelattermodelwhoconsidertheretailer as the Stackelberg leader, the others consider different scenarios whereeachSC memberistheleaderthena Nashbargaining sce-nario.Lietal.(2019)comparetheresultswithaﬁxedsharingrate, symmetricandasymmetricbargainingpower.

Xu,He,Xu,andZhan(2017)andXua,Chen,andBai(2016)also consider a demand function that is linearly decreasing in retail price and increasing in the greening level. They add a carbon trading priceto their cost functions. While the former studythe outcome of wholesale price and cost sharing contracts, the lat-ter consider revenue sharing and two-part tariff. Li, Xiao, and Qiu (2018) consider the same demand formulation.Theyassume that theretailer isfairness-concerned, inwhich they maximize a utility function instead of the proﬁt function forthe retailer. Zu, Chen, and Fan (2018) analyse both the microscopic and macro-scopiceffectsintheemissionreductionproblem.Theypresentthe government-based situation where a carbon subsidy (penalty or reward) is applied to the manufacturer (SUBS in Table 4). They also state that it is not a one-period decision and consider a differential game to assess the impact of time. Taleizadeh and Rabie (2018) consider a SC consisting of one retailer and two manufacturers. We refer to them with the subscript mi, i=1,2. Theyformulatethedemandfunctionasfollows:D=D0pηsμ.Yang, Zhang,andJi(2017b)consideratwo-manufacturer,two-retailerSC. Their demand function depends on the greening level of manu-factureri andthatofhisrival3− i,i=1,2.Yang,Luo,andWang (2017a) consider a non-linear demand function that depends on priceandgreeninglevel.Intheirpaper,D₌k

(

s

)

D0− p,wherekis

acoeﬃcient thatdependsontheabatementlevel.DeyaandSaha (2018) consider a two-period setting.They develop three models fordifferentprocurementscenarios.Theﬁrstoneassumes procure-mentinbothperiodswithstrategicinventory(SI)maintaining.The

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Table 1

Drivers of globalisation.

References Paper type Financial Operational Strategical

Lau and Zhang (2006) Case study

Gilley and Rasheed (2000) Survey

Beaumont and Sohal (2004) Survey

Gonzalez, Gasco, and Llopis (2005) Survey

Harland, Knight, Lamming, and Walker (2005) Delphi study

Talamo and Sabatino (2018) Data base analysis

Kinkel and Maloca (2009) Large-scale survey

Kremic, Tukel, and Rom (2006) Review

Mykhaylenko, Ágnes Motika, Waehrens, and Slepniov (2015) Survey

Persaud and Floyd (2013) Survey

Ancarani, Mauro, Fratocchi, Orzes, and Sartor (2015) Survival Analysis

Canham and Hamilton (2013) Survey

Table 2

Drivers of slowbalisation.

References Paper type Financial Operational The “Made in” effect Policies and regulation

L.Tate et al. (2014) Survey

Bergman and Ramachandran (2010) Company overview

Sirkin, Zinser, and Hohner (2011) Consulting Group report

Fishman (2012) Press article

Talamo and Sabatino (2018) Data base analysis

Kinkel (2014) Comment based on empirical evidence

Behar and Venables (2010) Discussion Paper

Arlbjorn and Mikkelsen (2014) Note based on a large-scale survey

Mauro et al. (2018) Case study

Guillaume (2018) Article

Kinkel and Maloca (2009) Large-scale survey

Sirilertsuwan et al. (2018) Literature review

Heikkilä, Martinsuo, and Nenonen (2018) Survey

Fratocchi and Stefano (2019) Literature review and empirical evidence

secondoneassumestwo-periodprocurementwithoutSI.Thethird oneassumesaone-timeprocurementinthebeginningoftheﬁrst period.In Table4,we referto inventorylevelasINV andwe use thesubscripti,i=1,2toindicatetheperiod.

We refer now to the models in which an additional deci-sionvariableimpacting thedemandhasbeenintroduced.Inthese cases,we still haveademand functionthat islinearlydecreasing inpriceandincreasinginsandanothervariable.SwamiandShah (2013)assumethateachofthemanufacturerandtheretailermake a greening effort. If we refer to them respectively as sm and sr, thedemandfunctioncanbewrittenasD=D0−

η

p+

μ

msm+

μ

rsr.

Dai, Zhang, andTang (2017) andYuyin and Jinxi(2018) consider bothreducing carbonemissionandenergy-saving(ener)as green-ing efforts. While the former consider an increasing demand in emissionreductioneffort,thelatterconsideradecreasingdemand in carbon emission level. Dai et al. (2017) introduce the govern-ment subsidy rate, a government support for green products re-ferred to as subs inTable 4. The authorsalso use empiricaldata from theChinese new energyvehicle industry to verifytheir re-sults.

Onanotherhand,Raj,Biswas,andK.Srivastava (2018)consider greening level andCorporate Social Responsibility(CSR). The pa-rameters ofthecontractsinthispaperaredecisionvariables and notexogenous.Zhou,Bao,Chen,andXu(2016)consideragreening effort madeby the manufacturer andan advertising effort(ADV) made by theretailer. Like Li etal.(2018),they consider a utility function for the retailer who is fairness concerned. Kuiti, Ghosh, Gouda,Swami,andShankar(2019)formulatethegreeningeffortas pack-sizereductionandshelf-spaceallocation,referred toasPACK

andSHELF in theanalytical models ofTable4. Bai,Chen,andXu (2017) consider ademand function varying insustainable invest-ment,promotionaleffort(PROM),priceandtime.Theyincorporate inventorycosts anditemdeteriorationto theircost functions.We refertothemwithsubscriptsINVandDETER.

Basiri and Heydari (2017) and Ma, Zhang, Hong, and Xuc (2018) consider two substitutable products. Thus, their demand functions depend on both products’ prices but also on the dif-ferencebetween prices,green qualities and sales efforts(SAL) of thetwo productsinBasiriandHeydari’sworkBasiriandHeydari (2017).Theyassume thatonemanufactureroffers thetwo substi-tutableproductswhile Maetal.(2018)considertwo manufactur-erseachofferingone typeofproduct.Theirdemandfunctionalso dependsoncustomers’loyaltytoeach product,referredtoasLOY

inTable4.

Table4presentsanoverviewofthesecontributions.Inthis ta-ble,Disthedemandfunction,ptheretailpriceand

ω

the whole-sale price. We refer to costs as c and use subscripts to refer to thosethatarenotproductioncosts.qreferstoproductionquantity. Underenvironmentalregulations,werefertothetotalemissionof aﬁrmasEandthecarboncapasCAP.

5. Discussion

In this survey, we analyse both the drivers and obstacles for slowbalisationandglobalisation andthe analyticalmodels devel-opedintheliteratureforsourcingdecisions.Wecanobserveagap betweenthefactorsthatinﬂuencethedecisionsinpracticeandthe modelsdevelopedtohelpthedecisionmakers.Currently,the ana-lyticalmodelsexisting inthe literature remainrather simpleand the state-of-the-art operational research methodology should be betteremployedinordertomodelmorerealisticfactorsdiscussed inSection 3. Forlarge supplychains, itis alsoimportant totake intoaccountsuchsettingsasuncertainty,complexityofthesupply chain structures and multi-periodoptimization where the evolu-tionof legislation canbe taken intoaccount. Uncertaintyis rele-vantforthedemandfunction,costfunctions(carbontax,exchange rate), delivery andlead time which impacts inventoriesand de-mandfulﬁllment. Suchfactorsascapacityconstraints,for

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produc-Table 3

Analytical models for the make or buy decision.

Reference SC structure Demand structure Objective function Decision variables Solution method Driver modeled

analytically

Teng and Hsu (2017)

one distributor, one warehouse

deterministic proﬁt

maximization

order quantity and outsourced fraction

Algorithmic solution

Made in effect and higher cost for insourced products

Kim et al. (2017) one manufacturer - one retailer under arm’s length regulation deterministic proﬁt maximization quantity, wholesale price

First and second order derivative

Lower tax and production cost in foreign countries, cost uncertainty

Liu and Nagurney (2011) multiple manufacturers, suppliers and demand markets stochastic proﬁt maximization quantity outsourced and locally produced,price Variational inequality Lead time (quick-response production), production capacity, demand and cost uncertainty, higher in-house production cost

Kaur et al. (2018) multiple manufacturers, suppliers and demand markets stochastic proﬁt maximization and cost minimization supplier’s rank, quantity outsourced, quantity produced, from which supplier

MINLP with fuzzy MCDM

demand uncertainty, capacity constraint, inventory and ordering costs

Wang et al. (2013) three-tiers stochastic proﬁt

maximization

order quantity, wholesale price

Game theory Lead time (early and late orders), lower cost and higher capacity in-house and power structure

Zhang et al. (2018) one manufacturer - one supplier

deterministic proﬁt

maximization

quantity First and second

order derivatives

Higher in-house cost

Choi (2013) one manufacturer - two suppliers

stochastic Inventory service

target and proﬁt maximization

quantity offshored and locally produced

First and second order derivative

Lead time (quick-response production), higher local costs, Bayesian forecast updating model

Meng et al. (2017) one manufacturer - one supplier stochastic proﬁt maximization for the manufacturer and the government and emission minimization quantity, wholesale price and tax

First and second order derivatives

Higher costs and lower emissions in-house

Zhang et al. (2015) one

manufacturer-one retailer with 2 substitutable products deterministic proﬁt maximization quantity, wholesale price and return credits

First and second order derivatives

Capacity constraint

Table 4

Analytical parameters related to the outsourcing contracts 0.8.

Reference Game leader X

Demand function fi ( X ) gi ( X ) Contract Singh et al. (2016) manufacturer then retailer p,ω D = D 0 −ηp fr(p) = pD fm(ω) = (1 − c tax)(1 − cexport)ωD gr(p) = (1 + c import)ωD gm(p) = (1 − c tax)(cD + T RANS)

long and short terms contracts

Ji et al. (2020) government, then manufacturer p,ω and Cap D = D 0 −ηp fr(p) = pD fm(ω) = ωD gr(ω) = ωD g m(s) = cD + c T RAD(E − CAP) α= 1 , β= 1 and γ= 0 0 ≤α≤ 1, β= 1 and γ= 0 Dong et al. (2014) manufacturer q and s D = D 0 + μs + E f r(q) = p min (q, D ) + cBB(q − D )+ E f m(s) = ωq E g r(q) = ωq + c INV(q − D )+ E g m(s) = cq + I + c T RAD(E − CAP) 0 ≤α≤ 1, β= 1 and γ= 0 α= 1 , β= 1 and γ= 0 α= 1 , β= 1 and γ= 1 and c BB Ghosh and Shah (2015) manufacturer, then retailer, then bargaining model p,ω and s D = D0 −ηp + μs fr(p) = pD fm(ω) = ωD gr(ω) = ωD g m(s) = cD + I α= 1 , 0 ≤β≤ 1 and γ= 0 Ghosh and Shah (2012) manufacturer then retailer then Nash bargaining p,ω and s D = D0 −ηp + μs fr(p) = pD fm(ω) = ωD gr(ω) = ωD g m(s) = cD + I α= 1 , β= 1 and γ= 1

Song and Gao (2018)

retailer then Nash bargaining p,ω and s D = D0 −ηp + μs fr(p) = pD fm(ω) = ωD gr(ω) = ωD g m(s) = cD + I 0 ≤α≤ 1, β= 1 and γ= 0

Yang and Chen

(2018) retailer p,ω and s D = D0 −ηp + μs fr(p) = pD fm(ω) = ωD gr(ω) = ωD g m(s) = cD + I 0 ≤α≤ 1, β= 1 and γ= 0 α= 1 , 0 ≤β≤ 1 and γ= 0 0 ≤α≤ 1, 0 ≤β≤ 1 and γ= 0

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Table 4 ( continued )

Reference Game leader X Demand

function

fi ( X ) gi ( X ) Contract

Li et al. (2019) manufacturer α,β, p, ω and s D = D0 −ηp + μs fr(p) = pD fm(ω) = ωD gr(ω) = ωD g m(s) = cD + I 0 ≤α≤ 1, β= 1 and γ= 0 α= 1 , 0 ≤β≤ 1 and γ= 0 0 ≤α≤ 1, 0 ≤β≤ 1 and γ= 0

Xu et al. (2017) manufacturer p,ω and s D =

D0 −ηp + μs fr(p) = pD fm(ω) = ωD gr(ω) = ωD g m(s) = cD + I + c T RAD(E − CAP) α= 1 , β= 1 and γ= 0 α= 1 , 0 ≤β≤ 1 and γ= 0 Xua et al. (2016) supplier p,ω and s D = D0 −ηp + μs fr(p) = pD fm(ω) = ωD gr(ω, s ) = ωD + I + c T RAD(E − CAP) gm(s) = cD 0 ≤α≤ 1, β= 1 and γ= 0 α= 1 , β= 1 and γ= 1

Li et al. (2018) manufacturer p,ω and s D =

D0 −ηp + μs fr(p) = pD fm(ω) = ωD gr(ω) = ωD gm(s) = (c + T AXs ) D + I 0 ≤α≤ 1, β= 1 and γ= 0

Zu et al. (2018) manufacturer p, s r , s m and

SUBS D = D 0 −ηp + μ(sr + s m) _∞ 0 f r(p, SUBS) = pD + SUBS d X fm(sm) = ∞ 0 ωD d X gr(sr) = ∞ 0 ωD + I r s r d X gm(sm) = _∞ 0 I m d X α= 1 , 0 ≤β≤ 1 and γ= 0 Taleizadeh and

Rabie (2018) manufacturer for quantity discount, retailer for cost sharing p, q D = D 0 p −ηsμ fr(p) = pq fmi(qi) = ωi q i gr(q) = ωq + c INV q + c ORDDq gmi(qi) = ci q i + c INV i q i + c ORDDqii + I i α= 1 , 0 ≤β≤ 1 and γ= 0 and quantity discount Yang et al. (2017b) manufacturer, Nash game in horizontal direction pi , ωi and s i Di = D oi −ηpi + si −μs3−i fri(pi) = p i D i fmi(ωi) = ωi D i gri(pi) = ωi D i g mi(si) = I + c T RAD (E − CAP) 0 ≤α≤ 1, β= 1 and γ= 0 Yang et al.

(2017a) the manufacturer then retailer ω

, q and s D = k (s) D 0 − p fr(q) = pq

fm(ω) = ωq

gr(q) = ωq g m(s) = cq α= 1 , β= 1 and γ= 0

0 ≤α≤ 1, β= 1 and

γ= 0

Deya and Saha (2018)

manufacturer p,ω, s and INV Di =

D0 −ηpi + μs fri(pi , INV ) = 2 i=1 .p i D i fmi(ωi , s ) = 2 i=1 .ωi D i gri(ωi) = ωi(Di ± INV ) + c INV gmi(s) = (c + s )(D ± I NV ) + I α= 1 , 0 ≤β≤ 1 and γ= 0 Swami and Shah (2013) manufacturer p,ω, s r , s m and F D = D 0 −ηp + μm s m + μr s r fr(p) = pD fm(ω) = ωD gr(ω, s r) = (ω + r) D + I r gm(sm) = cD + I m α= 1 , β= 1 and γ= 1

Dai et al. (2017) manufacturer, joint decision on the investment p,ω, s and ener D = D 0 −ηp + μ(s + ener) ffrm((pω) = pD ) = ωD gr(ω, s ) = (ω + SUBS) D + I r gm(ener) = I m α= 1 , 0 ≤β≤ 1 and γ= 0

Yuyin and Jinxi (2018)

manufacturer p,ω, s and ener D = D 0 −ηp + μs s + μener ener fr(p) = pD fm(ω) = ωD + SUBS gr(ω, s, ener) = ωD gm(s, ener) = (c + T AX) q + I s + I ener α= 1 , 0 ≤β≤ 1 and γ= 0

Raj et al. (2018) supplier p,ω, s, CSR, α,

βand γ Dμ = D s s + 0μ −CSRη CSR p + fr(p) = pD fm(ω) = ωD gr(ω, s, CSR ) = (ω − c) D + I CSR gm(s, CSR ) = cq + I s α= 1 , β= 1 and γ= 0 0 ≤α≤ 1, β= 1 and γ= 0 α= 1 , 0 ≤β≤ 1 and γ= 0 α= 1 , β= 1 and γ= 1 0 ≤α≤ 1, 0 ≤β≤ 1 and γ= 0 Zhou et al. (2016)

manufacturer s, ADV and β D = D 0 + μs s +

μADV ADV fr(ADV) = pD fm(s, β) = ωD gr(ADV, β) = I ADV gm(s, β) = I s α= 1 , 0 ≤β≤ 1 and γ= 0 Kuiti et al. (2019) manufacturer p,ω, PACK, s and c transport D = D 0 + μs s + μPACK PACK fr(p, s ) = pD fm(ω, PACK, c transport) = ωD gr(s) = ωD + K + I s gm(PACK, c transport) = cD + I SHELF + I PACK + K α= 1 , 0 ≤β≤ 1 and γ= 0 α= 1 , β= 1 and γ= 1

Bai et al. (2017) manufacturer s,ω, p and PROM D = (D0 −ηp + μs s + μPROM PROM)v(t) fr(p, PROM) = p(D − q DET ER) fm(ω, s ) = ωD gr(PROM) = ωD + c DET ER + c INV + I PROM gm(s, PROM) = cD + I s + c T RAD(E − CAP) 0 ≤α≤ 1, 0 ≤β≤ 1 and γ= 0 α= 1 , β= 1 and γ= 1 Basiri and Heydari (2017)

retailer p, s and SAL D = D 0 −ηpi +

μsi + λSALi +

ζp(p3−i − p i) −

ζs(s3−i − s i) −

ζSAL(SAL3−i − SALi) fr(p, SAL ) = 2 i=1 pi D i fm(s) = 2 i=1ωi D i gr(SAL) = 2 i=1ωi D i + I SAL gm(s) = 2 i=1ci D i + I s α= 1 , 0 ≤β≤ 1 and γ= 0

Ma et al. (2018) both competing manufacturers or only one of them or retailer pi , ωi and s i Di = LOY D 0 −ηpi + λp3−i + μsi fr(pi) = 2 i=1 pi D i fmi(ωi , s ) = ωi D i gr(pi) = 2 i=1ωi D i gm(s) = c i D i + I s α= 1 , 0 ≤β≤ 1 and γ= 0

tion,inventoryholdingandtransportloadsshouldbealso incorpo-rated.In ordertosupport thedecisionmakers intakingsourcing decisionsinslowbalisationvsglobalisationcontexts,more compre-hensive decision-aid toolshave to be developped. For SC design, complexmodelsshouldbedevelopedtodeterminetheoptimal de-cisionsrelatedtofacilitylocations,productiontechnology,material ﬂows,supplierselectionandtransportationmodes.

Thequestions relatedto thehuman capital,technology devel-opment and environment impact should not be ignored in new analytical models. The new models have to be developed in the lightofsustainabledevelopmentcoveringalltheaspectsimpacting thesupplyChainperformanceandresiliencethatwasshowntobe veryimportantunderthepossibilityofnewglobalcrisis.Although thesequestions are discussed in the literature for over a decade

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now,thereisstillalackofholisticanalyticalmodels.Inparticular, thesustainability aspectshould includelegislation, investmentto reduce theoverall emissions, customers’ sensitivityto the green-nessoftheproductandCSRinitiatives,thedifferentprocesses con-sideredforemissions,etc.Regardingtheenvironmentallegislation forexample,such factors ascarbon cap,tradeand carbontax as well asgovernmental subsidiesmay inﬂuence the sourcing deci-sions.Analyticalmodelsofthedifferentlegislationschemesshould bedevelopednotonlytosupportthedecisionmakersbutalsoto give insightful perspectives for governments to guide the policy makersinbuildingsuchlegislationschemes.

There isalso aneed fora standardandcommon deﬁnitionof thedifferentsourcingstrategies,insourcing,outsourcing,offshoring andreshoring.Thiswillbecrucialinthedeﬁnitionofthe respon-sibilities for the emissions and negative impacts on the society acrossthesupplychain.Thismeansthattheappropriateanalytical modelsshould includedifferent SC activities, such asproduction, inventoryandtransport,tobetterassestheenvironmentalans so-cialimpactsofthesupplychain.

6. Conclusion

The sourcing strategy is a complex multi-disciplinary decision that keeps evolvingdue to the dynamicbehaviour of the global marketandtheinternationaltradeconditionsandlegislation.Itis linkedto supplychain design,facilitylocation, supplierselection, inventorymanagement,productionmanagement,logistics manage-mentandcontract arrangement. It hasgained researchers’ atten-tionfordecadesnow.

Inthisstudy,weprovideanoverviewofthedriversand obsta-clesforslowbalisationandglobalisationandtheanalyticalmodels developedintheliteratureforsourcingdecisionsinthesedifferent contexts.Wecanobserveagapbetweenthefactorsthatinﬂuence thedecisionstakeninpracticeandtheanalyticalmodelspresented intheliterature.A discussiononthe researchperspectivesinthe ﬁeldandthe factorstobe includedin thenewanalyticalmodels isalsopresented.

DeclarationofCompetingInterest

Theauthorsdeclarethattheydonothaveanyfinancialor non-financialconflictofinterests

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