AIDecisionDefinitionandBusinessStrategyCoordinationinIntelligentEnterpriseSupplyChainOptimizationfromthePerspectiveofSystemAutonomyBoundary

Abstract

ThisstudyfocusesontheproblemofvaguedefinitionofAIdecisionsandinsufficientcoordinationofbusinessstrategiesintheoptimizationprocessofintelligententerprisesupplychainfromtheperspectiveofsystemautonomyboundaries.TheaimistoclarifytheconnotationandextensionofAIdecisionsinthisscenarioandexploreeffectivepathsfortheircoordinationwithbusinessstrategies.Intheory,bysystematicallyreviewingrelevantliteratureonsystemautonomy,supplychainoptimization,andAIdecision-making,extractingcoretheoreticalcorrelations,andconstructinganAIdecision-makingdefinitionframeworkbasedonsystemautonomyboundaries;Inpractice,bycombiningtypicalscenariosofintelligententerprisesupplychainsuchasprocurementcollaboration,inventoryscheduling,andlogisticsdistribution,weprovidepracticalimplementationstrategiesforAIdecision-makingandbusinessstrategycollaborationforenterprises.Bycombiningliteratureresearchwithsinglecaseanalysis,itisexpectedtoformanAIdecision-makingsystemandcollaborativemodelcovering"definitionmechanismpractice",providingtheoreticalreferenceandpracticalguidanceforintelligententerprisesupplychainmanagement.

Keywords:Systemautonomyboundary;AIdecision-making;Supplychainoptimization;Businessstrategycollaboration;IntelligentEnterprise

Introduction

Drivenbythewaveofdigitizationandintelligence,intelligententerpriseshaveincreasinglystringentrequirementsforsupplychainoperationefficiencyandstrategicadaptability.AItechnology,withitsdataprocessingcapabilitiesandreal-timeresponseadvantages,hasopenedupnewpathsforoptimizingsupplychaindecisions.Fromthepracticeoftheconsumerelectronicsindustry,topenterpriseshaveachieveddatatransparencyinpartstraceabilityandinventorysharingthroughtheintegrationoflightweightAIalgorithmsandblockchaintechnology.Whileimprovingtheaccuracyofdemandforecasting(averageimprovementof15%-20%)andlogisticssafetyfactors,theyhavenotclearlydefinedtheboundariesofAIinkeydecisionssuchas"whethertoadjustcoresuppliers"and"whethertoactivateemergencyinventory",resultinginconflictsbetweenAIdecisionsandthecompany's"supplychainresilience"strategyinsomescenarios

ADDINEN.CITE<EndNote><Cite><Author>Byeon</Author><Year>2025</Year><RecNum>56</RecNum><DisplayText>(Byeonetal.,2025)</DisplayText><record><rec-number>56</rec-number><foreign-keys><keyapp="EN"db-id="aextv02fzv00r1evaf5xvxwzpazdt55f0fzw"timestamp="1763811223">56</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Byeon,Haewon</author><author>Alsaadi,Mahmood</author><author>Keshta,Ismail</author><author>Ahanger,TariqAhamed</author><author>Safarova,Nodira</author><author>Aldawsari,Hamad</author><author>Cascone,Lucia</author><author>Shabaz,Mohammad</author></authors></contributors><titles><title>LightweightAIandBlockchainOptimizationforEnhancingConsumerElectronicsDecision-Making</title><secondary-title>ConsumerElectronics,IEEETransactionson</secondary-title></titles><periodical><full-title>ConsumerElectronics,IEEETransactionson</full-title></periodical><pages>6007-6015</pages><volume>71</volume><number>2</number><dates><year>2025</year></dates><urls></urls></record></Cite></EndNote>

(Byeonetal.,2025)

.Inaddition,thedecisionsupportpotentialofbiglanguagemodelsinsupplychainriskwarning,suppliercommunicationdocumentgeneration,andotheraspectsisgraduallyemerging.However,duetothelackofcleardefinitions,itisdifficultforenterprisestodeterminetheirroleinstrategicdecisionssuchas"long-termnetworkplanningforthesupplychain",furtherhighlightingthenecessityofclarifyingAIdecisiondefinitions

ADDINEN.CITE<EndNote><Cite><Author>Aghaei</Author><Year>2025</Year><RecNum>54</RecNum><DisplayText>(Aghaeietal.,2025)</DisplayText><record><rec-number>54</rec-number><foreign-keys><keyapp="EN"db-id="aextv02fzv00r1evaf5xvxwzpazdt55f0fzw"timestamp="1763811223">54</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Aghaei,Raha</author><author>Kiaei,AliA.</author><author>Boush,Mahnaz</author><author>Vahidi,Javad</author><author>Barzegar,Zeynab</author><author>Rofoosheh,Mahan</author></authors></contributors><titles><title>ThePotentialofLargeLanguageModelsinSupplyChainManagement:AdvancingDecision-Making,Efficiency,andInnovation</title></titles><dates><year>2025</year></dates><urls></urls></record></Cite></EndNote>

(Aghaeietal.,2025)

.

However,theproblemofvaguedefinitionofAIdecision-makinganddisconnectionfrombusinessstrategycollaborationinintelligententerprisesisgraduallybecomingprominent.WhensomemanufacturingcompaniesintroduceAIforsupplychaindemandforecasting,theyonlyfocusontheaccuracyofalgorithmsforshort-termsalesforecasting,butignorewhetherthedecisionmatchesthecompany'slong-termstrategyof"expandingemergingmarkets"-forexample,theAIforecastofacertainhomeappliancecompanyisonlybasedonhistoricalsalesdataanddoesnotincludeemergingmarketconsumptiontrends,resultinginproductionplansbiasedtowardstraditionalproducts,whichcontradictsthestrategyof"seizingthenewtrackofsmarthomeappliances"

ADDINEN.CITE<EndNote><Cite><Author>Grover</Author><Year>2025</Year><RecNum>62</RecNum><DisplayText>(Grover,2025)</DisplayText><record><rec-number>62</rec-number><foreign-keys><keyapp="EN"db-id="aextv02fzv00r1evaf5xvxwzpazdt55f0fzw"timestamp="1763811223">62</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Grover,Nitin</author></authors></contributors><titles><title>AI-EnabledSupplyChainOptimization</title><secondary-title>InternationalJournalofAdvancedResearchinScience,CommunicationandTechnology</secondary-title></titles><periodical><full-title>InternationalJournalofAdvancedResearchinScience,CommunicationandTechnology</full-title></periodical><pages>28-44</pages><dates><year>2025</year></dates><urls></urls></record></Cite></EndNote>

(Grover,2025)

.Atthesametime,asacomplexsystemwithautonomousadjustmentcapabilities,theintelligententerprisesupplychainispronetotargetdeviationiftheAIdecisionboundaryisnotclearlydefined.Forexample,theAIinventorysystemofacertainautomotivepartsenterpriseonlyadjuststheorderquantitybasedonthe"inventoryturnoverrate"asthetarget,ignoringtherequirementof"reducingtransportationcarbonemissions"intheenterprise's"greensupplychain"strategy,resultinginanincreaseinthetransportationfrequencyofsmallbatchhigh-frequencyorders,andultimatelyexceedingthecarbonemissionsby12%

ADDINEN.CITE<EndNote><Cite><Author>Khoa</Author><Year>2024</Year><RecNum>59</RecNum><DisplayText>(Khoaetal.,2024)</DisplayText><record><rec-number>59</rec-number><foreign-keys><keyapp="EN"db-id="aextv02fzv00r1evaf5xvxwzpazdt55f0fzw"timestamp="1763811223">59</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Khoa,BuiQuoc</author><author>Nguyen,HoangTien</author><author>Anh,DinhBaHung</author><author>Ngoc,NguyenMinh</author></authors></contributors><titles><title>ImpactofArtificialIntelligence'sPartinSupplyChainPlanningandDecisionMakingOptimization</title><secondary-title>InternationalJournalofMultidisciplinaryResearchandGrowthEvaluation</secondary-title></titles><periodical><full-title>InternationalJournalofMultidisciplinaryResearchandGrowthEvaluation</full-title></periodical><pages>837-856</pages><volume>5</volume><number>6</number><dates><year>2024</year></dates><urls></urls></record></Cite></EndNote>

(Khoaetal.,2024)

.

Exploringthisissuehassignificanttheoreticalandpracticalvalue.Intheory,thetheoryofsystemautonomycanbeextendedfromautomationsystemsandurbansocialtechnologysystemstothesupplychainfield,clarifyingtheuniquecharacteristicsofsystemautonomyinsupplychainscenarios,fillingtheresearchgapintheintegrationof"systemautonomyAIdecision-makingstrategiccollaboration"inintelligententerprisesupplychains

ADDINEN.CITE<EndNote><Cite><Author>Chen</Author><Year>2021</Year><RecNum>21</RecNum><DisplayText>(Chenetal.,2021;Li,2022)</DisplayText><record><rec-number>21</rec-number><foreign-keys><keyapp="EN"db-id="aextv02fzv00r1evaf5xvxwzpazdt55f0fzw"timestamp="1763811223">21</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Chen,Hualong</author><author>Wen,Yuanqiao</author><author>Zhu,Man</author><author>Huang,Yamin</author><author>Hahn,Axel</author></authors></contributors><titles><title>FromAutomationSystemtoAutonomousSystem:AnArchitecturePerspective</title><secondary-title>JournalofMarineScienceandEngineering</secondary-title></titles><periodical><full-title>JournalofMarineScienceandEngineering</full-title></periodical><pages>24</pages><volume>9</volume><number>645</number><dates><year>2021</year></dates><urls></urls></record></Cite><Cite><Author>Li</Author><Year>2022</Year><RecNum>24</RecNum><record><rec-number>24</rec-number><foreign-keys><keyapp="EN"db-id="aextv02fzv00r1evaf5xvxwzpazdt55f0fzw"timestamp="1763811223">24</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Li,Weizi</author></authors></contributors><titles><title>UrbanSocio-TechnicalSystems:AnAutonomyandMobilityPerspective</title></titles><dates><year>2022</year></dates><urls></urls></record></Cite></EndNote>

(Chenetal.,2021;Li,2022)

；Inpractice,itcanhelpenterprisesclarifythedecision-makingroleofAIinvariouslinksofthesupplychain,andenhancethesynergybetweenthesupplychainandstrategy.Forexample,intheprocurementprocess,byclarifyingthatAIisonlyresponsibleforoperationaldecisionssuchas"qualifiedsupplierscreening"and"procurementpricecomparison",whilestrategicdecisionssuchas"coresuppliercooperationmodeadjustment"aremanuallyled,itcaneffectivelysupporttheenterprise's"supplierdiversification"strategy

ADDINEN.CITE<EndNote><Cite><Author>Tsintotas</Author><Year>2025</Year><RecNum>55</RecNum><DisplayText>(Tsintotasetal.,2025)</DisplayText><record><rec-number>55</rec-number><foreign-keys><keyapp="EN"db-id="aextv02fzv00r1evaf5xvxwzpazdt55f0fzw"timestamp="1763811223">55</key></foreign-keys><ref-typename="ConferenceProceedings">10</ref-type><contributors><authors><author>Tsintotas,KonstantinosA.</author><author>Moutsis,StavrosN.</author><author>Kansizoglou,Ioannis</author><author>Konstantinidis,FotiosK.</author><author>Gasteratos,Antonios</author></authors></contributors><titles><title>TheAdventofAIinModernSupplyChain</title><secondary-title>OlympusInternationalConferenceonSupplyChains</secondary-title></titles><dates><year>2025</year></dates><urls></urls></record></Cite></EndNote>

(Tsintotasetal.,2025)

.Meanwhile,theresearchfindingscanbeappliedacrossindustries.Forexample,pharmaceuticalcompaniescandrawonAIdecision-makingandstrategiccoordinationlogictooptimizetheboundarybetween"coldchaintemperaturemonitoring"(AIautonomousdecision-making)and"vaccineemergencyallocation"(human-machinecollaborativedecision-making)inthevaccinesupplychain,balancingsupplychainefficiencyanddrugsafetystrategy

ADDINEN.CITE<EndNote><Cite><Author>Tirkolaee</Author><Year>2023</Year><RecNum>51</RecNum><DisplayText>(Tirkolaeeetal.,2023)</DisplayText><record><rec-number>51</rec-number><foreign-keys><keyapp="EN"db-id="aextv02fzv00r1evaf5xvxwzpazdt55f0fzw"timestamp="1763811223">51</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Tirkolaee,ErfanBabaee</author><author>Torkayesh,AliEbadi</author><author>Tavana,VladimirDing,Weiping</author></authors></contributors><titles><title>Anintegrateddecisionsupportframeworkforresilientvaccinesupplychainnetworkdesign</title><secondary-title>EngineeringApplicationsofArtificialIntelligence:TheInternationalJournalofIntelligentReal-TimeAutomation</secondary-title></titles><periodical><full-title>EngineeringApplicationsofArtificialIntelligence:TheInternationalJournalofIntelligentReal-TimeAutomation</full-title></periodical><volume>126</volume><number>Pt.B</number><dates><year>2023</year></dates><urls></urls></record></Cite></EndNote>

(Tirkolaeeetal.,2023)

.

LiteratureReview

ResearchonSupplyChainOptimizationofIntelligentEnterprises

Intheoptimizationofsmartenterprisesupplychains,AItechnologyprovidesmultidimensionalsolutionstoindustry-specificpainpoints.Inagriculture,researchersinNorthChinadevelopedanAIoptimizationmodeltoaddressthechallengesof"fragmentedcollectionandhightransportationcosts"inagriculturalbiomasssupplychains.Byanalyzingbiomassyielddistributionandtransportationroutecongestioncoefficientsthroughmachinelearningalgorithms,themodelachievesdynamicoptimizationofcollectionpointlocationsanddeliveryroutes,ultimatelyreducingsupplychainoperationalcostsby8%-10%andimprovingbiomassutilizationefficiency

ADDINEN.CITE<EndNote><Cite><Author>Wu</Author><Year>2022</Year><RecNum>48</RecNum><DisplayText>(Wuetal.,2022)</DisplayText><record><rec-number>48</rec-number><foreign-keys><keyapp="EN"db-id="aextv02fzv00r1evaf5xvxwzpazdt55f0fzw"timestamp="1763811223">48</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Wu,Juanjuan</author><author>Zhang,Jian</author><author>Yi,Weiming</author><author>Cai,Hongzhen</author><author>Li,Yang</author><author>Su,Zhanpeng</author></authors></contributors><titles><title>Agri-biomasssupplychainoptimizationinnorthChina:Modeldevelopmentandapplication</title><secondary-title>Energy</secondary-title></titles><periodical><full-title>Energy</full-title></periodical><pages>239</pages><number>Jan.15Pt.D</number><dates><year>2022</year></dates><urls></urls></record></Cite></EndNote>

(Wuetal.,2022)

.Inthedairyindustry,scholarsfocusedontheenvironmentalsensitivityofpigsemenproductionanditshighdeliverytimelinessrequirements.UsingAItechnologytoidentifykeyfactorsaffectingproductionefficiencysuchastemperaturefluctuationsinsemenstorageanddeliveryradius,predictivemodelswereemployedtooptimizeproductionbatchesanddeliveryschedules.Thisresultedina5%increaseinsemensurvivalrateanda15%reductionindeliverydelays,achievingcomprehensiveoptimizationoftheentiresupplychainprocess

ADDINEN.CITE<EndNote><Cite><Author>Gorr</Author><Year>2022</Year><RecNum>57</RecNum><DisplayText>(Gorr,2022)</DisplayText><record><rec-number>57</rec-number><foreign-keys><keyapp="EN"db-id="aextv02fzv00r1evaf5xvxwzpazdt55f0fzw"timestamp="1763811223">57</key></foreign-keys><ref-typename="Thesis">32</ref-type><contributors><authors><author>Gorr,AllisonQuick.</author></authors></contributors><titles><title>IdentificationofFactorsAffectingDairySireSemenProductionandOptimizationoftheAISupplyChain</title></titles><dates><year>2022</year></dates><publisher>TheUniversityofWisconsin-Madison.;TheUniversityofWisconsin-Madison.;TheUniversityofWisconsin-Madison.</publisher><urls></urls></record></Cite></EndNote>

(Gorr,2022)

.

Intechnicalapplicationscenarios,theintegrationoflightweightAIandblockchaintechnologyhasemergedasakeyoptimizationstrategyforconsumerelectronicssupplychains.AIalgorithmsprocessinventorydatainreal-time,whileblockchainensuresdataintegrity.Thiscombinationenablesenterprisestoenhancereal-timedecision-makingresponsespeedby20%anddatacredibilityby30%whenaddressingtheindustry'scharacteristicsofrapidproductiterationandvolatiledemand

ADDINEN.CITE<EndNote><Cite><Author>Byeon</Author><Year>2025</Year><RecNum>56</RecNum><DisplayText>(Byeonetal.,2025)</DisplayText><record><rec-number>56</rec-number><foreign-keys><keyapp="EN"db-id="aextv02fzv00r1evaf5xvxwzpazdt55f0fzw"timestamp="1763811223">56</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Byeon,Haewon</author><author>Alsaadi,Mahmood</author><author>Keshta,Ismail</author><author>Ahanger,TariqAhamed</author><author>Safarova,Nodira</author><author>Aldawsari,Hamad</author><author>Cascone,Lucia</author><author>Shabaz,Mohammad</author></authors></contributors><titles><title>LightweightAIandBlockchainOptimizationforEnhancingConsumerElectronicsDecision-Making</title><secondary-title>ConsumerElectronics,IEEETransactionson</secondary-title></titles><periodical><full-title>ConsumerElectronics,IEEETransactionson</full-title></periodical><pages>6007-6015</pages><volume>71</volume><number>2</number><dates><year>2025</year></dates><urls></urls></record></Cite></EndNote>

(Byeonetal.,2025)

.Furthermore,largelanguagemodelsareexpandingtheirapplicationsinsupplychains.Beyondtraditionaldemandforecastingandriskalerts,theycanautomaticallygeneratesuppliercommunicationletters,supplychainoptimizationreports,andevenanalyzetheimpactofindustrypolicydocumentsonsupplychains,therebyfurtherexpandingtheboundariesofAIapplicationsinsupplychains

ADDINEN.CITE<EndNote><Cite><Author>Aghaei</Author><Year>2025</Year><RecNum>54</RecNum><DisplayText>(Aghaeietal.,2025)</DisplayText><record><rec-number>54</rec-number><foreign-keys><keyapp="EN"db-id="aextv02fzv00r1evaf5xvxwzpazdt55f0fzw"timestamp="1763811223">54</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Aghaei,Raha</author><author>Kiaei,AliA.</author><author>Boush,Mahnaz</author><author>Vahidi,Javad</author><author>Barzegar,Zeynab</author><author>Rofoosheh,Mahan</author></authors></contributors><titles><title>ThePotentialofLargeLanguageModelsinSupplyChainManagement:AdvancingDecision-Making,Efficiency,andInnovation</title></titles><dates><year>2025</year></dates><urls></urls></record></Cite></EndNote>

(Aghaeietal.,2025)

.

ResearchonSystemAutonomyandSupplyChainSystem

Asacoreattributeofcomplexsystems,thedefinitionofsystemautonomyanditsinfluencingfactorsiscrucialforunderstandingsupplychainoperations.Fromanevolutionaryperspective,thetransitionfromautomatedsystemstoautonomoussystemsrequiresanarchitecturefeaturingthreelayers:perception,decision-making,andexecution.Theperceptionlayercollectsenvironmentaldata,thedecision-makinglayergeneratesdata-drivensolutions,andtheexecutionlayerimplementsdecisions.Thisframeworkprovidesatheoreticalbasisforanalyzingsupplychainautonomy,analogoustothe"datacollection-planning-logisticsexecution"processinsupplychains

ADDINEN.CITE<EndNote><Cite><Author>Chen</Author><Year>2021</Year><RecNum>21</RecNum><DisplayText>(Chenetal.,2021)</DisplayText><record><rec-number>21</rec-number><foreign-keys><keyapp="EN"db-id="aextv02fzv00r1evaf5xvxwzpazdt55f0fzw"timestamp="1763811223">21</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Chen,Hualong</author><author>Wen,Yuanqiao</author><author>Zhu,Man</author><author>Huang,Yamin</author><author>Hahn,Axel</author></authors></contributors><titles><title>FromAutomationSystemtoAutonomousSystem:AnArchitecturePerspective</title><secondary-title>JournalofMarineScienceandEngineering</secondary-title></titles><periodical><full-title>JournalofMarineScienceandEngineering</full-title></periodical><pages>24</pages><volume>9</volume><number>645</number><dates><year>2021</year></dates><urls></urls></record></Cite></EndNote>

(Chenetal.,2021)

.Inurbansocio-technologicalsystemresearch,scholarshaveidentifiedtechnologicalmaturity,externalpolicyconstraints,andorganizationalmanagementmodelsaskeydeterminantsofsystemautonomy.Thesefactorsequallyapplytosupplychains—forinstance,aretailcompany'sinabilitytoautonomouslymanageinventoryduetoinadequatedataprocessingtechnology,orachemicalplant'srelianceonmanualapprovalforenvironmentalcompliancechecksinitssupplychain,bothdemonstratinghowtheseconstraintslimitsupplychainautonomy

ADDINEN.CITE<EndNote><Cite><Author>Li</Author><Year>2022</Year><RecNum>24</RecNum><DisplayText>(Li,2022)</DisplayText><record><rec-number>24</rec-number><foreign-keys><keyapp="EN"db-id="aextv02fzv00r1evaf5xvxwzpazdt55f0fzw"timestamp="1763811223">24</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Li,Weizi</author></authors></contributors><titles><title>UrbanSocio-TechnicalSystems:AnAutonomyandMobilityPerspective</title></titles><dates><year>2022</year></dates><urls></urls></record></Cite></EndNote>

(Li,2022)

.

Insupplychainsystemresearch,theinformationvalueandcoordinationchallengesofclosed-loopsupplychainshavegarneredsignificantattention.Scholarshaveanalyzedhowinformationsharingimpactssystemautonomythroughdual-channelclosed-loopsupplychainmodels.Theirfindingsrevealthatwhenmanufacturersandretailerssharesalesandrecyclingdata,thesystem'sautonomouscoordinationcapabilityimprovessubstantially.Forinstance,manufacturerscanadjustremanufacturingplansbasedonshareddata,whileretailersoptimizerecyclingpointlayouts,resultingina12%increaseinoverallsupplychainprofitability

ADDINEN.CITE<EndNote><Cite><Author>Sepúlveda-Rojas</Author><Year>2020</Year><RecNum>61</RecNum><DisplayText>(Sepúlveda-Rojas&Ternero,2020)</DisplayText><record><rec-number>61</rec-number><foreign-keys><keyapp="EN"db-id="aextv02fzv00r1evaf5xvxwzpazdt55f0fzw"timestamp="1763811223">61</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>JuanPedroSepúlveda-Rojas</author><author>Ternero,Rodrigo</author></authors></contributors><titles><title>AnalysisoftheValueofInformationandCoordinationinaDyadicClosedLoopSupplyChain</title><secondary-title>Sustainability</secondary-title></titles><periodical><full-title>Sustainability</full-title></periodical><volume>12</volume><dates><year>2020</year></dates><urls></urls></record></Cite></EndNote>

(Sepúlveda-Rojas&Ternero,2020)

.Additionally,insatellitecommunicationsystemstudies,researchersemphasizefromanationalsecurityperspectivethattheboundariesofsystemautonomymustbalance"operationalefficiency"with"securityandcontrollability."Forexample,satellitecommunicationsrequireautonomousresponsetosignalinterference,butcorecommandsstillneedmanualreview.Thislogicparallelscriticalmaterialsupplychains,whereautonomyboundariesmuststrikeabalancebetween"rapiddemandresponse"and"supplychainsecurity"toavoidriskscausedbyexcessiveautonomy

ADDINEN.CITE<EndNote><Cite><Author>Doicariu</Author><Year>2022</Year><RecNum>23</RecNum><DisplayText>(Doicariu,2022)</DisplayText><record><rec-number>23</rec-number><foreign-keys><keyapp="EN"db-id="aextv02fzv00r1evaf5xvxwzpazdt55f0fzw"timestamp="1763811223">23</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Doicariu,Daniel</author></authors></contributors><titles><title>SATELLITECOMMUNICATIONSSYSTEM-APERSPECTIVEONNATIONALSECURITY</title><secondary-title>ScientificResearch&EducationintheAirForce-AFASES</secondary-title></titles><periodical><full-title>ScientificResearch&EducationintheAirForce-AFASES</full-title></periodical><volume>2022</volume><dates><year>2022</year></dates><urls></urls></record></Cite></EndNote>

(Doicariu,2022)

.

ResearchontheIntegrationofSystemAutonomyBoundarieswithAIDecisionmakingandBusinessStrategy

Theintegrationofsystemautonomyboundaries,servingasacriticallinkbetweenAIdecision-makingandbusinessstrategy,remainsaweaklinkincurrentresearch.Inbiofuelsupplychainstudies,scholarshavecategorizeddecision-makinglevelsintostrategic,tactical,andoperationaltiersbasedontheirscopeandcomplexity,providingaframeworkfordefiningsystemautonomyboundariesandAIdecisionscopesatdifferentlevels

ADDINEN.CITE<EndNote><Cite><Author>Pishvaee</Author><Year>2021</Year><RecNum>58</RecNum><DisplayText>(Pishvaeeetal.,2021)</DisplayText><record><rec-number>58</rec-number><foreign-keys><keyapp="EN"db-id="aextv02fzv00r1evaf5xvxwzpazdt55f0fzw"timestamp="1763811223">58</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Pishvaee,MirSaman</author><author>Mohseni,Shayan</author><author>Bairamzadeh,Samira</author></authors></contributors><titles><title>Decision-makinglevelsinbiofuelsupplychain</title><secondary-title>BiomasstoBiofuelSupplyChainDesignandPlanningUnderUncertainty</secondary-title></titles><periodical><full-title>BiomasstoBiofuelSupplyChainDesignandPlanningUnderUncertainty</full-title></periodical><dates><year>2021</year></dates><urls></urls></record></Cite></EndNote>

(Pishvaeeetal.,2021)

.Similarly,inresearchonsustainablesupplychainmanagementdrivers,scholarsemployedtheAI-ISM(ExplanatoryStructuralModel)methodtoidentifykeyfactorsinfluencingsupplychainsystemautonomy,suchas"governmentenvironmentalpolicies,""corporategreenstrategies,"and"supplychaindatasharing."ThesefindingsofferabasisforestablishingAIdecisionboundaries

ADDINEN.CITE<EndNote><Cite><Author>Roshanpour</Author><Year>2025</Year><RecNum>50</RecNum><DisplayText>(Roshanpouretal.,2025)</DisplayText><record><rec-number>50</rec-number><foreign-keys><keyapp="EN"db-id="aextv02fzv00r1evaf5xvxwzpazdt55f0fzw"timestamp="1763811223">50</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Roshanpour,Reza</author><author>Parsanejad,Mohammadreza</author><author>Pishvaee,MirSaman</author></authors></contributors><titles><title>AnAI-ISMMethodologyforStructuralModelingofSustainableSupplyChainManagementDrivers</title><secondary-title>Access,IEEE</secondary-title></titles><periodical><full-title>Access,IEEE</full-title></periodical><pages>76481-76496</pages><volume>13</volume