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Big Data Analytics in Supply Chain Management: Theory and Applications


Big Data Analytics in Supply Chain Management: A Scientometric AnalysisIntroductionAnalysisData CollectionScientometric AnalysisAn Analysis on KeywordsA Short Analysis on Countries and AffiliationsCo-author AnalysisAn Analysis on SourcesCo-citation AnalysisDiscussion and ConclusionReferencesSupply Chain Analytics Technology for Big DataIntroductionIntroduction to Supply Chain Analytics TechnologyNecessity for Supply Chain Analytics for Big DataFeatures of Supply Chain AnalyticsOpportunities and Applications for Supply Chain AnalyticsOpportunities for Supply Chain AnalyticsProcess Specific applications of Big Data AnalyticsTools for Supply Chain AnalyticsSupply Chain Analytics MethodsDescriptive AnalyticsPredictive AnalyticsPrescriptive AnalyticsSupply Chain Challenges in Adopting Big Data AnalyticsFuture of Supply Chain AnalyticsConclusionReferencesPrioritizing the Barriers and Challenges of Big Data Analytics in Logistics and Supply Chain Management Using MCDM MethodIntroduction to Big Data AnalyticsBarriers to BDA: BackgroundMethodologyThe Steps of HBWMDetermining the Consistency RateResults and DiscussionConclusionReferencesBig Data in Procurement 4.0: Critical Success Factors and SolutionsIntroductionMacroenvironmentLiterature ReviewMethodologyCritical Success Factors for Procurement 4.0CyberneticsCommunicationCONTROLLERSHIPCollaborationConnectionCognitionCoordinationConfidenceCritical Success Factors and Procurement CycleSupporting SolutionsApplication of the ModelConclusions, Practical Implications, and Future ResearchAbbreviationsReferencesRecommendation Model Based on Expiry Date of Product Using Big Data AnalyticsIntroductionStatement and ObjectiveLiterature SurveyProduct Recommendation SystemUser’s Preferences/ ChoicesKeyword ClassificationImplementation of Statistical Analysis for ProductsOne-Sided and Two-Sided T-Test of Data SetsLinear Regression ModelExperimental AssessmentEffects of Recommendation SystemRecommendation for Ratings and Reviews of the Customer of ProductsAdvantages of the Recommendation SystemConclusionReferencesComparing Company’s Performance to Its Peers: A Data Envelopment ApproachIntroductionPrevious Related ResearchMethodology DescriptionSlacks-Based Measure of EfficiencyMultiple Criteria Decision-MakingEmpirical ResultsData Description and PreprocessingMain DEA ResultsDiscussion on the Best and Worst Ranked CompaniesRobustness Checking – MCDMFurther Possible Integrations of DEA and MCDMConclusionAppendixReferencesSustainability, Big Data, and Consumer Behavior: A Supply Chain FrameworkBackgroundAttributes Impacting Consumer’s Purchasing BehaviorPurchase PriceDerived UtilityProduct QualityProduct Support ServicesReturn PolicySummaryA Bidirectional Supply Chain FrameworkConcluding RemarksReferencesA Soft Computing Techniques Application of an Inventory Model in Solving Two-Warehouses Using Cuckoo Search AlgorithmIntroductionInventory Models with Two WarehousesCuckoo Behavior and Lévy FlightsRelated WorksAssumption and NotationsMathematical Formulation of Model and AnalysisCuckoo Search AlgorithmNumerical AnalysisSensitivity AnalysisConclusionsReferencesAn Overview of the Internet of Things Technologies Focusing on Disaster ResponseIntroductionArtificial IntelligenceInternet of ThingsThe Use of IoT and AI for Risk and Disaster ManagementThe IoT Relationship in the Supply Chain During DisasterDiscussionFuture TrendsConclusionsReferencesClosing the Big Data Talent GapResearch Benefits | What’s in It for Me?The State of Big Data EducationData Scientist vs Data AnalystA Qualitative ApproachDependability and TrustworthinessData AnalysisBig Data InitiativesYears of Big Data InitiativesSize of Big Data TeamsBig Data Resources NeededWhere Are Organizations Finding Big Data Resources?Challenges Finding Big Data ResourcesQualities Most Difficult to Find in CandidatesThe Ideal Big Data Specialist CandidateNumber of Candidates InterviewedEasing the Big Data Hiring ProcessIT Manager InterviewsSpecialist InterviewsKey Analysis & FindingsTheme 1: “ Lacking”Theme 2: “ Passion”Theme 3: Soft SkillsTheme 4: Technical SkillsConclusionDiscussionReferences
 
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