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Machine Learning Support in Supply Chain Management- Potential PhD Topics

ML is a way of Programming with Artificial Intelligence. It replaces set rules of calculations with the program. With the given set of data, algorithms statistics, it combines and represents in a model form. These models will make predictions based on the input data.<br>ttt<br>For #Enquiry:t<br>Website URL: https://www.phdassistance.com/services/phd-data-analysis/computer-programming/<br>India: 91 91769 66446<br>Email: info@phdassistance.com<br>

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Machine Learning Support in Supply Chain Management- Potential PhD Topics

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  1. HowCanIApplyTo MachineLearning ToPredictSupplyChainRisks- PotentialPhDTopics Copyright©2022 PhdAssistance.Allrights reserved Journalsupport |Dissertation support| Analysis |Data collection| Coding& Algorithms |Editing &Peer- Reviewing

  2. Copyright©2022 PhdAssistance.Allrights reserved TODAY'SDISCUSSION Inbrief TheroleofMLinpredictingsupplychain risks Importance of ML in supply chain risks InterpretationbasedonMachineLearning Conclusion References Journalsupport |Dissertation support| Analysis |Data collection| Coding& Algorithms |Editing &Peer- Reviewing

  3. Copyright©2022 PhdAssistance.Allrights reserved InBrief In a world full of competition where every business is struggling to put itself ahead, MachineLearning(ML)cangrantsomeexclusive opportunities. Fromincreasingprofitmarginstoreducingcosts and engaging customers, machine learning can helpyouinmanyways. Contd... Journalsupport |Dissertation support| Analysis |Data collection| Coding& Algorithms |Editing &Peer- Reviewing

  4. Copyright©2022 PhdAssistance.Allrights reserved As the world is triggered by the COVID-19 situation,managingandhandlingthesupply chainriskiswhateveryoneisthinkingabout. From lowering the risk and improving the forecastaccuracymachinelearningistheUSBin thesupplychains. Journalsupport |Dissertation support| Analysis |Data collection| Coding& Algorithms |Editing &Peer- Reviewing

  5. Copyright©2022 PhdAssistance.Allrights reserved TheroleofMLinpredictingsupply chainrisks This application is based on artificial intelligencethatsearchesfortrends,accuracy, patterns and quality which makes your experiencebetterinthesystem. EspeciallytheMLalgorithmswhichleadtothe platformofsupplychainmanagementhelpsto predict various risks involved from unknown factors this will help in keeping up the constantflowofallgoodsinthesupplychain. Journalsupport |Dissertation support| Analysis |Data collection| Coding& Algorithms |Editing &Peer- Reviewing

  6. Copyright©2022 PhdAssistance.Allrights reserved ImportanceofMLinsupplychainrisks Manyrenownedfirmsarenowpayingkeenattention to ML to improve their business efficiency and predictriskinsupplychains. So, let’s take some time to understand how AI addressesthevariousproblemsinvolvedinsupply chains. Moreover, we will also learn about the advancedTechnologiesroleintheManagementof thesupplychain. Journalsupport |Dissertation support| Analysis |Data collection| Coding& Algorithms |Editing &Peer- Reviewing

  7. Copyright©2022 PhdAssistance.Allrights reserved 1.Costefficiency MLcanbegreatinwastereductionandimprovingthe quality.Itcanhaveanenormousimpactonthesupply chains. Thepowerliesinitsalgorithmsthatdetectthepattern fromthedataandhelpinpredictingtheinvolvedrisks in supply chains. ML can continuously integrate information and emerging trends to meet the new demands.Thus,it’sveryusefulforretailersandbusiness to deal with aggressive markdowns and helping them incostefficiency. Journalsupport |Dissertation support| Analysis |Data collection| Coding& Algorithms |Editing &Peer- Reviewing

  8. Copyright©2022 PhdAssistance.Allrights reserved 2. Enablesproductflow With its set sequential operations it enables smoothproductflow. Itmonitorstheproduct line and ensures the targeted process of productionisachieved. It offers an overview of the system thus it minimizesrisksinvolvedinthesupplychain. Journalsupport |Dissertation support| Analysis |Data collection| Coding& Algorithms |Editing &Peer- Reviewing

  9. Copyright©2022 PhdAssistance.Allrights reserved 3.Transparentmanagement MI can communicate and explain the risk involved in supply chains with transparency. It helps humans to understandtheprocedureandtaketherightdecision. Frome-commercegiantstoosmalltomedium-sized business MI helps to manage their sales and predict futureriskswithtransparency. Moreover,ithelpsinrelationshipmanagementbecause of its faster, simpler and proven practices in administrativework. Journalsupport |Dissertation support| Analysis |Data collection| Coding& Algorithms |Editing &Peer- Reviewing

  10. Copyright©2022 PhdAssistance.Allrights reserved 4.Quicksolutionforproblems MIhelpstoresolveproblemsquicklywiththehelp of previous data. The MI prediction is based on outcomesofthepastresultsfromdata. It is best to deal with unbiased analysis of quantifiedfactorstogeneratethebestoutcome. Journalsupport |Dissertation support| Analysis |Data collection| Coding& Algorithms |Editing &Peer- Reviewing

  11. Copyright©2022 PhdAssistance.Allrights reserved InterpretationbasedonMachine Learning ML is a way of Programming with Artificial Intelligence.It replaces set rules of calculations with the program. With the given set of data, algorithmsstatistics,itcombinesandrepresents inamodelform. Thesemodelswillmakepredictionsbasedon theinputdata. Contd... Journalsupport |Dissertation support| Analysis |Data collection| Coding& Algorithms |Editing &Peer- Reviewing

  12. Copyright©2022 PhdAssistance.Allrights reserved It involves computer-aided modelling for supply chains. It is a process to enhance performanceandlimitriskswithconcrete predictions.WiththeHelpofData Collection, MI concludes with precise algorithms. MI is perfecttomanagethesupplychainanddeal withalltheriskinvolvedinit. Journalsupport |Dissertation support| Analysis |Data collection| Coding& Algorithms |Editing &Peer- Reviewing

  13. Copyright©2022 PhdAssistance.Allrights reserved FUTURERESEARCHTOPICS S.No 1 2 3 4 5 TypeofData Patients data Ontology database(risk hidden dangerdatabase) CloudDatabase BusinessData Datafrom physical sources (e.g. ERP, RFID, sensors) and cybersources (e.g. blockchain, supplier collaboration portals, andriskdata) Algorithm MachineLearning OntoLFR(Logistics Financial Risk Ontology + Apriori algorithm Block chain machine learning- basedfoodtraceabilitysystem Statistical approach for power control based upon multiple costing frameworks using a machine learning model (SCM– MLM) Digital supply chain twin – Industry4.0 Purpose To identifykey biomarkersto23predictthemortalityofindividualpatient To adapttothevariability,complexityandrelevanceofriskinearlywarningand pre-control. The blockchaindataflowisdesignedtoshowtheextensionof ML atthelevelof food traceability.Moreover,thereliable and accuratedataare usedin a supply chain to improve shelf life. To evaluateidlenessand createtechniquestooptimizetheprofitabilityofthe enterprise. The maximizationoftrade-offcapacityagainstorganizational performanceisdemonstratedanditis seentobeorganizationalinefficiencyby poweroptimization hasbeen validated Research and practice of SC risk management by enhancing predictive and reactive decisionstoutilizetheadvantagesofSCvisualization,historical disruptiondataanalysis,andreal-timedisruptiondataandensureend-to-end visibility and business continuity inglobal companies. References [1] [2] [3] [4] [5] Journalsupport |Dissertation support| Analysis |Data collection| Coding& Algorithms |Editing &Peer- Reviewing

  14. Copyright©2022 PhdAssistance.Allrights reserved Conclusion The efficiency level of the supply chain is crucial for businesses. Operating businesseswithtightprofitmarginsandwithcertainimprovementscanimpact theoverallprofitlineofthebusiness. MITechnologiesmakethejobsimpletodealwithvariouschallengesof forecastingandvolatilitydemandinvolvedinsupplychains. Moreover,itensuresefficiency,profitabilityandbettermanagementofthe supplychain. Journalsupport |Dissertation support| Analysis |Data collection| Coding& Algorithms |Editing &Peer- Reviewing

  15. Copyright©2022 PhdAssistance.Allrights reserved References Ivanov,D.,&Dolgui,A.(2020).Adigitalsupplychaintwinformanagingthedisruption risksandresilienceintheeraofIndustry4.0.ProductionPlanning&Control,1-14. Baryannis,G.,Dani,S.,&Antoniou, G. (2019).Predictingsupplychainrisksusingmachine learning: The trade-off between performance and interpretability. Future Generation ComputerSystems,101,993-1004. Asrol,M.,&Taira,E.(2021).RiskManagementforImprovingSupplyChainPerformance ofSugarcaneAgroindustry.IndustrialEngineering&ManagementSystems,20(1),9-26. Journalsupport |Dissertation support| Analysis |Data collection| Coding& Algorithms |Editing &Peer- Reviewing

  16. Copyright©2022 PhdAssistance.Allrights reserved Chowdhury,M.E.,Rahman,T.,Khandakar,A.,Al-Madeed,S.,Zughaier,S.M.,Doi,S.A.,…& Islam, M. T. (2021). An early warning tool for predicting mortality risk of COVID-19 patientsusingmachinelearning.CognitiveComputation,1-16. Yang,B.(2020).Constructionoflogisticsfinancialsecurityriskontologymodelbasedon riskassociationandmachinelearning.SafetyScience,123,104437. Shahbazi,Z.,&Byun,Y.C.(2021).AProcedureforTracingSupplyChainsforPerishable FoodBasedonBlockchain,MachineLearningandFuzzyLogic.Electronics,10(1),41. Wang, D., & Zhang, Y. (2020). Implications for sustainability in supply chain managementandthecirculareconomyusingmachinelearningmodel.Information Systemsande-BusinessManagement,1-13. Journalsupport |Dissertation support| Analysis |Data collection| Coding& Algorithms |Editing &Peer- Reviewing

  17. Copyright©2022 PhdAssistance.Allrights reserved ContactUs UK:+447537144372 INDIA:+91-9176966446 info@phdassistance.com Journalsupport |Dissertation support| Analysis |Data collection| Coding& Algorithms |Editing &Peer- Reviewing

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