Recent Developments in Data Science and Business Analytics

494 Pages·2018·11.73 MB·English

by  Madjid Tavana

Springer Proceedings in Business and Economics
Madjid Tavana
Srikanta Patnaik Editors
Recent
Developments in
Data Science and
Business Analytics
Proceedings of the International
Conference on Data Science and
Business Analytics (ICDSBA- 2017)
Springer Proceedings in Business and Economics
Moreinformationaboutthisseriesathttp://www.springer.com/series/11960
(cid:129)
Madjid Tavana Srikanta Patnaik
Editors
Recent Developments in Data
Science and Business
Analytics
Proceedings of the International Conference
on Data Science and Business Analytics
(ICDSBA- 2017)
Editors
MadjidTavana SrikantaPatnaik
DepartmentofBusinessSystems DepartmentofComputerScience
andAnalytics andEngineering
LaSalleUniversity SOAUniversity
Philadelphia,PA,USA Bhubaneswar,Odisha,India
ISSN2198-7246 ISSN2198-7254 (electronic)
SpringerProceedingsinBusinessandEconomics
ISBN978-3-319-72744-8 ISBN978-3-319-72745-5 (eBook)
https://doi.org/10.1007/978-3-319-72745-5
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Preface
Weliveinadata-drivensociety.Thesearchforinnovativesolutionsandcompetitive
advantagehasledtodigitalrevolutionwhereahugeamountofdataisproducedevery
day. Today’s organizations are facing new challenges in processing and analyzing
this massive data, collected from different sources, containing inconsistent, incor-
rect,inadequate,andmisleadinginformation.Thesedatabasesinvolveagreatdealof
complexitiesduetothevariationsinsourcesofdata(suchasmixtureofmedia:text,
graphics, audio, video) and velocity at which real-time data is being generated,
collected, and processed. New data analytics patterns and trends are needed to
analyze this enormous volume of structured and unstructured data. Big Data is a
massivecollectionofdatacontainingplentifulinformation,whichisbroadlydefined
by five dimensions: (i) volume: referring to the large size of the data; (ii) variety:
representingunstructureddatafromheterogeneoussourcessuchastraditionaldata-
bases, transactional systems, Internet, and social media; (iii) velocity: the speed at
whichnewdataisgeneratedandanalyzedinrealtime;(iv)veracity:theauthenticity
ororiginofthedata;and(v)value:thevalueofdatawhichdependsonwhetherthe
datacollectedisoldorrecent.
OneofthemajorproblemsencounteredduringtheuseofBigDatainindustrial
processes and applications is the hardware and software requirements to deal with
such a huge amount of data from varying sources. Although the development of
advanced technologies such as cloud computing, virtualization of processes and
storages, new programming and data processing frameworks like MapReduce and
Hadoop have been successful to some extent, still complex industrial applications
need more improved technologies to deal with real-time requirements. In addition,
theinformationabouttheauthenticityandoriginofthedatacollectedismandatory
asinaccurateanderroneousdatamayresultininadequateandirrelevantinformation
thushamperingpredictionaccuracy.Thesechallengeshavecreatedawiderangeof
researchopportunitiesincluding:
1. AlgorithmsforanalyzingandoptimizingBigData
2. Alternativesolutionsforstoring,accessing,andtransmittingBigData
v
vi Preface
3. Various communication techniques for fast processing and transmission of
BigData
4. Innovativesolutionsforcontrollingandmonitoringmachineriesandprocesses
5. Data-drivensolutionsforsupplychainmanagementandlogisticsystems
6. Innovativesoftwareforremotedistributedapplications
AlthoughagreatdealofresearchisdevotedtotherealizationofsmartIndustry,
synergetic study of Big Data has not yet peaked up momentum. This conference
provided a platform for discussion of various challenges and issues relating to Big
Dataandbusinessanalytics.Thisvolumecontains55researcharticlesfromdiverse
areasofdatascienceandbusinessanalyticscategorizedintofivesections:
(i) Marketing and Supply Chain Analytics: focuses on the integration of hori-
zontal processes related to all value chain partners from various suppliers of
raw materials to intermediate suppliers to final customers using tracking and
tracingsystems.Thereareeightresearcharticlesinthissection.
(ii) LogisticsandOperationsAnalytics:focusesonthedigitalization,integration,
and efficient management of various processesacross the vertical value chain
such as designing and planning of product, purchasing of raw materials,
development of products, manufacturing of products, and logistics. There are
12researcharticlesinthissection.
(iii) Financial Analytics: focuses on improving the financial health of the busi-
nessesandeconomy.Thereare11contributionsinthissection.
(iv) PredictiveModelingandDataAnalytics:focusesonpredictivemodelingand
digital solutions for integrated and data-driven platforms. These solutions are
based upon optimization of customer access and interaction for generating
additional revenue using predictive data analysis and modeling. There are
14researcharticlesinthissection.
(v) Communications and Information Systems Analytics: focuses on smart
factories and autonomous systems and intelligent equipment and machineries
embedded with sensors and actuators and establishes the concept of smart
manufacturing processes. High-level automation results in self-adaption and
self-optimizationofmanufacturingprocessesfromacquisitionofrawmaterials
toproductionof final products. Automatic data exchange andcommunication
betweenmachineries,infrastructuresandhumanagents,andcompleteautoma-
tion of machineries and processesare also a branch of this domain. There are
10researcharticlesinthissection.
Philadelphia,PA,USA MadjidTavana
Bhubaneswar,Odisha,India SrikantaPatnaik
Acknowledgments
The research articles included in this proceeding are the selected collection of the
contributionsmadetotheICDSBA-2017.Wearegratefultoallcontributorsandthe
EditorialDirectoroftheSpringerBookserieson“SpringerProceedingsinBusiness
and Economics,” Nicholas Philipson for his support to bring out the conference
proceedingsofICDSBA-2017.WearealsothankfultoProf.Dr.QinXin,professor
of Information and Communication Technology in the Department of Science and
TechnologyattheUniversityoftheFaroeIslands,FaroeIslands(Denmark),forhis
keynoteaddress.
The organizing committee is most grateful to Prof. Dr. Xilong Qu, the dean of
SchoolofInformationTechnologyandManagement,HunanUniversityofFinance
and Economics, who is also the master degree student advisor for both Xiangtan
UniversityandHunanInstituteofEngineering.Weoweourhighestgratitudeto:
(cid:129) HunanUniversityofFinanceandEconomics
(cid:129) HunanInstituteofEngineering
(cid:129) HunanAssociationforScienceandTechnology
(cid:129) FinancialBigDataScienceandTechnologyKeyLaboratoryofHunanProvince
(cid:129) Hunan Provincial 2011 Collaborative Innovation Center for Development and
UtilizationofFinanceandEconomicsBigDataProperty
(cid:129) Hunan Province Higher Educational Institutions Key Laboratory “Information
TechnologyandInformationSecurity”
We look forward to the second International Conference on Data Science and
BusinessAnalytics(ICDSBA2018)tobeheldinChangsha,China.
vii
Contents
PartI MarketingandSupplyChainAnalytics
1 ResearchonDifferentialPricingandCoordinationMechanism
ofSecond-ClassSupplyChainofNewProductsandRemanufactured
Products. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
HubiaoLi
2 AStudyonCooperationStrategiesofDualChannelSupply
ChainBasedonServiceLevel. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
GuojunJiandXiLiu
3 TheQualityManagementofFoodSupplyChaininPerspective
ofFoodSafety. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
GangLiuandLiGuo
4 StrategicCustomerBehaviorwithRiskPreference
foraSupplyChainManagementBasedonDoubleChannel. . . . . . 37
WanjinHouandQuanshengLei
5 CompetitionandCoordinationinSingle-Supplier
Multiple-RetailerSupplyChain. . . . . . . . .. . . . . . . . . . . . . . . .. . . 45
JiahangDuandQuanshengLei
6 ResearchontheConstructionofEnterpriseBrand
CompetitivenessEvaluationSystemBasedontheIntegration
ofSWOTandAHPModel. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
MudanTaoandZhiLi
7 ReflectionsontheTrainingModeofE-CommerceProfessionals
withImprovedPracticalExercisesandInnovativeAbility. . . . . . . 63
ShunminWang
ix
x Contents
8 ResearchonEvaluatingMarketingAbilityofTraditional
ChineseMedicineEnterprisesinGansuProvince. . . . . . . . . . . . . . 71
LixinYun,ZhonghuaLuo,andMingweiWu
PartII LogisticsandOperationsAnalytics
9 TheImpactoftheRelationshipBetweenOperational
CostandOilPricesonEconomicAssessment
inOilandGasIndustry. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
LihuiZhu,DongkunLuo,XiaoyuWang,andRuiGuo
10 TheConstructionofUniversityStudents’Entrepreneurship
CompetencyModelinApplication-OrientedUniversities. . . . . . . . 91
ChunxiaoChenandJinjianWang
11 ImprovingAirportSecurityScreeningSysteminTerms
ofEfficiencyandFairnessViaNetworkModel. . . . . . . . . . . . . . . . 103
TanLiming,CaiXiaohang,andHeYuandi
12 StudyontheChoiceofStrategicEmergingIndustries
inGansuProvinceBasedonMulti-levelGreyModel. . . . . . . . . . . 113
ZhonghuaLuo,QiMen,andLixinYun
13 TheStudyof“BigQuality”SatisfactionEvaluation. . . . . . . . . . . . 123
LizhongTongandTaoyuJia
14 ResearchonEvaluationIndexSystemofEnterpriseBrand
Competitiveness:TakingLiquorIndustryasanExample. . . . . . . . 131
YanFanandLizhongTong
15 SimulationofStochasticVolatilityVarianceSwap. . . . . . . . . . . . . 139
ShicanLiu,YanliZhou,YonghongWu,andXiangyuGe
16 EmpiricalResearchoftheContributionRate
ofUniversityScienceandTechnologytotheRegional
EconomicDevelopment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
TianhangWangandFenggeYao
17 ResearchonIncentiveStrategyofLogisticsOutsourcingAbout
ManufacturingEnterprises. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157
BoYangandWenwenJin
18 ResearchonExternalQualityInspectionTechnologyofTropical
FruitsBasedonComputerVision. . . . . . . . . . . . . . . . . . . . . . . . . . 165
KunZhang,XiaoyanChen,andHaifengWang
19 WholesalePriceContractandQuantityDiscountContract
UnderCompetitionwithVariousGames. . . . . . . . . . . . . . . . . . . . . 175
QinJiabao,LeiQuansheng,andHeYezi