Big data analytics in supply chain management: A state-of-the-art literature review

被引：157

作者：

Truong Nguyen ^{[1
]}

Zhou, Li ^{[1
]}

Spiegler, Virginia ^{[2
]}

Ieromonachou, Petros ^{[1
]}

Lin, Yong ^{[1
]}

机构：

[1] Univ Greenwich, Fac Business, Connected Cities Res Grp, Syst Management & Strategy Dept, London, England

[2] Univ Kent, Kent Business Sch, Canterbury, Kent, England

来源：

COMPUTERS & OPERATIONS RESEARCH | 2018年 / 98卷

关键词：

Literature review; Big data; Big data analytics; Supply chain management; Research directions; MACHINE LEARNING APPROACH; DATA MINING APPROACH; PREDICTIVE ANALYTICS; STORAGE ASSIGNMENT; SOCIAL MEDIA; SYSTEM; NETWORK; OPERATIONS; FRAMEWORK; TIME;

D O I：

10.1016/j.cor.2017.07.004

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

The rapidly growing interest from both academics and practitioners in the application of big data analytics (BDA) in supply chain management (SCM) has urged the need for review of up-to-date research development in order to develop a new agenda. This review responds to the call by proposing a novel classification framework that provides a full picture of current literature on where and how BDA has been applied within the SCM context. The classification framework is structurally based on the content analysis method of Mayring (2008), addressing four research questions: (1) in what areas of SCM is BDA being applied? (2) At what level of analytics is BDA used in these SCM areas? (3) What types of BDA models are used in SCM? (4) What BDA techniques are employed to develop these models? The discussion tackling these four questions reveals a number of research gaps, which leads to future research directions. (C) 2017 Elsevier Ltd. All rights reserved.

引用

页码：254 / 264

页数：11

共 134 条

[1] Big data applications in operations/supply-chain management: A literature review [J].

Addo-Tenkorang, Richard ;

Helo, Petri T. .

COMPUTERS & INDUSTRIAL ENGINEERING, 2016, 101 :528-543

[2] Dealing with construction cost overruns using data mining [J].

Ahiaga-Dagbui, Dominic D. ;

Smith, Simon D. .

CONSTRUCTION MANAGEMENT AND ECONOMICS, 2014, 32 (7-8) :682-694

[3] Application and integration of an RFID-enabled warehousing management system - a feasibility study [J].

Alyahya, Saleh ;

Wang, Qian ;

Bennett, Nick .

JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION, 2016, 4 :15-25

[4]

[Anonymous], WHAT IS BIG DAT

[5]

[Anonymous], 2015, J. Big Data, DOI [DOI 10.1186/S40537-015-0015-2, 10.1186/s40537-015-0015-2]

[6]

[Anonymous], 2012, DIGITAL UNIVERSE 202

[7] Big Data computing and clouds: Trends and future directions [J].

Assuncao, Marcos D. ;

Calheiros, Rodrigo N. ;

Bianchi, Silvia ;

Netto, Marco A. S. ;

Buyya, Rajkumar .

JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING, 2015, 79-80 :3-15

[8] Big Data and virtualization for manufacturing cyber-physical systems: A survey of the current status and future outlook [J].

Babiceanu, Radu F. ;

Seker, Remzi .

COMPUTERS IN INDUSTRY, 2016, 81 :128-137

[9] Product development with data mining techniques: A case on design of digital camera [J].

Bae, Jae Kwon ;

Kim, Jinhwa .

EXPERT SYSTEMS WITH APPLICATIONS, 2011, 38 (08) :9274-9280

[10] Static and dynamic policies with RFID for the scheduling of retrieval and storage warehouse operations [J].

Ballestin, Francisco ;

Perez, Angeles ;

Lino, Pilar ;

Quintanilla, Sacramento ;

Valls, Vicente .

COMPUTERS & INDUSTRIAL ENGINEERING, 2013, 66 (04) :696-709

← 1 2 3 4 5 6 7 8 9 10 →