Big data supply chain analytics: ethical, privacy and security challenges posed to business, industries and society

被引:73
|
作者
Ogbuke, Nnamdi Johnson [1 ]
Yusuf, Yahaya Y. [1 ]
Dharma, Kovvuri [1 ]
Mercangoz, Burcu A. [2 ]
机构
[1] Univ Cent Lancashire, Lancashire Sch Business & Enterprise, Preston, Lancs, England
[2] Istanbul Univ, Fac Transportat & Logist, Istanbul, Turkey
关键词
Big data; business analytics; Industry; 4; 0; ethical issues; supply chain management; CYBER-SECURITY; SMART CITIES; PERFORMANCE; OPPORTUNITIES; MANAGEMENT; IMPACT; INFORMATION; INNOVATION; LOGISTICS; INTERNET;
D O I
10.1080/09537287.2020.1810764
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
This study conducted a comprehensive review of big data supply chain analytics (BDSCA). The paper explored the application of big data in supply chain management and its benefits for organisations and society. The paper also examined the ethical, security, privacy and operational challenges of big data techniques, as well as the potential reputational damages to businesses. The review outlined four principal facets, namely: Big data analytics, applications, ethics and privacy issues, and how organizations employed this emerging tool to anticipate and even predict the future and direct their operations. These principle facets are built across the multiple levels and unique conceptual standpoints indicated by 7 themes and 14 sub-themes. These themes were generated based on 120 articles (2005-2020) drawn mainly from leading academic journals. Overall, there is a considerable consensus across current literature that big data analytics extend far beyond just reinventing the supply chain. It has the potential to support more responsive next-generation of global companies who are operating in an increasingly challenging and uncertain environment.
引用
收藏
页码:123 / 137
页数:15
相关论文
共 50 条
  • [41] Challenges with big data analytics in service supply chains in the UAE
    Khan, Mehmood
    MANAGEMENT DECISION, 2019, 57 (08) : 2124 - 2147
  • [42] A note on big data analytics capability development in supply chain
    Jha, Ashish Kumar
    Agi, Maher A. N.
    Ngai, Eric W. T.
    DECISION SUPPORT SYSTEMS, 2020, 138
  • [43] Big Data Analytics in Supply Chain Management: A Qualitative Study
    Aljabhan, Basim
    Abeyie, Melese
    COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2022, 2022
  • [44] Big Data Analytics on The Supply Chain Management: A Significant Impact
    Handanga, Suilety
    Bernardino, Jorge
    Pedrosa, Isabel
    PROCEEDINGS OF 2021 16TH IBERIAN CONFERENCE ON INFORMATION SYSTEMS AND TECHNOLOGIES (CISTI'2021), 2021,
  • [45] Big data analytics in supply chain and logistics: an empirical approach
    Queiroz, Maciel Manoel
    Telles, Renato
    INTERNATIONAL JOURNAL OF LOGISTICS MANAGEMENT, 2018, 29 (02) : 767 - 783
  • [46] Big data and predictive analytics for supply chain and organizational performance
    Gunasekaran, Angappa
    Papadopoulos, Thanos
    Dubey, Rameshwar
    Wamba, Samuel Fosso
    Childe, Stephen J.
    Hazen, Benjamin
    Akter, Shahriar
    JOURNAL OF BUSINESS RESEARCH, 2017, 70 : 308 - 317
  • [47] Big data and predictive analytics applications in supply chain management
    Gunasekaran, Angappa
    Tiwari, Manoj Kumar
    Dubey, Rameshwar
    Wamba, Samuel Fosso
    COMPUTERS & INDUSTRIAL ENGINEERING, 2016, 101 : 525 - 527
  • [48] Big data analytics and application for logistics and supply chain management
    Govindan, Kannan
    Cheng, T. C. E.
    Mishra, Nishikant
    Shukla, Nagesh
    TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW, 2018, 114 : 343 - 349
  • [49] An Analytical Study on Big Data Management for Supply Chain Analytics
    Kumar, Sundeep
    Rathore, Vikram Singh
    Mathur, Alok
    RECENT ADVANCES IN INDUSTRIAL PRODUCTION, ICEM 2020, 2022, : 333 - 341
  • [50] Improving Supply Chain Security Using Big Data
    Zage, David
    Glass, Kristin
    Colbaugh, Richard
    2013 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENCE AND SECURITY INFORMATICS: BIG DATA, EMERGENT THREATS, AND DECISION-MAKING IN SECURITY INFORMATICS, 2013, : 254 - 259