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Cited by 586

The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.

Big Data for supply chain management in the service and manufacturing sectors: Challenges, opportunities, and future perspectives

Zhong, Ray Y., Newman, Stephen T., Huang, George Q., Lan, Shulin·2016·Computers & Industrial Engineering
DOI 10.1016/j.cie.2016.07.013
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Citations

Cites

Show all 61 references

Research works printed but not linked: 26

  1. Artigues, 2015, Scientific big data visualization: A coupled tools approach, Supercomputing Frontiers and Innovations, 1, 4
  2. AustralianGovernment (2013). The Australian public service big data strategy (pp. 1–24).
  3. Crochet-Damais, 2013, La Poste: pour ne plus perdre de courrier, Journal Du Net
  4. Dimakopoulou, 2014, Applying real options to IT investment evaluation: The case of radio frequency identification (RFID) technology in the supply chain, International Journal of Production Economics, 156, 191, 10.1016/j.ijpe.2014.06.005 · doi:10.1016/j.ijpe.2014.06.005
  5. Eichengreen, 2013, The two waves of service-sector growth, Oxford Economic Papers, 65, 96, 10.1093/oep/gpr059 · doi:10.1093/oep/gpr059
  6. Fukase, 2013, Japan post prepares for IPO, Wall Street J.
  7. Gim, 2015, SmartCon: Smart Context Switching for Fast Storage IO Devices, ACM Transactions on Storage (TOS), 11, 5:1
  8. Harrison, 2012, Deal watch: ‘Big data’ deal for diabetes clinical trial modelling, Nature Reviews Drug Discovery, 11, 822, 10.1038/nrd3891 · doi:10.1038/nrd3891
  9. Hill, 2012, IBM smarter storage: What a smart idea, Mesabi Group Commentary, 1
  10. Jimenez, 2013, Asia/Pacific big data technology and services 2013–2017 analysis and forecast: the journey to tech+transformation continues. Next Stop Is Innovation, Market Analysis, 1
  11. Jin, 2015, A domain knowledge based method on active and focused information service for decision support within big data environment, Procedia Computer Science, 60, 93, 10.1016/j.procs.2015.08.108 · doi:10.1016/j.procs.2015.08.108
  12. Jitkajornwanich, K., Gupta, U., Elmasri, R., Fegaras, L., & McEnery, J. (2013). Using mapreduce to speed up storm identification from big raw rainfall data. In The fourth international conference on cloud computing, GRIDs, and virtualization (pp. 49–55).
  13. LaValle, 2013, Big data, analytics and the path from insights to value, MIT Sloan Management Review, 52, 21
  14. Martin, 2013, Big data in logistics a DHL perspective on how to move beyond the hype, DHL Customer Solutions & Innovation, 1
  15. NEC (2012). Case Study Nippon Express Co., Ltd. Empowered by Innovation NEC <http://www.nec.com/>, pp. 1–3.
  16. O’Connell, 2010, Interactive clinical data review for safety assessment and trial operations management, Phuse, 1
  17. O’Connell, 2013, Object storage systems: The underpinning of cloud and big data initiatives, SNIA Education, 1
  18. Qu, 2012, Semantic processing on big data, Vol. 2, 43
  19. Raman, A. C. (2014). Storage infrastructure for big data and cloud. Handbook of research on cloud infrastructures for big data analytics, p. 110. · doi:10.4018/978-1-4666-5864-6.ch005
  20. Sellars, 2013, Computational earth science: Big data transformed into insight, Eos, Transactions American Geophysical Union, 94, 277, 10.1002/2013EO320001 · doi:10.1002/2013eo320001
  21. Spoorthy, 2014, A survey on data storage and security in cloud computing, International Journal of Computer Science and Mobile Computing, 3, 306
  22. Syed, 2013, The future revolution on big data, International Journal of Advanced Research in Computer and Communication Engineering, 2, 2446
  23. Tripathy, 2016, Hadoop based uncertain possibilistic kernelized c-means algorithms for image segmentation and a comparative analysis, Applied Soft Computing, 46, 886, 10.1016/j.asoc.2016.01.045 · doi:10.1016/j.asoc.2016.01.045
  24. Tsuchiya, 2012, Big data processing in cloud environments, FUJITSU Science and Technology Journal, 48, 159
  25. Versace, 2012, The case for big data in the financial services industry, IDC Financial Insights, 1
  26. Wang, 2012, Faster, larger, easier: reining real-time big data processing in cloud, Vol. 4, 1

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Metadata

Authors
Zhong, R. Y., Newman, S. T., Huang, G. Q., & Lan, S.
Venue
Computers & Industrial Engineering
Publisher
Elsevier BV
Published
2016-11
Volume
101
Pages
572-591
ISSN
0360-8352 (ISSN-L)
DOI
10.1016/j.cie.2016.07.013
OpenAlex
W2470843486
Cited by
586
References
61
FWCI
100.35
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Funders
  • National Natural Science Foundation of China (51405307, 61473093, 61540030)
  • China Postdoctoral Science Foundation (2015M570720)

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CoChat. (2026). Citation check: Big Data for supply chain management in the service and manufacturing sectors: Challenges, opportunities, and future perspectives. https://app.cochat.ai/papers/10.1016/j.cie.2016.07.013
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Zhong, R. Y., Newman, S. T., Huang, G. Q., & Lan, S. (2016). Big Data for supply chain management in the service and manufacturing sectors: Challenges, opportunities, and future perspectives. Computers & Industrial Engineering. https://doi.org/10.1016/j.cie.2016.07.013

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.