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Big Data Driven Supply Chain Management and Business Administration

Author

Listed:
  • Lidong Wang
  • Cheryl Ann Alexander

Abstract

Big Data helps improve visibility throughout the supply chain, provides an integrated view of operational performance and customer interaction and gives businesses real-time insights that help make critical decisions. Big Data also has a potential to yield new management principles. This paper introduces the Big Data concept, its characteristics and some major issues of Big Data in supply chain management and business administration. These issues include supply chain and business data, Big Data benefits and its applications and opportunities. Methods and technology progress about Big Data are presented in this study. General challenges of Big Data and Big Data challenges in supply chain management and business administration are also discussed.

Suggested Citation

  • Lidong Wang & Cheryl Ann Alexander, 2015. "Big Data Driven Supply Chain Management and Business Administration," American Journal of Economics and Business Administration, Science Publications, vol. 7(2), pages 60-67, June.
    • Handle: RePEc:abk:jajeba:ajebasp.2015.60.67
    DOI: 10.3844/ajebasp.2015.60.67
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    References listed on IDEAS

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    1. Nada R. Sanders & Ram Ganeshan, 2015. "Special Issue of Production and Operations Management on “Big Data in Supply Chain Management”," Production and Operations Management, Production and Operations Management Society, vol. 24(8), pages 1371-1372, August.
    2. Hazen, Benjamin T. & Boone, Christopher A. & Ezell, Jeremy D. & Jones-Farmer, L. Allison, 2014. "Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications," International Journal of Production Economics, Elsevier, vol. 154(C), pages 72-80.
    3. Nada R. Sanders & Ram Ganeshan, 2015. "Special Issue of Production and Operations Management on “Big Data in Supply Chain Management”," Production and Operations Management, Production and Operations Management Society, vol. 24(10), pages 1671-1672, October.
    4. Nada R. Sanders & Ram Ganeshan, 2015. "Call for Papers: Special Issue of Production and Operations Management on Big Data in Supply Chain Management," Production and Operations Management, Production and Operations Management Society, vol. 24(2), pages 354-355, February.
    5. Nada R. Sanders & Ram Ganeshan, 2015. "Special Issue of Production and Operations Management on “Big Data in Supply Chain Management”," Production and Operations Management, Production and Operations Management Society, vol. 24(7), pages 1193-1194, July.
    6. Tan, Kim Hua & Zhan, YuanZhu & Ji, Guojun & Ye, Fei & Chang, Chingter, 2015. "Harvesting big data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph," International Journal of Production Economics, Elsevier, vol. 165(C), pages 223-233.
    7. Nada R. Sanders & Ram Ganeshan, 2015. "Call for Papers: Special Issue of Production and Operations Management on “Big Data in Supply Chain Management”," Production and Operations Management, Production and Operations Management Society, vol. 24(6), pages 1028-1029, June.
    8. Nada R. Sanders & Ram Ganeshan, 2015. "Special Issue of Production and Operations Management on “Big Data in Supply Chain Management”," Production and Operations Management, Production and Operations Management Society, vol. 24(9), pages 1509-1510, September.
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    Cited by:

    1. Gang Wang & Angappa Gunasekaran & Eric W. T. Ngai, 2018. "Distribution network design with big data: model and analysis," Annals of Operations Research, Springer, vol. 270(1), pages 539-551, November.
    2. Lei Xu & Runpeng Gao & Yu Xie & Peng Du, 2019. "To Be or Not to Be? Big Data Business Investment Decision-Making in the Supply Chain," Sustainability, MDPI, vol. 11(8), pages 1-14, April.
    3. Yaping Zhao & Zelong Yi, 2021. "Pricing of a Three-Stage Supply Chain with a Big Data Company," SN Operations Research Forum, Springer, vol. 2(4), pages 1-19, December.
    4. Zhan, Yuanzhu & Tan, Kim Hua, 2020. "An analytic infrastructure for harvesting big data to enhance supply chain performance," European Journal of Operational Research, Elsevier, vol. 281(3), pages 559-574.
    5. Dong-Hui Jin & Hyun-Jung Kim, 2018. "Integrated Understanding of Big Data, Big Data Analysis, and Business Intelligence: A Case Study of Logistics," Sustainability, MDPI, vol. 10(10), pages 1-15, October.

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