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A study on supply chain investment decision-making and coordination in the Big Data environment

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  • Pan Liu

    (Chongqing University)

  • Shu-ping Yi

    (Chongqing University)

Abstract

In the Big Data environment, aims of enterprises investing in Big Data are to gain Big Data information (BDI). To study the decision-making issues of BDI investment and its effects on supply chain coordination, a supply chain with one retailer and one manufacturer was chosen. Meanwhile, considering a company owned the internal BDI and the external BDI, the market demand function was revised and four decision models were proposed from a new perspective. Then, the effects of BDI investment on supply chain members’ benefits under the four models were analyzed and an effectively coordination tactic was presented for achieving supply chain coordination. Results indicated when the investment cost could face a certain threshold, the retailer or the manufacturer investing in BDI could increase its benefits. Meanwhile, there existed “positive externalities” for other supply chain members. In addition, after supply chain members investing in BDI together, revenue-sharing contract could coordinate the supply chain effectively. This article provided a theoretical guidance or a decision basis for companies investing in BDI, meanwhile, it had reference values for supply chain coordination after investing in BDI.

Suggested Citation

  • Pan Liu & Shu-ping Yi, 2018. "A study on supply chain investment decision-making and coordination in the Big Data environment," Annals of Operations Research, Springer, vol. 270(1), pages 235-253, November.
    • Handle: RePEc:spr:annopr:v:270:y:2018:i:1:d:10.1007_s10479-017-2424-4
    DOI: 10.1007/s10479-017-2424-4
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    1. Wang, Gang & Gunasekaran, Angappa & Ngai, Eric W.T. & Papadopoulos, Thanos, 2016. "Big data analytics in logistics and supply chain management: Certain investigations for research and applications," International Journal of Production Economics, Elsevier, vol. 176(C), pages 98-110.
    2. Gunasekaran, Angappa & Papadopoulos, Thanos & Dubey, Rameshwar & Wamba, Samuel Fosso & Childe, Stephen J. & Hazen, Benjamin & Akter, Shahriar, 2017. "Big data and predictive analytics for supply chain and organizational performance," Journal of Business Research, Elsevier, vol. 70(C), pages 308-317.
    3. Fosso Wamba, Samuel & Akter, Shahriar & Edwards, Andrew & Chopin, Geoffrey & Gnanzou, Denis, 2015. "How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study," International Journal of Production Economics, Elsevier, vol. 165(C), pages 234-246.
    4. Prasanna Tambe, 2014. "Big Data Investment, Skills, and Firm Value," Management Science, INFORMS, vol. 60(6), pages 1452-1469, June.
    5. Stefan Voß, 2014. "Interview with Daniel Dolk and Christer Carlsson on “Decision Analytics”," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 6(3), pages 181-184, June.
    6. Wamba, Samuel Fosso & Gunasekaran, Angappa & Akter, Shahriar & Ren, Steven Ji-fan & Dubey, Rameshwar & Childe, Stephen J., 2017. "Big data analytics and firm performance: Effects of dynamic capabilities," Journal of Business Research, Elsevier, vol. 70(C), pages 356-365.
    7. Yang, Shuai & Hong, Ki-sung & Lee, Chulung, 2014. "Supply chain coordination with stock-dependent demand rate and credit incentives," International Journal of Production Economics, Elsevier, vol. 157(C), pages 105-111.
    8. Xiao, Tiaojun & Shi, Kuiran & Yang, Danqin, 2010. "Coordination of a supply chain with consumer return under demand uncertainty," International Journal of Production Economics, Elsevier, vol. 124(1), pages 171-180, March.
    9. Shibaji Panda, 2013. "Coordinating Two-Echelon Supply Chains Under Stock And Price Dependent Demand Rate," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 30(02), pages 1-20.
    10. 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.
    11. Z. Kevin Weng, 1995. "Channel Coordination and Quantity Discounts," Management Science, INFORMS, vol. 41(9), pages 1509-1522, September.
    12. Akter, Shahriar & Wamba, Samuel Fosso & Gunasekaran, Angappa & Dubey, Rameshwar & Childe, Stephen J., 2016. "How to improve firm performance using big data analytics capability and business strategy alignment?," International Journal of Production Economics, Elsevier, vol. 182(C), pages 113-131.
    13. Hsieh, Chung-Chi & Liu, Yu-Te & Wang, Wei-Ming, 2010. "Coordinating ordering and pricing decisions in a two-stage distribution system with price-sensitive demand through short-term discounting," European Journal of Operational Research, Elsevier, vol. 207(1), pages 142-151, November.
    14. 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.
    15. Hernandez, Manuel A., 2011. "Nonlinear pricing and competition intensity in a Hotelling-type model with discrete product and consumer types," Economics Letters, Elsevier, vol. 110(3), pages 174-177, March.
    16. Gérard P. Cachon & Martin A. Lariviere, 2005. "Supply Chain Coordination with Revenue-Sharing Contracts: Strengths and Limitations," Management Science, INFORMS, vol. 51(1), pages 30-44, January.
    17. Bongsug Kevin Chae & David L. Olson, 2013. "Business Analytics For Supply Chain: A Dynamic-Capabilities Framework," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 12(01), pages 9-26.
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    Cited by:

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    2. Liu Jiaguo & Zhang Huimin & Zhao Huida, 2021. "Blockchain Technology Investment and Sharing Strategy of Port Supply Chain Under Competitive Environment," Journal of Systems Science and Information, De Gruyter, vol. 9(3), pages 280-309, June.
    3. Seyyed-Mahdi Hosseini-Motlagh & Maryam Johari & Mohammadreza Nematollahi & Parvin Pazari, 2023. "Reverse supply chain management with dual channel and collection disruptions: supply chain coordination and game theory approaches," Annals of Operations Research, Springer, vol. 324(1), pages 215-248, May.
    4. 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.
    5. 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.

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