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Pricing of a Three-Stage Supply Chain with a Big Data Company

Author

Listed:
  • Yaping Zhao

    (Shenzhen University)

  • Zelong Yi

    (Shenzhen University)

Abstract

As Big Data is crucial to the competition and growth of companies, this paper considers a multi-stage supply chain with one Big Data company, multiple manufacturers, and one retailer. Demands of different products are dependent on the price and volume of each other, and such information can be obtained by the Big Data company at a cost. Manufacturers will purchase the demand information from the Big Data company and produce and sell products to the retailer. We intend to examine interactions among these firms. Specifically, various power structures are considered, and the equilibrium pricing decisions are analyzed and expressed explicitly through theoretical study. Effects of parameters under different power structures are explored and compared with each other through both special case studies and numerical experiments. This study enables the derivation of managerial insights related to Big Data that are useful for practical applications.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:snopef:v:2:y:2021:i:4:d:10.1007_s43069-021-00078-9
    DOI: 10.1007/s43069-021-00078-9
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    References listed on IDEAS

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    1. 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.
    2. Morgan, C. & Dewhurst, A., 2008. "Multiple retailer supplier performance: An exploratory investigation into using SPC techniques," International Journal of Production Economics, Elsevier, vol. 111(1), pages 13-26, January.
    3. Fernando Bernstein & Awi Federgruen, 2004. "A General Equilibrium Model for Industries with Price and Service Competition," Operations Research, INFORMS, vol. 52(6), pages 868-886, December.
    4. Pan Liu, 2017. "Pricing Strategies of a Three-Stage Supply Chain: A New Research in the Big Data Era," Discrete Dynamics in Nature and Society, Hindawi, vol. 2017, pages 1-16, October.
    5. 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.
    6. Jean Tirole, 1988. "The Theory of Industrial Organization," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262200716, April.
    7. Opresnik, David & Taisch, Marco, 2015. "The value of Big Data in servitization," International Journal of Production Economics, Elsevier, vol. 165(C), pages 174-184.
    8. Kyle Cattani & Wendell Gilland & Hans Sebastian Heese & Jayashankar Swaminathan, 2006. "Boiling Frogs: Pricing Strategies for a Manufacturer Adding a Direct Channel that Competes with the Traditional Channel," Production and Operations Management, Production and Operations Management Society, vol. 15(1), pages 40-56, March.
    9. Yi, Zelong & Li, Fan & Ma, Lijun, 2019. "The impact of distribution channels on trial-version provision with a positive network effect," Omega, Elsevier, vol. 85(C), pages 115-133.
    10. Pan Liu, 2019. "Decision-Making and Coordination of a Three-Stage Book Supply Chain considering Big Data Information Cost," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-14, May.
    11. Amy David & Elodie Adida, 2015. "Competition and Coordination in a Two-Channel Supply Chain," Production and Operations Management, Production and Operations Management Society, vol. 24(8), pages 1358-1370, August.
    12. 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.
    13. Wei-yu Kevin Chiang & Dilip Chhajed & James D. Hess, 2003. "Direct Marketing, Indirect Profits: A Strategic Analysis of Dual-Channel Supply-Chain Design," Management Science, INFORMS, vol. 49(1), pages 1-20, January.
    14. Pan Liu & Shu-ping Yi, 2016. "Investment Decision-Making and Coordination of Supply Chain: A New Research in the Big Data Era," Discrete Dynamics in Nature and Society, Hindawi, vol. 2016, pages 1-10, May.
    15. Pan Liu & Shu-ping Yi, 2018. "Investment decision-making and coordination of a three-stage supply chain considering Data Company in the Big Data era," Annals of Operations Research, Springer, vol. 270(1), pages 255-271, November.
    16. 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.
    17. Erik Hofmann, 2017. "Big data and supply chain decisions: the impact of volume, variety and velocity properties on the bullwhip effect," International Journal of Production Research, Taylor & Francis Journals, vol. 55(17), pages 5108-5126, September.
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