IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i8p2298-d223479.html
   My bibliography  Save this article

To Be or Not to Be? Big Data Business Investment Decision-Making in the Supply Chain

Author

Listed:
  • Lei Xu

    (School of Management, Tianjin University of Technology, Tianjin 300384, China)

  • Runpeng Gao

    (School of Management, Tianjin University of Technology, Tianjin 300384, China)

  • Yu Xie

    (School of Management, Tianjin University of Technology, Tianjin 300384, China)

  • Peng Du

    (School of Management, Tianjin University of Technology, Tianjin 300384, China)

Abstract

The development of Big Data technology initiates an emerging research question of whether and how to invest in Big Data business for supply chain members to establish sustainable competitive edge. The aim of our study was to assess investment in Big Data business and its sustainable effects on supply chain coordination. We considered a two-stage supply chain with one supplier and one retailer who may or may not invest in Big Data business. Five decision-making modes were proposed based on the investment portfolios. The impacts of Big Data business on the profit of the supply chain and its members were analyzed and it was confirmed that a coordination scheme could achieve supply chain coordination. The results indicated that when the Big Data cost met a certain threshold, the profit of the supply chain and its members would increase whether supply chain members choose to invest in Big Data business individually or jointly. A reasonable cost allocation of Big Data business between supply chain members was provided when both members invest in Big Data. In addition, after the members invested jointly, a revenue-sharing contract could be applied to perfectly coordinate the supply chain.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:8:p:2298-:d:223479
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/8/2298/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/8/2298/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    3. Xu, Lei & Li, Yongjian & Govindan, Kannan & Xu, Xiaolin, 2015. "Consumer returns policies with endogenous deadline and supply chain coordination," European Journal of Operational Research, Elsevier, vol. 242(1), pages 88-99.
    4. 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.
    5. Lei Li & Ting Chi & Tongtong Hao & Tao Yu, 2018. "Customer demand analysis of the electronic commerce supply chain using Big Data," Annals of Operations Research, Springer, vol. 268(1), pages 113-128, September.
    6. Arunachalam, Deepak & Kumar, Niraj & Kawalek, John Paul, 2018. "Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 416-436.
    7. Samayita Guha & Subodha Kumar, 2018. "Emergence of Big Data Research in Operations Management, Information Systems, and Healthcare: Past Contributions and Future Roadmap," Production and Operations Management, Production and Operations Management Society, vol. 27(9), pages 1724-1735, September.
    8. Li, Jianli & Liu, Liwen, 2006. "Supply chain coordination with quantity discount policy," International Journal of Production Economics, Elsevier, vol. 101(1), pages 89-98, May.
    9. Hau L. Lee, 2018. "Big Data and the Innovation Cycle," Production and Operations Management, Production and Operations Management Society, vol. 27(9), pages 1642-1646, September.
    10. Barry Alan Pasternack, 1985. "Optimal Pricing and Return Policies for Perishable Commodities," Marketing Science, INFORMS, vol. 4(2), pages 166-176.
    11. 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.
    12. Saha, Subrata, 2013. "Supply chain coordination through rebate induced contracts," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 50(C), pages 120-137.
    13. 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.
    14. 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.
    15. Yanping Cheng & Yunjuan Kuang & Xiutian Shi & Ciwei Dong, 2018. "Sustainable Investment in a Supply Chain in the Big Data Era: An Information Updating Approach," Sustainability, MDPI, vol. 10(2), pages 1-18, February.
    16. 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.
    17. Dmitry Ivanov & Alexander Tsipoulanidis & Jörn Schönberger, 2019. "Global Supply Chain and Operations Management," Springer Texts in Business and Economics, Springer, edition 2, number 978-3-319-94313-8, October.
    18. Choi, Tsan-Ming & Li, Yongjian & Xu, Lei, 2013. "Channel leadership, performance and coordination in closed loop supply chains," International Journal of Production Economics, Elsevier, vol. 146(1), pages 371-380.
    19. Qi Feng & J. George Shanthikumar, 2018. "How Research in Production and Operations Management May Evolve in the Era of Big Data," Production and Operations Management, Production and Operations Management Society, vol. 27(9), pages 1670-1684, September.
    20. 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.
    21. Xin Chen & Chung‐Lun Li & Byong‐Duk Rhee & David Simchi‐Levi, 2007. "The impact of manufacturer rebates on supply chain profits," Naval Research Logistics (NRL), John Wiley & Sons, vol. 54(6), pages 667-680, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ionica Oncioiu & Ovidiu Constantin Bunget & Mirela Cătălina Türkeș & Sorinel Căpușneanu & Dan Ioan Topor & Attila Szora Tamaș & Ileana-Sorina Rakoș & Mihaela Ștefan Hint, 2019. "The Impact of Big Data Analytics on Company Performance in Supply Chain Management," Sustainability, MDPI, vol. 11(18), pages 1-22, September.
    2. Shengbin Hao & Haili Zhang & Michael Song, 2019. "Big Data, Big Data Analytics Capability, and Sustainable Innovation Performance," Sustainability, MDPI, vol. 11(24), pages 1-15, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Acciarini, Chiara & Cappa, Francesco & Boccardelli, Paolo & Oriani, Raffaele, 2023. "How can organizations leverage big data to innovate their business models? A systematic literature review," Technovation, Elsevier, vol. 123(C).
    2. B.C. Giri & S. Bardhan & T. Maiti, 2016. "Coordinating a three-layer supply chain with uncertain demand and random yield," International Journal of Production Research, Taylor & Francis Journals, vol. 54(8), pages 2499-2518, April.
    3. 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.
    4. Subrata Saha, 2016. "Channel characteristics and coordination in three-echelon dual-channel supply chain," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(3), pages 740-754, February.
    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.
    6. Wilkin, Carla & Ferreira, Aldónio & Rotaru, Kristian & Gaerlan, Luigi Red, 2020. "Big data prioritization in SCM decision-making: Its role and performance implications," International Journal of Accounting Information Systems, Elsevier, vol. 38(C).
    7. Neda Dabaghian & Reza Tavakkoli-Moghaddam & Ata Allah Taleizadeh & Mohammad Sadegh Moshtagh, 2022. "Channel coordination and profit distribution in a three-echelon supply chain considering social responsibility and product returns," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(3), pages 3165-3197, March.
    8. Cao, Zong-Hong & Zhou, Yong-Wu & Zhao, Ju & Li, Chang-Wen, 2015. "Entry mode selection and its impact on an incumbent supply chain coordination," Journal of Retailing and Consumer Services, Elsevier, vol. 26(C), pages 1-13.
    9. Michael Krapp & Johannes B. Kraus, 2019. "Coordination contracts for reverse supply chains: a state-of-the-art review," Journal of Business Economics, Springer, vol. 89(7), pages 747-792, September.
    10. Roßmann, Bernhard & Canzaniello, Angelo & von der Gracht, Heiko & Hartmann, Evi, 2018. "The future and social impact of Big Data Analytics in Supply Chain Management: Results from a Delphi study," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 135-149.
    11. Patrucco, Andrea S. & Marzi, Giacomo & Trabucchi, Daniel, 2023. "The role of absorptive capacity and big data analytics in strategic purchasing and supply chain management decisions," Technovation, Elsevier, vol. 126(C).
    12. Xingzheng Ai & Jing Chen & Jianhua Ma, 2012. "Contracting with demand uncertainty under supply chain competition," Annals of Operations Research, Springer, vol. 201(1), pages 17-38, December.
    13. Vicky Ching Gu & Bin Zhou & Qing Cao & Jeffery Adams, 2021. "Exploring the relationship between supplier development, big data analytics capability, and firm performance," Annals of Operations Research, Springer, vol. 302(1), pages 151-172, July.
    14. S. Vijayakumar Bharathi, 2017. "Prioritizing and Ranking the Big Data Information Security Risk Spectrum," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 18(3), pages 183-201, September.
    15. Dubey, Rameshwar & Gunasekaran, Angappa & Childe, Stephen J. & Papadopoulos, Thanos & Luo, Zongwei & Wamba, Samuel Fosso & Roubaud, David, 2019. "Can big data and predictive analytics improve social and environmental sustainability?," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 534-545.
    16. Biman Darshana Hettiarachchi & Stefan Seuring & Marcus Brandenburg, 2022. "Industry 4.0-driven operations and supply chains for the circular economy: a bibliometric analysis," Operations Management Research, Springer, vol. 15(3), pages 858-878, December.
    17. Chen, Kebing & Xiao, Tiaojun, 2015. "Outsourcing strategy and production disruption of supply chain with demand and capacity allocation uncertainties," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 243-257.
    18. Brinch, Morten & Gunasekaran, Angappa & Fosso Wamba, Samuel, 2021. "Firm-level capabilities towards big data value creation," Journal of Business Research, Elsevier, vol. 131(C), pages 539-548.
    19. Syed Asif Raza, 2022. "A bibliometric analysis of pricing models in supply chain," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 21(2), pages 228-251, April.
    20. Peng Liang & Hasan Cavusoglu & Nan Hu, 2023. "Customers’ managerial expectations and suppliers’ asymmetric cost management," Production and Operations Management, Production and Operations Management Society, vol. 32(6), pages 1975-1993, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:11:y:2019:i:8:p:2298-:d:223479. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.