IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i3p438-d1329162.html
   My bibliography  Save this article

Research on Decision Analysis with CVaR for Supply Chain Finance Based on Blockchain Technology

Author

Listed:
  • Shujian Ma

    (School of Economics & Management, Nanjing Tech University, Nanjing 211816, China
    Institute of Block Chain and Complex Systems, Nanjing Tech University, Nanjing 211816, China
    School of Mathematical and Physical Sciences, Nanjing Tech University, Nanjing 211816, China)

  • Jilong Cai

    (Institute of Block Chain and Complex Systems, Nanjing Tech University, Nanjing 211816, China
    School of Mathematical and Physical Sciences, Nanjing Tech University, Nanjing 211816, China)

  • Gang Wang

    (School of Mathematical and Physical Sciences, Nanjing Tech University, Nanjing 211816, China)

  • Xiangxiang Ge

    (Institute of Block Chain and Complex Systems, Nanjing Tech University, Nanjing 211816, China
    School of Mathematical and Physical Sciences, Nanjing Tech University, Nanjing 211816, China)

  • Ying Teng

    (School of Economics & Management, Nanjing Tech University, Nanjing 211816, China
    Institute of Block Chain and Complex Systems, Nanjing Tech University, Nanjing 211816, China)

  • Hua Jiang

    (School of Mathematical and Physical Sciences, Nanjing Tech University, Nanjing 211816, China)

Abstract

The application of blockchain has become a trend in the development of supply chain finance. Aiming to bridge the gap in the existing literature, this paper investigates a supply chain finance system based on blockchain technology which contains a manufacturer, a retailer and a financial institution and incorporates blockchain costs into the model. Firstly, this paper establishes a supply chain finance model based on blockchain technology and it presents a comparison with the process employed under the traditional model. Secondly, this paper establishes the revenue mathematical model of supply chain finance based on blockchain technology. Thirdly, the optimal decisions of each participant under centralized and decentralized decision-making are proved and obtained, respectively, and the influencing factors of the optimal decisions are analyzed. Finally, the conclusions are verified via simulations. This study finds that, when blockchain is used, the benefits of each participant in the chain are increased. In addition, centralized decision-making, which is more optimal in the traditional model, is also enhanced under blockchain. This paper demonstrates the superiority of blockchain-enabled supply chain finance in terms of model and revenue. This provides some suggestions for companies in the supply chain with regard to solving the problem of financing difficulties.

Suggested Citation

  • Shujian Ma & Jilong Cai & Gang Wang & Xiangxiang Ge & Ying Teng & Hua Jiang, 2024. "Research on Decision Analysis with CVaR for Supply Chain Finance Based on Blockchain Technology," Mathematics, MDPI, vol. 12(3), pages 1-25, January.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:3:p:438-:d:1329162
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/3/438/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/12/3/438/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Víctor M. Adame & Fernando Fernández-Rodríguez & Simon Sosvilla-Rivero, 2016. "Portfolios in the Ibex 35 before and after the Global Financial Crisis," Applied Economics, Taylor & Francis Journals, vol. 48(40), pages 3826-3847, August.
    2. Chen, Jianxin & Zhang, Tonghua & Zhou, Yongwu, 2020. "Dynamics of a risk-averse newsvendor model with continuous-time delay in supply chain financing," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 169(C), pages 133-148.
    3. Lu Liu & Yongjian Li & Tao Jiang, 2023. "Optimal strategies for financing a three-level supply chain through blockchain platform finance," International Journal of Production Research, Taylor & Francis Journals, vol. 61(11), pages 3564-3581, June.
    4. Lili Xu & Yubin Yang & Xuejian Chu, 2021. "Research on the Influence Mechanism of Block Chain on the Credit of Transportation Capacity Supply Chain Finance," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-11, November.
    5. Frank Fabozzi & Dashan Huang & Guofu Zhou, 2010. "Robust portfolios: contributions from operations research and finance," Annals of Operations Research, Springer, vol. 176(1), pages 191-220, April.
    6. Jena, Sarat Kumar & Padhi, Sidhartha S & Cheng, T.C.E., 2023. "Optimal selection of supply chain financing programmes for a financially distressed manufacturer," European Journal of Operational Research, Elsevier, vol. 306(1), pages 457-477.
    7. Xinsheng, Xu & Zhiqing, Meng & Rui, Shen & Min, Jiang & Ping, Ji, 2015. "Optimal decisions for the loss-averse newsvendor problem under CVaR," International Journal of Production Economics, Elsevier, vol. 164(C), pages 146-159.
    8. Luca Mattia Gelsomino & Saskia Sardesai & Miia Pirttilä & Michael Henke, 2023. "Addressing the relation between transparency and supply chain finance schemes," International Journal of Production Research, Taylor & Francis Journals, vol. 61(17), pages 5806-5821, September.
    9. Shifeng Han & Xingzhong Xu, 2018. "NEV supply chain coordination and sustainability considering sales effort and risk aversion under the CVaR criterion," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-39, June.
    Full references (including those not matched with items on IDEAS)

    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. Jang Ho Kim & Yongjae Lee & Woo Chang Kim & Frank J. Fabozzi, 2022. "Goal-based investing based on multi-stage robust portfolio optimization," Annals of Operations Research, Springer, vol. 313(2), pages 1141-1158, June.
    2. Maillet, Bertrand & Tokpavi, Sessi & Vaucher, Benoit, 2015. "Global minimum variance portfolio optimisation under some model risk: A robust regression-based approach," European Journal of Operational Research, Elsevier, vol. 244(1), pages 289-299.
    3. Juan F. Monge & Mercedes Landete & Jos'e L. Ruiz, 2016. "Sharpe portfolio using a cross-efficiency evaluation," Papers 1610.00937, arXiv.org, revised Oct 2016.
    4. Taras Bodnar & Yarema Okhrin & Valdemar Vitlinskyy & Taras Zabolotskyy, 2018. "Determination and estimation of risk aversion coefficients," Computational Management Science, Springer, vol. 15(2), pages 297-317, June.
    5. Francisco Peñaranda & Enrique Sentana, 2024. "Portfolio management with big data," Working Papers wp2024_2411, CEMFI.
    6. Liu, Congzheng & Zhu, Wenqi, 2024. "Newsvendor conditional value-at-risk minimisation: A feature-based approach under adaptive data selection," European Journal of Operational Research, Elsevier, vol. 313(2), pages 548-564.
    7. Ken Kobayashi & Yuichi Takano & Kazuhide Nakata, 2021. "Bilevel cutting-plane algorithm for cardinality-constrained mean-CVaR portfolio optimization," Journal of Global Optimization, Springer, vol. 81(2), pages 493-528, October.
    8. William Lefebvre & Gregoire Loeper & Huy^en Pham, 2020. "Mean-variance portfolio selection with tracking error penalization," Papers 2009.08214, arXiv.org, revised Sep 2020.
    9. Andreas Thomann, 2021. "Multi-asset scenario building for trend-following trading strategies," Annals of Operations Research, Springer, vol. 299(1), pages 293-315, April.
    10. Benati, S. & Conde, E., 2022. "A relative robust approach on expected returns with bounded CVaR for portfolio selection," European Journal of Operational Research, Elsevier, vol. 296(1), pages 332-352.
    11. Pattanayak, Sirsha & Ramkumar, M. & Goswami, Mohit & Rana, Nripendra P., 2024. "Blockchain technology and supply chain performance: The role of trust and relational capabilities," International Journal of Production Economics, Elsevier, vol. 271(C).
    12. Geng Deng & Tim Dulaney & Craig McCann & Olivia Wang, 2013. "Robust portfolio optimization with Value-at-Risk-adjusted Sharpe ratios," Journal of Asset Management, Palgrave Macmillan, vol. 14(5), pages 293-305, October.
    13. Ben-Tal, A. & den Hertog, D. & De Waegenaere, A.M.B. & Melenberg, B. & Rennen, G., 2011. "Robust Solutions of Optimization Problems Affected by Uncertain Probabilities," Other publications TiSEM 4d43dc51-86d9-4804-8563-9, Tilburg University, School of Economics and Management.
    14. Marchioni, Andrea & Magni, Carlo Alberto, 2018. "Investment decisions and sensitivity analysis: NPV-consistency of rates of return," European Journal of Operational Research, Elsevier, vol. 268(1), pages 361-372.
    15. Maria Scutellà & Raffaella Recchia, 2013. "Robust portfolio asset allocation and risk measures," Annals of Operations Research, Springer, vol. 204(1), pages 145-169, April.
    16. Yang Li & Quanlong Liu, 2023. "The Operational Mechanism of Agricultural Products Supply Chain Finance Based on the Mode-Capability-Customer Matching Approach," Sustainability, MDPI, vol. 15(23), pages 1-24, December.
    17. Omid Momen & Akbar Esfahanipour & Abbas Seifi, 2020. "A robust behavioral portfolio selection: model with investor attitudes and biases," Operational Research, Springer, vol. 20(1), pages 427-446, March.
    18. Ashrafi, Hedieh & Thiele, Aurélie C., 2021. "A study of robust portfolio optimization with European options using polyhedral uncertainty sets," Operations Research Perspectives, Elsevier, vol. 8(C).
    19. Kim, Jang Ho & Kim, Woo Chang & Fabozzi, Frank J., 2016. "Portfolio selection with conservative short-selling," Finance Research Letters, Elsevier, vol. 18(C), pages 363-369.
    20. Bazovkin, Pavel, 2014. "Geometrical framework for robust portfolio optimization," Discussion Papers in Econometrics and Statistics 01/14, University of Cologne, Institute of Econometrics and Statistics.

    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:jmathe:v:12:y:2024:i:3:p:438-:d:1329162. 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.