IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v339y2024i1d10.1007_s10479-021-04101-4.html
   My bibliography  Save this article

Great partners: how deep learning and blockchain help improve business operations together

Author

Listed:
  • Suyuan Luo

    (Shenzhen University)

  • Tsan-Ming Choi

    (College of Management, National Taiwan University)

Abstract

Business operations have entered the digital era in which artificial intelligence (AI), machine learning (ML) and blockchain (BKC) have emerged as major disruptive forces. In AI and ML, deep learning is a critical area. In this paper, we aim to investigate how deep learning and BKC together can help improve business operations. We first examine the operations research (OR) literature related to the applications of deep learning for business operations. Then, we discuss the prior studies on using BKC for operations. After that, we explore deep learning’s applications for BKC, BKC’s applications for deep learning as well as how deep learning and BKC have been used together for business operations. Then, we construct a research framework and propose future research directions.

Suggested Citation

  • Suyuan Luo & Tsan-Ming Choi, 2024. "Great partners: how deep learning and blockchain help improve business operations together," Annals of Operations Research, Springer, vol. 339(1), pages 53-78, August.
    • Handle: RePEc:spr:annopr:v:339:y:2024:i:1:d:10.1007_s10479-021-04101-4
    DOI: 10.1007/s10479-021-04101-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-021-04101-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-021-04101-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kim, A. & Yang, Y. & Lessmann, S. & Ma, T. & Sung, M.-C. & Johnson, J.E.V., 2020. "Can deep learning predict risky retail investors? A case study in financial risk behavior forecasting," European Journal of Operational Research, Elsevier, vol. 283(1), pages 217-234.
    2. Choi, Tsan-Ming & Feng, Lipan & Li, Rong, 2020. "Information disclosure structure in supply chains with rental service platforms in the blockchain technology era," International Journal of Production Economics, Elsevier, vol. 221(C).
    3. Chun-Hung Chiu & Tsan-Ming Choi, 2016. "Supply chain risk analysis with mean-variance models: a technical review," Annals of Operations Research, Springer, vol. 240(2), pages 489-507, May.
    4. Choi, Tsan-Ming & Guo, Shu & Liu, Na & Shi, Xiutian, 2020. "Optimal pricing in on-demand-service-platform-operations with hired agents and risk-sensitive customers in the blockchain era," European Journal of Operational Research, Elsevier, vol. 284(3), pages 1031-1042.
    5. Jiri Chod & Nikolaos Trichakis & Gerry Tsoukalas & Henry Aspegren & Mark Weber, 2020. "On the Financing Benefits of Supply Chain Transparency and Blockchain Adoption," Management Science, INFORMS, vol. 66(10), pages 4378-4396, October.
    6. Dutta, Pankaj & Choi, Tsan-Ming & Somani, Surabhi & Butala, Richa, 2020. "Blockchain technology in supply chain operations: Applications, challenges and research opportunities," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).
    7. Choi, Tsan-Ming & Guo, Shu & Luo, Suyuan, 2020. "When blockchain meets social-media: Will the result benefit social media analytics for supply chain operations management?," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 135(C).
    8. Luo, Suyuan & Lin, Xudong & Zheng, Zunxin, 2019. "A novel CNN-DDPG based AI-trader: Performance and roles in business operations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 131(C), pages 68-79.
    9. Shailendra Rathore & Yi Pan & Jong Hyuk Park, 2019. "BlockDeepNet: A Blockchain-Based Secure Deep Learning for IoT Network," Sustainability, MDPI, vol. 11(14), pages 1-15, July.
    10. Chun‐Hung Chiu & Tsan‐Ming Choi & Xin Dai & Bin Shen & Jin‐Hui Zheng, 2018. "Optimal Advertising Budget Allocation in Luxury Fashion Markets with Social Influences: A Mean‐Variance Analysis," Production and Operations Management, Production and Operations Management Society, vol. 27(8), pages 1611-1629, August.
    11. Ajay Kumar & Ravi Shankar & Alok Choudhary & Lakshman S. Thakur, 2016. "A big data MapReduce framework for fault diagnosis in cloud-based manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 54(23), pages 7060-7073, December.
    12. Juzhi Zhang & Tsan‐Ming Choi & T. C. E. Cheng, 2020. "Stochastic production capacity: A bane or a boon for quick response supply chains?," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(2), pages 126-146, March.
    13. Kraus, Mathias & Feuerriegel, Stefan & Oztekin, Asil, 2020. "Deep learning in business analytics and operations research: Models, applications and managerial implications," European Journal of Operational Research, Elsevier, vol. 281(3), pages 628-641.
    14. Tsan‐Ming Choi & Stein W. Wallace & Yulan Wang, 2018. "Big Data Analytics in Operations Management," Production and Operations Management, Production and Operations Management Society, vol. 27(10), pages 1868-1883, October.
    15. Juzhi Zhang & Suresh P. Sethi & Tsan‐Ming Choi & T. C. E. Cheng, 2020. "Supply Chains Involving a Mean‐Variance‐Skewness‐Kurtosis Newsvendor: Analysis and Coordination," Production and Operations Management, Production and Operations Management Society, vol. 29(6), pages 1397-1430, June.
    16. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
    17. Tsan‐Ming Choi & James H. Lambert, 2017. "Advances in Risk Analysis with Big Data," Risk Analysis, John Wiley & Sons, vol. 37(8), pages 1435-1442, August.
    18. Mai, Feng & Tian, Shaonan & Lee, Chihoon & Ma, Ling, 2019. "Deep learning models for bankruptcy prediction using textual disclosures," European Journal of Operational Research, Elsevier, vol. 274(2), pages 743-758.
    19. 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.
    20. Verónica H. Villena, 2019. "The Missing Link? The Strategic Role of Procurement in Building Sustainable Supply Networks," Production and Operations Management, Production and Operations Management Society, vol. 28(5), pages 1149-1172, May.
    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. Choi, Tsan-Ming & Ouyang, Xu, 2021. "Initial coin offerings for blockchain based product provenance authentication platforms," International Journal of Production Economics, Elsevier, vol. 233(C).
    2. Xu, Xiaoping & Choi, Tsan-Ming, 2021. "Supply chain operations with online platforms under the cap-and-trade regulation: Impacts of using blockchain technology," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 155(C).
    3. Zhang, Tianyu & Dong, Peiwu & Chen, Xiangfeng & Gong, Yu, 2023. "The impacts of blockchain adoption on a dual-channel supply chain with risk-averse members," Omega, Elsevier, vol. 114(C).
    4. Choi, Tsan-Ming, 2020. "Innovative “Bring-Service-Near-Your-Home” operations under Corona-Virus (COVID-19/SARS-CoV-2) outbreak: Can logistics become the Messiah?," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 140(C).
    5. Dutta, Pankaj & Choi, Tsan-Ming & Somani, Surabhi & Butala, Richa, 2020. "Blockchain technology in supply chain operations: Applications, challenges and research opportunities," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).
    6. Liu, Shuai & Hua, Guowei & Kang, Yuxuan & Edwin Cheng, T.C. & Xu, Yadong, 2022. "What value does blockchain bring to the imported fresh food supply chain?," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 165(C).
    7. Cao, Yu & Yi, Chaoqun & Wan, Guangyu & Hu, Hanli & Li, Qingsong & Wang, Shouyang, 2022. "An analysis on the role of blockchain-based platforms in agricultural supply chains," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 163(C).
    8. Tiwari, Sunil & Sharma, Pankaj & Choi, Tsan-Ming & Lim, Andrew, 2023. "Blockchain and third-party logistics for global supply chain operations: Stakeholders’ perspectives and decision roadmap," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 170(C).
    9. Yu, Yugang & Luo, Yifei & Shi, Ye, 2022. "Adoption of blockchain technology in a two-stage supply chain: Spillover effect on workforce," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
    10. Ma, Deqing & Hu, Jinsong, 2022. "The optimal combination between blockchain and sales format in an internet platform-based closed-loop supply chain," International Journal of Production Economics, Elsevier, vol. 254(C).
    11. Kriebel, Johannes & Stitz, Lennart, 2022. "Credit default prediction from user-generated text in peer-to-peer lending using deep learning," European Journal of Operational Research, Elsevier, vol. 302(1), pages 309-323.
    12. Li, Qingying & Ma, Manqiong & Shi, Tianqin & Zhu, Chen, 2022. "Green investment in a sustainable supply chain: The role of blockchain and fairness," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 167(C).
    13. Mangla, Sachin Kumar & Kazancoglu, Yigit & Ekinci, Esra & Liu, Mengqi & Özbiltekin, Melisa & Sezer, Muruvvet Deniz, 2021. "Using system dynamics to analyze the societal impacts of blockchain technology in milk supply chainsrefer," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    14. Wang, Yingjia & Lin, Jiaxin & Choi, Tsan-Ming, 2020. "Gray market and counterfeiting in supply chains: A review of the operations literature and implications to luxury industries," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 133(C).
    15. Niu, Baozhuang & Mu, Zihao & Cao, Bin & Gao, Jie, 2021. "Should multinational firms implement blockchain to provide quality verification?," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    16. Koen W. de Bock & Kristof Coussement & Arno De Caigny & Roman Slowiński & Bart Baesens & Robert N Boute & Tsan-Ming Choi & Dursun Delen & Mathias Kraus & Stefan Lessmann & Sebastián Maldonado & David , 2023. "Explainable AI for Operational Research: A Defining Framework, Methods, Applications, and a Research Agenda," Post-Print hal-04219546, HAL.
    17. Niu, Baozhuang & Dong, Jian & Liu, Yaoqi, 2021. "Incentive alignment for blockchain adoption in medicine supply chains," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    18. Siqin, Tana & Choi, Tsan-Ming & Chung, Sai-Ho, 2022. "Optimal E-tailing channel structure and service contracting in the platform era," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 160(C).
    19. Choi, Tsan-Ming & Siqin, Tana, 2022. "Blockchain in logistics and production from Blockchain 1.0 to Blockchain 5.0: An intra-inter-organizational framework," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 160(C).
    20. Choi, Tsan-Ming & Guo, Shu & Luo, Suyuan, 2020. "When blockchain meets social-media: Will the result benefit social media analytics for supply chain operations management?," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 135(C).

    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:spr:annopr:v:339:y:2024:i:1:d:10.1007_s10479-021-04101-4. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.