IDEAS home Printed from https://ideas.repec.org/a/inm/ormnsc/v66y2020i10p4378-4396.html
   My bibliography  Save this article

On the Financing Benefits of Supply Chain Transparency and Blockchain Adoption

Author

Listed:
  • Jiri Chod

    (Carroll School of Management, Boston College, Chestnut Hill, Massachusetts 02467;)

  • Nikolaos Trichakis

    (MIT Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142; Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139;)

  • Gerry Tsoukalas

    (The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104;)

  • Henry Aspegren

    (Google Inc., Mountain View, California 94043)

  • Mark Weber

    (IBM Research, Cambridge, Massachusetts 02142; MIT-IBM Watson Artificial Intelligence Laboratory, Cambridge, Massachusetts 02142)

Abstract

We develop a theory that shows signaling a firm’s fundamental quality (e.g., its operational capabilities) to lenders through inventory transactions to be more efficient—it leads to less costly operational distortions—than signaling through loan requests, and we characterize how the efficiency gains depend on firm operational characteristics, such as operating costs, market size, and inventory salvage value. Signaling through inventory being only tenable when inventory transactions are verifiable at low enough cost, we then turn our attention to how this verifiability can be achieved in practice and argue that blockchain technology could enable it more efficiently than traditional monitoring mechanisms. To demonstrate, we develop b_verify, an open-source blockchain protocol that leverages Bitcoin to provide supply chain transparency at scale and in a cost-effective way. The paper identifies an important benefit of blockchain adoption—by opening a window of transparency into a firm’s supply chain, blockchain technology furnishes the ability to secure favorable financing terms at lower signaling costs. Furthermore, the analysis of the preferred signaling mode sheds light on what types of firms or supply chains would stand to benefit the most from this use of blockchain technology.

Suggested Citation

  • 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.
  • Handle: RePEc:inm:ormnsc:v:66:y:2020:i:10:p:4378-4396
    DOI: 10.1287/mnsc.2019.3434
    as

    Download full text from publisher

    File URL: https://doi.org/10.1287/mnsc.2019.3434
    Download Restriction: no

    File URL: https://libkey.io/10.1287/mnsc.2019.3434?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
    ---><---

    References listed on IDEAS

    as
    1. Fabbri, Daniela & Menichini, Anna Maria C., 2016. "The commitment problem of secured lending," Journal of Financial Economics, Elsevier, vol. 120(3), pages 561-584.
    2. Duan, Jin-Chuan & Yoon, Suk Heun, 1993. "Loan commitments, investment decisions and the signalling equilibrium," Journal of Banking & Finance, Elsevier, vol. 17(4), pages 645-661, June.
    3. Lin William Cong & Zhiguo He & Jiasun Li & Wei Jiang, 2021. "Decentralized Mining in Centralized Pools [Concentrating on the fall of the labor share]," The Review of Financial Studies, Society for Financial Studies, vol. 34(3), pages 1191-1235.
    4. Mike Burkart & Tore Ellingsen, 2004. "In-Kind Finance: A Theory of Trade Credit," American Economic Review, American Economic Association, vol. 94(3), pages 569-590, June.
    5. Gerry Tsoukalas & Brett Hemenway Falk, 2020. "Token-Weighted Crowdsourcing," Management Science, INFORMS, vol. 66(9), pages 3843-3859, September.
    6. Özalp Özer & Wei Wei, 2006. "Strategic Commitments for an Optimal Capacity Decision Under Asymmetric Forecast Information," Management Science, INFORMS, vol. 52(8), pages 1238-1257, August.
    7. Stephen A. Ross, 1977. "The Determination of Financial Structure: The Incentive-Signalling Approach," Bell Journal of Economics, The RAND Corporation, vol. 8(1), pages 23-40, Spring.
    8. In-Koo Cho & David M. Kreps, 1987. "Signaling Games and Stable Equilibria," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 102(2), pages 179-221.
    9. Diamond, Douglas W, 1991. "Monitoring and Reputation: The Choice between Bank Loans and Directly Placed Debt," Journal of Political Economy, University of Chicago Press, vol. 99(4), pages 689-721, August.
    10. Guoming Lai & Wenqiang Xiao, 2018. "Inventory Decisions and Signals of Demand Uncertainty to Investors," Manufacturing & Service Operations Management, INFORMS, vol. 20(1), pages 113-129, February.
    11. Eric Budish, 2018. "The Economic Limits of Bitcoin and the Blockchain," NBER Working Papers 24717, National Bureau of Economic Research, Inc.
    12. Lin William Cong & Zhiguo He, 2019. "Blockchain Disruption and Smart Contracts," The Review of Financial Studies, Society for Financial Studies, vol. 32(5), pages 1754-1797.
    13. Bruno Biais & Christophe Bisière & Matthieu Bouvard & Catherine Casamatta, 2019. "The Blockchain Folk Theorem," The Review of Financial Studies, Society for Financial Studies, vol. 32(5), pages 1662-1715.
    14. David Yermack, 2017. "Corporate Governance and Blockchains," Review of Finance, European Finance Association, vol. 21(1), pages 7-31.
    15. Jiri Chod, 2017. "Inventory, Risk Shifting, and Trade Credit," Management Science, INFORMS, vol. 63(10), pages 3207-3225, October.
    16. Hellmuth Milde & John G. Riley, 1988. "Signaling in Credit Markets," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 103(1), pages 101-129.
    17. Hanna Halaburda, 2018. "Blockchain Revolution Without the Blockchain," Staff Analytical Notes 2018-5, Bank of Canada.
    18. Özalp Özer & Yanchong Zheng & Yufei Ren, 2014. "Trust, Trustworthiness, and Information Sharing in Supply Chains Bridging China and the United States," Management Science, INFORMS, vol. 60(10), pages 2435-2460, October.
    19. Gérard P. Cachon & Martin A. Lariviere, 2001. "Contracting to Assure Supply: How to Share Demand Forecasts in a Supply Chain," Management Science, INFORMS, vol. 47(5), pages 629-646, May.
    20. Besanko, David & Thakor, Anjan V., 1987. "Competitive equilibrium in the credit market under asymmetric information," Journal of Economic Theory, Elsevier, vol. 42(1), pages 167-182, June.
    21. Dan A. Iancu & Nikolaos Trichakis & Gerry Tsoukalas, 2017. "Is Operating Flexibility Harmful Under Debt?," Management Science, INFORMS, vol. 63(6), pages 1730-1761, June.
    22. Lin William Cong & Ye Li & Neng Wang, 2021. "Tokenomics: Dynamic Adoption and Valuation [The demand of liquid assets with uncertain lumpy expenditures]," The Review of Financial Studies, Society for Financial Studies, vol. 34(3), pages 1105-1155.
    23. Biais, Bruno & Gollier, Christian, 1997. "Trade Credit and Credit Rationing," The Review of Financial Studies, Society for Financial Studies, vol. 10(4), pages 903-937.
    24. Guoming Lai & Wenqiang Xiao & Jun Yang, 2012. "Supply Chain Performance Under Market Valuation: An Operational Approach to Restore Efficiency," Management Science, INFORMS, vol. 58(10), pages 1933-1951, October.
    25. Jain, Neelam, 2001. "Monitoring costs and trade credit," The Quarterly Review of Economics and Finance, Elsevier, vol. 41(1), pages 89-110.
    26. Christopher S. Tang & S. Alex Yang & Jing Wu, 2018. "Sourcing from Suppliers with Financial Constraints and Performance Risk," Manufacturing & Service Operations Management, INFORMS, vol. 20(1), pages 70-84, February.
    27. Bebchuk, Lucian Arye & Stole, Lars A, 1993. "Do Short-Term Objectives Lead to Under- or Overinvestment in Long-Term Projects?," Journal of Finance, American Finance Association, vol. 48(2), pages 719-729, June.
    28. Emery, Gary W., 1984. "A Pure Financial Explanation for Trade Credit," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 19(3), pages 271-285, September.
    29. Soudipta Chakraborty & Robert Swinney, 2021. "Signaling to the Crowd: Private Quality Information and Rewards-Based Crowdfunding," Manufacturing & Service Operations Management, INFORMS, vol. 23(1), pages 155-169, 1-2.
    30. Bimpikis, Kostas & Drakopoulos, Kimon & Ehsani, Shayan, 2018. "Disclosing Information in Strategic Experimentation," Research Papers repec:ecl:stabus:3635, Stanford University, Graduate School of Business.
    31. Michael Spence, 1973. "Job Market Signaling," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 87(3), pages 355-374.
    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. Zhang, Zhiming & Ren, Da & Lan, Yanfei & Yang, Shanxue, 2022. "Price competition and blockchain adoption in retailing markets," European Journal of Operational Research, Elsevier, vol. 300(2), pages 647-660.
    2. Volodymyr Babich & Panos Kouvelis, 2018. "Introduction to the Special Issue on Research at the Interface of Finance, Operations, and Risk Management (iFORM): Recent Contributions and Future Directions," Manufacturing & Service Operations Management, INFORMS, vol. 20(1), pages 1-18, February.
    3. Jiri Chod & Nikolaos Trichakis & Gerry Tsoukalas, 2019. "Supplier Diversification Under Buyer Risk," Management Science, INFORMS, vol. 65(7), pages 3150-3173, July.
    4. Jiao Wang & Lima Zhao & Arnd Huchzermeier, 2021. "Operations‐Finance Interface in Risk Management: Research Evolution and Opportunities," Production and Operations Management, Production and Operations Management Society, vol. 30(2), pages 355-389, February.
    5. Lin William Cong & Zhiguo He & Jiasun Li & Wei Jiang, 2021. "Decentralized Mining in Centralized Pools [Concentrating on the fall of the labor share]," The Review of Financial Studies, Society for Financial Studies, vol. 34(3), pages 1191-1235.
    6. Guoming Lai & Wenqiang Xiao, 2018. "Inventory Decisions and Signals of Demand Uncertainty to Investors," Manufacturing & Service Operations Management, INFORMS, vol. 20(1), pages 113-129, February.
    7. Cole, Rebel A. & Sokolyk, Tatyana, 2018. "Debt financing, survival, and growth of start-up firms," Journal of Corporate Finance, Elsevier, vol. 50(C), pages 609-625.
    8. William Schmidt & Ryan W. Buell, 2017. "Experimental Evidence of Pooling Outcomes Under Information Asymmetry," Management Science, INFORMS, vol. 63(5), pages 1586-1605, May.
    9. Michael Sockin & Wei Xiong, 2023. "Decentralization through Tokenization," Journal of Finance, American Finance Association, vol. 78(1), pages 247-299, February.
    10. Jiri Chod, 2017. "Inventory, Risk Shifting, and Trade Credit," Management Science, INFORMS, vol. 63(10), pages 3207-3225, October.
    11. Michael Sockin & Wei Xiong, 2021. "A Model of Cryptocurrencies," Working Papers 2021-67, Princeton University. Economics Department..
    12. Deng, Sijing & Fu, Ke & Xu, Jiayan & Zhu, Kaijie, 2021. "The supply chain effects of trade credit under uncertain demands," Omega, Elsevier, vol. 98(C).
    13. Cao, Yiqiu & Wang, Qiangqiang, 2022. "The informational role of guarantee contracts," European Journal of Operational Research, Elsevier, vol. 301(1), pages 191-202.
    14. Romi Kher & Siri Terjesen & Chen Liu, 2021. "Blockchain, Bitcoin, and ICOs: a review and research agenda," Small Business Economics, Springer, vol. 56(4), pages 1699-1720, April.
    15. Ye Li & Simon Mayer & Simon Mayer, 2021. "Money Creation in Decentralized Finance: A Dynamic Model of Stablecoin and Crypto Shadow Banking," CESifo Working Paper Series 9260, CESifo.
    16. Hanna Halaburda & Guillaume Haeringer & Joshua Gans & Neil Gandal, 2022. "The Microeconomics of Cryptocurrencies," Journal of Economic Literature, American Economic Association, vol. 60(3), pages 971-1013, September.
    17. Tarkom, Augustine & Yang, Lukai, 2024. "Presidential economic approval rating and trade credit," International Review of Financial Analysis, Elsevier, vol. 93(C).
    18. Ferreira, Daniel & Li, Jin & Nikolowa, Radoslawa, 2019. "Corporate Capture of Blockchain Governance," CEPR Discussion Papers 13493, C.E.P.R. Discussion Papers.
    19. Guoming Lai & Wenqiang Xiao & Jun Yang, 2012. "Supply Chain Performance Under Market Valuation: An Operational Approach to Restore Efficiency," Management Science, INFORMS, vol. 58(10), pages 1933-1951, October.
    20. Rose Cunningham, 2004. "Trade Credit and Credit Rationing in Canadian Firms," Staff Working Papers 04-49, Bank of Canada.

    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:inm:ormnsc:v:66:y:2020:i:10:p:4378-4396. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.