Identify and rank the challenges of implementing sustainable Supply Chain Blockchain Technology using the Bayesian Best Worst Method
Abstract
Globalization initiated the challenges in Supply Chain (SC) such as management and control. In this situation, Blockchain as a digital distributed ledger can guarantee clarity, tractability, and safety. Many case studies proved that we can use Blockchain Technology (BT) to solve global supply chain problems especially in smart contracts with their potential applications. BT is in its early period and it is hard to find supply chains that have successfully implemented this technology to track their sustainable actions. Therefore, it is worth studying about the role of customers, members, domestic, national, and international challenges that could resist implementing Blockchain and may affect SC sustainability. Accordingly, four categories of barriers to the use of BT are introduced which are inter-organizational, intra-organizational, technical, and external barriers. Then with Bayesian Best Worst Method, we ranked the BT barriers and the sub-barriers. The study illustrates the interconnection of these barriers and the priority of each element. The lack of business models and the best practices in implementing Blockchain technology is a challenge and it is important that practitioners acknowledge these barriers in the first steps.
First published online 04 May 2021
Keyword : Supply Chain, Blockchain Technology, Bayesian Best Worst Method, sustainable
How to Cite
Liu, P., Hendalianpour, A., Hamzehlou, M., Feylizadeh, M. R., & Razmi, J. (2021). Identify and rank the challenges of implementing sustainable Supply Chain Blockchain Technology using the Bayesian Best Worst Method. Technological and Economic Development of Economy, 27(3), 656-680. https://doi.org/10.3846/tede.2021.14421
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Hendalianpour, A., Hamzehlou, M., Feylizadeh, M. R., Xie, N., & Shakerizadeh, M. H. (2020a). Coordination and competition in two-echelon supply chain using grey revenue-sharing contracts. Grey Systems: Theory and Application (in press). https://doi.org/10.1108/GS-04-2020-0056
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Mohammadi, M., & Rezaei, J. (2019). Bayesian best-worst method: A probabilistic group decision making model. Omega, 96, 102075. https://doi.org/10.1016/j.omega.2019.06.001
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Salo, A. A., & Hämäläinen, R. P. (1997). On the measurement of preferences in the analytic hierarchy process. Journal of Multi-Criteria Decision Analysis, 6(6), 309–319. https://doi.org/10.1002/(SICI)1099-1360(199711)6:6<309::AID-MCDA163>3.0.CO;2-2
Schmidt, C. G., & Wagner, S. M. (2019). Blockchain and supply chain relations: A transaction cost theory perspective. Journal of Purchasing and Supply Management, 25(4), 100552. https://doi.org/10.1016/j.pursup.2019.100552
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Skilton, P. F., & Robinson, J. L. (2009). Traceability and normal accident theory: How does supply network complexity influence the traceability of adverse events? Journal of Supply Chain Management, 45(3), 40–53. https://doi.org/10.1111/j.1745-493X.2009.03170.x
Tian, F. (2012). An information system for food safety monitoring in supply chains based on HACCP, blockchain and internet of things [Doctoral dissertation]. WU Vienna University of Economics and Business. http://epub.wu.ac.at/
Tian, F. (2016). An agri-food supply chain traceability system for China based on RFID & blockchain technology. In 13th International Conference on Service Systems and Service Management (ICSSSM). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ICSSSM.2016.7538424
Tokar, T., & Swink, M. (2019). Public policy and supply chain management: using shared foundational principles to improve formulation, implementation, and evaluation. Journal of Supply Chain Management, 55(2), 68–79. https://doi.org/10.1111/jscm.12190
Underwood, S. (2016). Blockchain beyond bitcoin. Communications of the ACM, 59(11), 15–17. https://doi.org/10.1145/2994581
Wang, Y., Singgih, M., Wang, J., & Rit, M. (2019). Making sense of blockchain technology: How will it transform supply chains? International Journal of Production Economics, 211, 221–236. https://doi.org/10.1016/j.ijpe.2019.02.002
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Borgatti, S. P., & Li, X. (2009). On social network analysis in a supply chain context. Journal of Supply Chain Management, 45(2), 5–22. https://doi.org/10.1111/j.1745-493X.2009.03166.x
Busse, C., Meinlschmidt, J., & Foerstl, K. (2017). Managing information processing needs in global supply chains: a prerequisite to sustainable supply chain management. Journal of Supply Chain Management, 53(1), 87–113. https://doi.org/10.1111/jscm.12129
Carter, C. R., Rogers, D. S., & Choi, T. Y. (2015). Toward the theory of the supply chain. Journal of Supply Chain Management, 51(2), 89–97. https://doi.org/10.1111/jscm.12073
Choi, T.-M., Feng, L., & Li, R. (2020). Information disclosure structure in supply chains with rental service platforms in the blockchain technology era. International Journal of Production Economics, 221, 107473. https://doi.org/10.1016/j.ijpe.2019.08.008
de Sousa Jabbour, A. B. L., Jabbour, C. J. C., Foropon, C., & Filho, M. G. (2018). When titans meet – Can industry 4.0 revolutionise the environmentally-sustainable manufacturing wave? The role of critical success factors. Technological Forecasting and Social Change, 132, 18–25. https://doi.org/10.1016/j.techfore.2018.01.017
Dolgui, A., Ivanov, D., & Sokolov, B. (2018). Ripple effect in the supply chain: an analysis and recent literature. International Journal of Production Research, 56(1–2), 414–430. https://doi.org/10.1080/00207543.2017.1387680
Ellis, S. C., Shockley, J., & Henry, R. M. (2011). Making sense of supple disruption risk research: a conceptual framework grounded in enactment theory. Journal of Supply Chain Management, 47(2), 65–96. https://doi.org/10.1111/j.1745-493X.2011.03217.x
Eyal, I. (2017). Blockchain technology: transforming libertarian cryptocurrency dreams to finance and banking realities. Computer, 50(9), 38–49. https://doi.org/10.1109/MC.2017.3571042
Fahimnia, B., Sarkis, J., & Davarzani, H. (2015). Green supply chain management: A review and bibliometric analysis. International Journal of Production Economics, 162, 101–114. https://doi.org/10.1016/j.ijpe.2015.01.003
Fanning, K., & Centers, D. P. (2016). Blockchain and its coming impact on financial services. Journal of Corporate Accounting & Finance, 27(5), 53–57. https://doi.org/10.1002/jcaf.22179
Flynn, B. B., Koufteros, X., & Lu, G. (2016). On theory in supply chain uncertainty and its implications for supply chain integration. Journal of Supply Chain Management, 52(3), 3–27. https://doi.org/10.1111/jscm.12106
Govindan, K., & Hasanagic, M. (2018). A systematic review on drivers, barriers, and practices towards circular economy: a supply chain perspective. International Journal of Production Research, 56(1–2), 278–311. https://doi.org/10.1080/00207543.2017.1402141
Gray, J. V., Skowronski, K., Esenduran, G., & Johnny Rungtusanatham, M. (2013). The reshoring phenomenon: What supply chain academics ought to know and should do. Journal of Supply Chain Management, 49(2), 27–33. https://doi.org/10.1111/jscm.12012
Hendalianpour, A. (2020). Optimal lot-size and price of perishable goods: a novel game-theoretic model using double interval grey numbers. Computers & Industrial Engineering, 149, 106780. https://doi.org/10.1016/j.cie.2020.106780
Hendalianpour, A., Fakhrabadi, M., Sangari, M. S., & Razmi, J. (2020b). A combined benders decomposition and lagrangian relaxation algorithm for optimizing a multi-product, multi-level Omni-channel distribution system. Scientia Iranica (in press). https://doi.org/10.24200/sci.2020.53644.3349
Hendalianpour, A., Fakhrabadi, M., Zhang, X., Feylizadeh, M. R., Gheisari, M., Liu, P., & Ashktorab, N. (2019). Hybrid model of IVFRN-BWM and robust goal programming in agile and flexible supply chain, a case study: automobile industry. IEEE Access, 7, 71481–71492. https://doi.org/10.1109/ACCESS.2019.2915309
Hendalianpour, A., Hamzehlou, M., Feylizadeh, M. R., Xie, N., & Shakerizadeh, M. H. (2020a). Coordination and competition in two-echelon supply chain using grey revenue-sharing contracts. Grey Systems: Theory and Application (in press). https://doi.org/10.1108/GS-04-2020-0056
Hofmann, E., Strewe, U. M., & Bosia, N. (2018). Supply chain finance and blockchain technology: the case of reverse securitisation. Springer. https://books.google.com/books?hl=en&lr=&id=tLIvDwAAQBAJ&oi=fnd&pg=PR5&dq=Hofmann,+E.,+U.+M.+Strewe,+and+N.+Bosia.+2017.+Supply+Chain+Finance+and+Blockchain+Technology:+The+Case+of+Reverse+Securitisation.+Cham:+Springer.&ots=kVXZXUAurW&sig=wxvx32gi-tb794A
Ivanov, D., Dolgui, A., & Sokolov, B. (2019). The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics. International Journal of Production Research, 57(3), 829–846. https://doi.org/10.1080/00207543.2018.1488086
Kass, R. E., Gilks, W. R., Richardson, S., & Spiegelhalter, D. J. (1997). Markov chain Monte Carlo in practice. Journal of the American Statistical Association, 92(440), 1645–1646. https://doi.org/10.2307/2965438
Kasten, J. (2019). Blockchain application: the dairy supply chain. Journal of Supply Chain Management Systems, 8(1), 45–54. http://search.ebscohost.com/login.aspx?direct=true&profile=ehost&scope=site&authtype=crawler&jrnl=22771387&AN=136630613&h=VMq%2BA6q3EQUOIRGqZroWvm0dlz2WkrSUuCegNFf%2FvdqDht6W%2B4KAA%2BWfKeEh9s%2BeRkV95UkksRdEv3q9oxquGA%3D%3D&crl=c
Manupati, V. K., Schoenherr, T., Ramkumar, M., Wagner, S. M., Pabba, S. K., & Inder Raj Singh, R. (2020). A blockchain-based approach for a multi-echelon sustainable supply chain. International Journal of Production Research, 58(7), 2222–2241. https://doi.org/10.1080/00207543.2019.1683248
Mcivor, R. (2013). Understanding the manufacturing location decision: The case for the transaction cost and capability perspectives. Journal of Supply Chain Management, 49(2), 23–26. https://doi.org/10.1111/jscm.12010
Mohammadi, M., & Rezaei, J. (2019). Bayesian best-worst method: A probabilistic group decision making model. Omega, 96, 102075. https://doi.org/10.1016/j.omega.2019.06.001
Ølnes, S., Ubacht, J., & Janssen, M. (2017). Blockchain in government: Benefits and implications of distributed ledger technology for information sharing. Government Information Quarterly, 34(3), 355–364. https://doi.org/10.1016/j.giq.2017.09.007
Plummer, M. (2004). JAGS: Just another Gibbs sampler. http://www3.jouy.inra.fr/miaj/public/matrisq/Contacts/applibugs.07_11_08.plummer.pdf
Rajeev, A., Pati, R. K., Padhi, S. S., & Govindan, K. (2017). Evolution of sustainability in supply chain management: A literature review. Journal of Cleaner Production, 162, 299–314. https://doi.org/10.1016/j.jclepro.2017.05.026
Rezaei, J. (2016). Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega, 64, 126–130. https://doi.org/10.1016/j.omega.2015.12.001
Saberi, S., Kouhizadeh, M., & Sarkis, J. (2018). Blockchain technology: A panacea or pariah for resources conservation and recycling? Resources, Conservation and Recycling, 130, 80–81. https://doi.org/10.1016/j.resconrec.2017.11.020
Saberi, S., Kouhizadeh, M., Sarkis, J., & Shen, L. (2019a). Blockchain technology and its relationships to sustainable supply chain management. International Journal of Production Research, 57(7), 2117– 2135. https://doi.org/10.1080/00207543.2018.1533261
Saberi, S., Kouhizadeh, M., Sarkis, J., & Shen, L. (2019b). Blockchain technology and its relationships to sustainable supply chain management. International Journal of Production Research, 57(7), 2117– 2135. https://doi.org/10.1080/00207543.2018.1533261
Salo, A. A., & Hämäläinen, R. P. (1997). On the measurement of preferences in the analytic hierarchy process. Journal of Multi-Criteria Decision Analysis, 6(6), 309–319. https://doi.org/10.1002/(SICI)1099-1360(199711)6:6<309::AID-MCDA163>3.0.CO;2-2
Schmidt, C. G., & Wagner, S. M. (2019). Blockchain and supply chain relations: A transaction cost theory perspective. Journal of Purchasing and Supply Management, 25(4), 100552. https://doi.org/10.1016/j.pursup.2019.100552
Schniederjans, D. G., Curado, C., & Khalajhedayati, M. (2020). Supply chain digitisation trends: An integration of knowledge management. International Journal of Production Economics, 220, 107439. https://doi.org/10.1016/j.ijpe.2019.07.012
Skilton, P. F., & Robinson, J. L. (2009). Traceability and normal accident theory: How does supply network complexity influence the traceability of adverse events? Journal of Supply Chain Management, 45(3), 40–53. https://doi.org/10.1111/j.1745-493X.2009.03170.x
Tian, F. (2012). An information system for food safety monitoring in supply chains based on HACCP, blockchain and internet of things [Doctoral dissertation]. WU Vienna University of Economics and Business. http://epub.wu.ac.at/
Tian, F. (2016). An agri-food supply chain traceability system for China based on RFID & blockchain technology. In 13th International Conference on Service Systems and Service Management (ICSSSM). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ICSSSM.2016.7538424
Tokar, T., & Swink, M. (2019). Public policy and supply chain management: using shared foundational principles to improve formulation, implementation, and evaluation. Journal of Supply Chain Management, 55(2), 68–79. https://doi.org/10.1111/jscm.12190
Underwood, S. (2016). Blockchain beyond bitcoin. Communications of the ACM, 59(11), 15–17. https://doi.org/10.1145/2994581
Wang, Y., Singgih, M., Wang, J., & Rit, M. (2019). Making sense of blockchain technology: How will it transform supply chains? International Journal of Production Economics, 211, 221–236. https://doi.org/10.1016/j.ijpe.2019.02.002
Yatsalo, B. I., & Martinez, L. (2018). Fuzzy rank acceptability analysis: A confidence measure of ranking fuzzy numbers. IEEE Transactions on Fuzzy Systems, 26(6), 3579–3593. https://doi.org/10.1109/TFUZZ.2018.2838063
Zhao, G., Liu, S., Lopez, C., Lu, H., Elgueta, S., Chen, H., & Boshkoska, B. M. (2019). Blockchain technology in agri-food value chain management: A synthesis of applications, challenges and future research directions. Computers in Industry, 109, 83–99. https://doi.org/10.1016/j.compind.2019.04.002
Zijm, H., Knofius, N., & van der Heijden, M. (2019). Additive Manufacturing and Its Impact on the Supply Chain. In H. Zijm, M. Klumpp, A. Regattieri, & S. Heragu (Eds.), Operations, logistics and supply chain management (pp. 521–543). Springer. https://doi.org/10.1007/978-3-319-92447-2_23