IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i19p3550-d928773.html
   My bibliography  Save this article

An Industrial Blockchain-Based Multi-Criteria Decision Framework for Global Freight Management in Agricultural Supply Chains

Author

Listed:
  • Dilupa Nakandala

    (School of Business, Western Sydney University, Sydney 2750, Australia)

  • Yung Po Tsang

    (Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hunghom, Hong Kong)

  • Henry Lau

    (School of Business, Western Sydney University, Sydney 2750, Australia)

  • Carman Ka Man Lee

    (Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hunghom, Hong Kong)

Abstract

In view of increasing supply chain disruption events, for example the China–United States trade war, the COVID-19 pandemic, and the Russia–Ukraine war, the complexity and dynamicity of global freight management keeps increasing. To build a resilient and sustainable supply chain, industrial practitioners are eager to systematically revamp the freight management decision process related to the selection of carriers, shipping lanes, and third-party logistics service providers. Therefore, this study aims at strengthening decision-making capabilities for global freight management, in which an industrial blockchain-based global freight decision framework (IB-GFDF) is proposed to incorporate consortium blockchain technology with the Bayesian best-worst method. Through the blockchain technology, pairwise comparisons can be conducted over the international freight network in a decentralized and immutable manner, and thus, a secure and commonly agreed-on pairwise comparison dataset is acquired. Subsequently, the pairwise comparison dataset with multi-stakeholder opinions is analyzed using the Bayesian best-worst method in order to prioritize the selection decision criteria related to carriers, shipping lanes, and 3PL service providers for global freight management. To verify the methodological feasibility, a case study of an Australian agricultural supply chain firm was conducted to support the development end-to-end (E2E) supply chain solutions originated from Australia. It was found that port infrastructure, ports of call and communication effectiveness were the major criteria for the selection decision, which can be emphasized in future global freight collaboration. In addition, an immutable and append-only record of pairwise comparisons can be established to support the visibility of time-varying stakeholders’ preferences.

Suggested Citation

  • Dilupa Nakandala & Yung Po Tsang & Henry Lau & Carman Ka Man Lee, 2022. "An Industrial Blockchain-Based Multi-Criteria Decision Framework for Global Freight Management in Agricultural Supply Chains," Mathematics, MDPI, vol. 10(19), pages 1-23, September.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:19:p:3550-:d:928773
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/19/3550/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/19/3550/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. William Ferrell & Kimberly Ellis & Phil Kaminsky & Chase Rainwater, 2020. "Horizontal collaboration: opportunities for improved logistics planning," International Journal of Production Research, Taylor & Francis Journals, vol. 58(14), pages 4267-4284, July.
    2. Sdoukopoulos, Eleftherios & Boile, Maria, 2020. "Port-hinterland concept evolution: A critical review," Journal of Transport Geography, Elsevier, vol. 86(C).
    3. Aleksander Banasik & Jacqueline M. Bloemhof-Ruwaard & Argyris Kanellopoulos & G. D. H. Claassen & Jack G. A. J. Vorst, 2018. "Multi-criteria decision making approaches for green supply chains: a review," Flexible Services and Manufacturing Journal, Springer, vol. 30(3), pages 366-396, September.
    4. Orji, Ifeyinwa Juliet & Kusi-Sarpong, Simonov & Huang, Shuangfa & Vazquez-Brust, Diego, 2020. "Evaluating the factors that influence blockchain adoption in the freight logistics industry," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 141(C).
    5. Yanling Chang & Eleftherios Iakovou & Weidong Shi, 2020. "Blockchain in global supply chains and cross border trade: a critical synthesis of the state-of-the-art, challenges and opportunities," International Journal of Production Research, Taylor & Francis Journals, vol. 58(7), pages 2082-2099, April.
    6. Faghih-Roohi, Shahrzad & Akcay, Alp & Zhang, Yingqian & Shekarian, Ehsan & de Jong, Eelco, 2020. "A group risk assessment approach for the selection of pharmaceutical product shipping lanes," International Journal of Production Economics, Elsevier, vol. 229(C).
    7. Gunasekaran, Angappa & Subramanian, Nachiappan & Papadopoulos, Thanos, 2017. "Information technology for competitive advantage within logistics and supply chains: A review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 99(C), pages 14-33.
    8. Carman-Ka-Man Lee & Lucas Lui & Yung-Po Tsang, 2021. "Formulation and Prioritization of Sustainable New Product Design in Smart Glasses Development," Sustainability, MDPI, vol. 13(18), pages 1-18, September.
    9. Rezaei, Jafar & van Roekel, Wilco S. & Tavasszy, Lori, 2018. "Measuring the relative importance of the logistics performance index indicators using Best Worst Method," Transport Policy, Elsevier, vol. 68(C), pages 158-169.
    10. Bo Lu & Han Qiao & Yuzhe Zhao & Xianfei Yang & Guowei Hua & Xiaoxu Zhang, 2018. "Economic evaluation on container shipping route selection," International Journal of Manufacturing Technology and Management, Inderscience Enterprises Ltd, vol. 32(2), pages 85-101.
    11. Hosseini, Seyedmohsen & Ivanov, Dmitry & Dolgui, Alexandre, 2019. "Review of quantitative methods for supply chain resilience analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 125(C), pages 285-307.
    12. Aljohani, Khalid & Thompson, Russell G., 2020. "A multi-criteria spatial evaluation framework to optimise the siting of freight consolidation facilities in inner-city areas," Transportation Research Part A: Policy and Practice, Elsevier, vol. 138(C), pages 51-69.
    13. Shankar, Ravi & Gupta, Rachita & Pathak, Devendra Kumar, 2018. "Modeling critical success factors of traceability for food logistics system," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 119(C), pages 205-222.
    14. Mohammadi, Majid & Rezaei, Jafar, 2020. "Bayesian best-worst method: A probabilistic group decision making model," Omega, Elsevier, vol. 96(C).
    15. Rajesh Kr. Singh & Angappa Gunasekaran & Pravin Kumar, 2018. "Third party logistics (3PL) selection for cold chain management: a fuzzy AHP and fuzzy TOPSIS approach," Annals of Operations Research, Springer, vol. 267(1), pages 531-553, August.
    16. Rezaei, Jafar & Hemmes, Alexander & Tavasszy, Lori, 2017. "Multi-criteria decision-making for complex bundling configurations in surface transportation of air freight," Journal of Air Transport Management, Elsevier, vol. 61(C), pages 95-105.
    17. Rezaei, Jafar, 2015. "Best-worst multi-criteria decision-making method," Omega, Elsevier, vol. 53(C), pages 49-57.
    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. Zhao, Chuan & Guo, Qidong & Dong, Kangyin & Mo, Lipo, 2024. "Multi-scenario analyses for antitrust immunity policies on shipping alliances: A dynamic tripartite evolutionary game perspective," Transportation Research Part A: Policy and Practice, Elsevier, vol. 179(C).

    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. 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).
    2. Bai, Chunguang & Zhu, Qingyun & Sarkis, Joseph, 2021. "Joint blockchain service vendor-platform selection using social network relationships: A multi-provider multi-user decision perspective," International Journal of Production Economics, Elsevier, vol. 238(C).
    3. Fahim, Patrick B.M. & Rezaei, Jafar & Montreuil, Benoit & Tavasszy, Lorant, 2022. "Port performance evaluation and selection in the Physical Internet," Transport Policy, Elsevier, vol. 124(C), pages 83-94.
    4. Mi, Xiaomei & Tang, Ming & Liao, Huchang & Shen, Wenjing & Lev, Benjamin, 2019. "The state-of-the-art survey on integrations and applications of the best worst method in decision making: Why, what, what for and what's next?," Omega, Elsevier, vol. 87(C), pages 205-225.
    5. Feng, Jianghong & Guo, Ping & Xu, Guangyi & Xu, Gangyan & Ning, Yu, 2024. "An integrated decision framework for resilient sustainable waste electric vehicle battery recycling transfer station site selection," Applied Energy, Elsevier, vol. 373(C).
    6. Sarfaraz Hashemkhani Zolfani & Ramin Bazrafshan & Fatih Ecer & Çağlar Karamaşa, 2022. "The Suitability-Feasibility-Acceptability Strategy Integrated with Bayesian BWM-MARCOS Methods to Determine the Optimal Lithium Battery Plant Located in South America," Mathematics, MDPI, vol. 10(14), pages 1-18, July.
    7. Paul, Ananna & Shukla, Nagesh & Trianni, Andrea, 2023. "Modelling supply chain sustainability challenges in the food processing sector amid the COVID-19 outbreak," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).
    8. Corrente, Salvatore & Greco, Salvatore & Rezaei, Jafar, 2024. "Better decisions with less cognitive load: The Parsimonious BWM," Omega, Elsevier, vol. 126(C).
    9. Vieira, Fabiana C. & Ferreira, Fernando A.F. & Govindan, Kannan & Ferreira, Neuza C.M.Q.F. & Banaitis, Audrius, 2022. "Measuring urban digitalization using cognitive mapping and the best worst method (BWM)," Technology in Society, Elsevier, vol. 71(C).
    10. Javid Nafari & Alireza Arab & Sina Ghaffari, 2017. "Through the Looking Glass: Analysis of Factors Influencing Iranian Student’s Study Abroad Motivations and Destination Choice," SAGE Open, , vol. 7(2), pages 21582440177, June.
    11. Mete, Suleyman & Yucesan, Melih & Gul, Muhammet & Ozceylan, Eren, 2023. "An integrated hybrid MCDM approach to evaluate countries’ COVID-19 risks," Socio-Economic Planning Sciences, Elsevier, vol. 90(C).
    12. Yang, Hua & Xu, Yong & Zhou, Kan & Li, Jiuyi, 2024. "Mapping development potential and priority zones for utility-scale photovoltaic on the Qinghai-Tibet Plateau," Renewable Energy, Elsevier, vol. 237(PA).
    13. Ulutaş Alptekin & Karaköy Çağatay, 2019. "An analysis of the logistics performance index of EU countries with an integrated MCDM model," Economics and Business Review, Sciendo, vol. 5(4), pages 49-69, December.
    14. Munim, Ziaul Haque & Duru, Okan & Ng, Adolf K.Y., 2022. "Transhipment port's competitiveness forecasting using analytic network process modelling," Transport Policy, Elsevier, vol. 124(C), pages 70-82.
    15. Changhua Liao & Qihui Lu & Ying Shui, 2022. "Governmental Anti-Pandemic and Subsidy Strategies for Blockchain-Enabled Food Supply Chains in the Post-Pandemic Era," Sustainability, MDPI, vol. 14(15), pages 1-23, August.
    16. Gul, Muhammet & Yucesan, Melih, 2022. "Performance evaluation of Turkish Universities by an integrated Bayesian BWM-TOPSIS model," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    17. Chong Li & He Huang & Ya Luo, 2022. "An Integrated Two-Dimension Linguistic Intuitionistic Fuzzy Decision-Making Approach for Unmanned Aerial Vehicle Supplier Selection," Sustainability, MDPI, vol. 14(18), pages 1-24, September.
    18. Mukesh Kumar & Rakesh D. Raut & Mahak Sharma & Vikas Kumar Choubey & Sanjoy Kumar Paul, 2022. "Enablers for resilience and pandemic preparedness in food supply chain," Operations Management Research, Springer, vol. 15(3), pages 1198-1223, December.
    19. Lin, Xueshan & Huang, Tao & Bompard, Ettore & Wang, Beibei & Zheng, Yaxian, 2023. "Ex-ante market power evaluation and mitigation in day-ahead electricity market considering market maturity levels," Energy, Elsevier, vol. 278(C).
    20. Göçmen Polat, Elifcan & Yücesan, Melih & Gül, Muhammet, 2023. "A comparative framework for criticality assessment of strategic raw materials in Turkey," Resources Policy, Elsevier, vol. 82(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:gam:jmathe:v:10:y:2022:i:19:p:3550-:d:928773. 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.