مطالعات مدیریت کسب و کار هوشمند

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، گروه مهندسی صنایع، دانشکده مدیریت و مهندسی صنایع، دانشگاه صنعتی مالک اشتر، تهران، ایران

2 استادیار، گروه مهندسی صنایع، دانشکده مدیریت و مهندسی صنایع، دانشگاه صنعتی مالک اشتر، تهران، ایران نویسنده مسئول: mabbasi@mut.ac.ir

3 استادیار، گروه مهندسی صنایع، دانشکده مدیریت و مهندسی صنایع، دانشگاه صنعتی مالک اشتر، تهران، ایران

4 استاد، گروه مهندسی صنایع، دانشگاه صنعتی قم، قم، ایران

چکیده

در عصر دیجیتال، فناوری بلاک چین به عنوان نوآوری عملیاتی شناخته شده است که به سرعت در حال پیوستن به زمینه زنجیره تامین و لجستیک بشردوستانه است. از این رو، تکنولوژی بلاکچین این پتانسیل را دارد که زمینه کمک های بشردوستانه را به طور اساسی تغییر دهد، اما هنوز تحقیقات نسبتاً کمی با هدف بهبود درک موانع مختلف پذیرش بلاک چین در لجستیک بشردوستانه منتشر شده است. هدف این تحقیق ارائه یک چارچوب یکپارچه جهت ارزیابی موانع پذیرش بلاک چین در زمینه لجستیک بشردوستانه است. برای تجزیه و تحلیل موانع از رویکرد یکپارچه روش FMEA مبتنی بر Z-ARAS در سه فاز استفاده شده است. در فاز اول این رویکرد بر اساس ادبیات، 10 مانع پذیرش بلاک چین در لجستیک بشردوستانه بر اساس روش FMEA شناسایی شده و عوامل تعیین کننده RPN مقدار دهی می شوند. در فاز دوم، با بهره گیری از نظرات خبرگان، وزن های عوامل سه گانه محاسبه می شوند. سپس در فاز سوم، با توجه به خروجی های فاز های قبل، موانع با استفاده از روش پیشنهادی Z-ARAS با در نظر گرفتن عدم قطعیت و قابلیت اطمینان اولویت بندی می شوند. رویکرد پیشنهادی این تحقیق در ارزیابی موانع پیاده سازی بلاک چین در لجستیک بشردوستانه پیاده سازی گردید و بر اساس نتایج، مشکلات یکپارچه سازی، ریسک حملات سایبری و ریسک های فناوری به عنوان موانع مهم و بحرانی شناسایی شده اند. نتایج حاصل از رویکرد پیشنهادی نشانگر قابلیت و برتری آن در مقایسه با سایر روش ها نظیر FMEA و آراس فازی بوده است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Barriers to blockchain adoption in humanitarian logistics in an uncertain environment

نویسندگان [English]

  • Ali Memarpour Ghiaci 1
  • Morteza Abbasi 2
  • Morteza Piri 3
  • Peyman Akhavan 4

1 Industrial Engineering Department, Malek Ashtar University of Technology, Tehran 1774-15875, Iran Ph.D Student, Industrial Engineering Department, Malek Ashtar University of Technology, Tehran 1774-15875, Iran

2 Assistant Professor, Industrial Engineering Department, Malek Ashtar University of Technology, Tehran 1774-15875, Iran, Iran Corresponding Author: mabbasi@mut.ac.ir

3 Assistant Professor, Industrial Engineering Department, Malek Ashtar University of Technology, Tehran 1774-15875, Iran

4 Professor, Faculty of Industrial Engineering, Qom University of Technology, Qom, Iran

چکیده [English]

Abstract
In the digital age, blockchain technology is recognized as an operational innovation that is rapidly joining the field of supply chain and humanitarian logistics. Hence, blockchain technology has the potential to fundamentally change the field of humanitarian aid, but still relatively little research has been published aimed at improving understanding of the various barriers to blockchain adoption in humanitarian logistics. The aim of this research is to provide an integrated framework for evaluating the barriers to blockchain adoption in the field of humanitarian logistics. To assess the barriers, integrated approach has been applied in three phases. In the first phase of this approach, based on the literature, 10 barriers to the adoption of blockchain in humanitarian logistics are identified and evaluated using the FMEA method. In the second phase, using the opinions of experts, the weights of the three factors are calculated. Then, in the third phase and according to the outputs of the previous phases, obstacles are prioritized using the proposed Z-ARAS method. In addition to assigning different weights to the three factors considering uncertainty and reliability in barriers is also considered in this approach through the theory of Z numbers. The proposed approach of current study was implemented in the evaluation of blockchain adoption barriers in humanitarian logistics. According to the results, the most critical barriers concern with integrating issues, risk of cyber-attacks, and technology risks. The results shown the capability and superiority of the proposed approach compared to other traditional methods such as FMEA and Fuzzy ARAS.
Introduction
In the context of the Fourth Industrial Revolution, advanced technologies are reshaping production and business models across various industries, offering new opportunities for enhanced competitiveness but also introducing challenges in terms of adoption and optimization (Wong et al., 2020; Khan et al., 2021). Notably, the convergence of advanced technology and humanitarian logistics is crucial, especially in addressing natural and man-made disasters (Ar et al., 2020; Dubey et al., 2020). This necessitates effective management and the combination of humanitarian logistics with blockchain technology, although this integration comes with multifaceted challenges (Baharmand et al., 2021).
To address these challenges, we explore the Failure Modes and Effects Analysis (FMEA) method as a systematic approach to identify and assess barriers and risks. Traditional FMEA approaches rely on subjective evaluations, which introduce uncertainty into the results. In this context, our research aims to introduce an innovative approach that addresses these limitations by integrating the ARAS method and Z-numbers theory. This approach allows for more reliable prioritization of barriers related to blockchain technology adoption in humanitarian logistics, enhancing the robustness and effectiveness of decision-making processes. In this extended abstract, we present our method and compare its outcomes with traditional approaches to prioritize barriers and risks in blockchain technology adoption within humanitarian logistics. Also, the barriers to blockchain technology adoption in humanitarian logistics and how to prioritize these barriers are among the main research questions.

Literature Review

Blockchain technology is gaining traction in supply chains due to its diverse applications and unique advantages. As supply chains face increasing disruptions, blockchain technology adoption can address challenges and enhance performance (Akhavan & Philsoophian, 2022; Hald & Kinra, 2019). Blockchain structures data into interconnected blocks, ensuring the security and transparency of transactions (Akhavan & Namvar, 2021; Azizi et al., 2021). Blockchain technology is appealing for supply chains due to four main characteristics: encouraging data sharing, minimizing fraudulent transactions, ensuring data immutability, and providing asset security (Babich & Hilary, 2020; Cole, Stevenson, & Aitken, 2019; Rahimi, Akhavan, Philsofian, & Darabi, 2022).
Research on blockchain applications in humanitarian logistics primarily focuses on motivations, such as improved collaboration, transparency, trust, cost reduction, intermediary removal, and shared participation (Baharmand, Maghsoudi, et al., 2021; Seyedsayamdost & Vanderwal, 2020). However, more research is needed in this area (Sahebi, Masoomi, & Ghorbani, 2020). Existing studies have identified barriers to blockchain adoption in humanitarian supply chains, including financial constraints, senior management support, organizational readiness, technological complexity, infrastructure, technology compatibility, and regulatory issues (Baharmand & Comes, 2019).
Multi-criteria decision-making methods (MCDM) have been used to improve FMEA's performance (Ghoushchi et al., 2021; Ghoushchi et al., 2022). These approaches often combine FMEA with methods like GRA, BWM, TOPSIS, and AHP in various fuzzy environments. Such integrated methods have been proposed for barrer identification in the context of blockchain adoption (Li, Li, Sun, & Wang, 2018; Lo & Liou, 2018; Kolios, Umofia, & Shafiee, 2017; Carpitella, Certa, Izquierdo, & La Fata, 2018; Sayyadi Tooranloo & Ayatollah, 2017). Additionally, unified methods like MOORA have been applied to address specific challenges in different contexts (Jafarzadeh Ghoushchi, Memarpour Ghiaci, et al., 2022).
The literature indicates a gap in research on blockchain applications in humanitarian logistics, as most studies focus on business supply chains. Using insights from business supply chains to inform decisions in humanitarian logistics can be misleading, given their fundamental differences (Baharmand, Saeed, Comes, & Lauras, 2021). Consequently, this study aims to address these gaps by proposing an extended FMEA approach based on MCDM methods to identify and prioritize barriers to blockchain adoption in humanitarian logistics, using Z-numbers theory.

Methodology

The proposed approach of this research is presented, utilizing FMEA and Z-ARAS methods for barrier assessment. The proposed approach consists of three phases. In the first phase, barriers are identified, and the values of the criteria are scored by the FMEA team using linguistic variables from Z-number theory. In the second phase, considering the differences in the importance of criteria, the weight of each criterion is determined based on expert opinions as triangular fuzzy numbers. In the third phase, based on the results of the first and second phases, barrier prioritization is performed while taking into account the criterion weights, using the Z-ARAS method. Unlike the conventional fuzzy ARAS method, the Z-ARAS method can consider uncertainty and reliability for each criterion concerning the options. In this method, after determining the decision matrix, which comprises fuzzy numbers and reliability values (Z-numbers), these values are transformed into triangular fuzzy numbers, and then the Z-ARAS method is executed.

Conclusion

Humanitarian logistics is a relatively new area of research. The impact of humanitarian logistics is crucial, as it saves lives and improves conditions. Research has shown that effective humanitarian logistics is a key driver for the performance of humanitarian organizations. Currently, there exists a significant gap in humanitarian logistics research, particularly in developing countries, between theoretical research and practical implementation.
The adoption of blockchain technology will play a pivotal role in the future development of humanitarian logistics. Therefore, the identification and prioritization of barriers to adopting blockchain technology in humanitarian logistics have gained increasing importance. In this study, an enhanced approach to FMEA is proposed using the Z-ARAS method. Based on the results obtained, "Integration Issues," "Cybersecurity Risks," and "Technology Risks" have been chosen as critical barriers to blockchain technology adoption in humanitarian logistics and are given priority for mitigation and resource allocation. The use of this enhanced approach has addressed some of the limitations of the conventional FMEA method, such as not providing a complete ranking of options. While the developed FMEA approach using the Z-ARAS method is a promising and reliable method, it has limitations. This model may be complex for decision-makers, and it is expected that software tools will be developed to assist decision-makers using this enhanced approach. Additionally, the interaction and impact of barriers were not discussed in this study. Future work can analyze the interplay between barriers to identify critical barriers. Furthermore, researchers can consider multi-criteria decision-making methods like PIPRECIA, SWARA, BWM, and others to determine the importance and weights of criteria. Developing the FMEA method using multi-criteria decision-making methods such as MARCOS, EDAS, CoCoSo, and others for ranking barriers in uncertain environments, including pythagorean, q-rung, and spherical fuzzy scenarios, is also suggested for future studies. Regardless of the issue used for implementing the proposed approach in this research, this approach can be applied to identify and analyze risks and failure modes in various scenarios..
Keywords: Blockchain, Humanitarian logistics, FMEA, Multi-criteria decision-making, Z-number theory.
 
 

کلیدواژه‌ها [English]

  • Blockchain
  • Humanitarian logistics
  • FMEA
  • Multi-criteria decision-making
  • Z-number theory
Akhavan, P., & Namvar, M. (2021). The mediating role of blockchain technology in improvement of knowledge sharing for supply chain management. Management Decision.
Akhavan, P., & Philsoophian, M. (2022). Improving of Supply Chain Collaboration and Performance by Using Block Chain Technology as a Mediating Role and Resilience as a Moderating Variable. Journal of the Knowledge Economy, 1-22.
Ar, I. M., Erol, I., Peker, I., Ozdemir, A. I., Medeni, T. D., & Medeni, I. T. (2020). Evaluating the feasibility of blockchain in logistics operations: A decision framework. Expert Systems with applications, 158, 113543.
Azizi, N., Malekzadeh, H., Akhavan, P., Haass, O., Saremi, S., & Mirjalili, S. (2021). IoT–blockchain: Harnessing the power of internet of thing and blockchain for smart supply chain. Sensors, 21(18), 6048.
Babich, V., & Hilary, G. (2020). OM Forum—Distributed ledgers and operations: What operations management researchers should know about blockchain technology. Manufacturing & Service Operations Management, 22(2), 223-240.
Baharmand, H., & Comes, T. (2019). Leveraging partnerships with logistics service providers in humanitarian supply chains by blockchain-based smart contracts. IFAC-PapersOnLine, 52(13), 12-17.
Baharmand, H., Maghsoudi, A., & Coppi, G. (2021). Exploring the application of blockchain to humanitarian supply chains: insights from Humanitarian Supply Blockchain pilot project. International Journal of Operations & Production Management.
Baharmand, H., Saeed, N., Comes, T., & Lauras, M. (2021). Developing a framework for designing humanitarian blockchain projects. Computers in Industry, 131, 103487.
Bashir, I. (2017). Mastering blockchain: Packt Publishing Ltd.
Böhme, R. (2013). Internet protocol adoption: Learning from Bitcoin. Paper presented at the IAB Workshop on Internet Technology Adoption and Transition (ITAT).
Böhme, R., Christin, N., Edelman, B., & Moore, T. (2015). Bitcoin: Economics, technology, and governance. Journal of economic Perspectives, 29(2), 213-238.
Brezo, F., & Bringas, P. G. (2012). Issues and risks associated with cryptocurrencies such as Bitcoin.
Carpitella, S., Certa, A., Izquierdo, J., & La Fata, C. M. (2018). A combined multi-criteria approach to support FMECA analyses: A real-world case. Reliability Engineering & System Safety, 169, 394-402.
Cole, R., Stevenson, M., & Aitken, J. (2019). Blockchain technology: implications for operations and supply chain management. Supply Chain Management: An International Journal.
Demir, E., Bilgin, M. H., Karabulut, G., & Doker, A. C. (2020). The relationship between cryptocurrencies and COVID-19 pandemic. Eurasian Economic Review, 10(3), 349-360.
Dubey, R., Gunasekaran, A., Bryde, D. J., Dwivedi, Y. K., & Papadopoulos, T. (2020). Blockchain technology for enhancing swift-trust, collaboration and resilience within a humanitarian supply chain setting. International Journal of Production Research, 58(11), 3381-3398.
Gencer, A. E. (2017). On scalability of blockchain technologies. Cornell University,
Gervais, A., Karame, G. O., Wüst, K., Glykantzis, V., Ritzdorf, H., & Capkun, S. (2016). On the security and performance of proof of work blockchains. Paper presented at the Proceedings of the 2016 ACM SIGSAC conference on computer and communications security.
Gheibdoust, H., Gilaninia, S., & Taleghani, M. (2023). Identification and Prioritization of the Factors Influencing Service Quality in the Hotel Industry by SWARA and ARAS Methods During the COVID-19 Pandemic. Journal of Quality Assurance in Hospitality & Tourism, 1-23.
Ghiaci, A. M., & Ghoushchi, S. J. (2023). Assessment of barriers to IoT-enabled circular economy using an extended decision-making-based FMEA model under uncertain environment. Internet of Things, 100719.
Ghoushchi, S. J., Bonab, S. R., Ghiaci, A. M., Haseli, G., Tomaskova, H., & Hajiaghaei-Keshteli, M. (2021). Landfill Site Selection for Medical Waste Using an Integrated SWARA-WASPAS Framework Based on Spherical Fuzzy Set. Sustainability, 13(24), 13950.
Ghoushchi, S. J., Jalalat, S. M., Bonab, S. R., Ghiaci, A. M., Haseli, G., & Tomaskova, H. (2022). Evaluation of wind turbine failure modes using the developed SWARA-CoCoSo methods based on the spherical fuzzy environment. IEEE Access, 10, 86750-86764.
Ghoushchi, S. J., Yousefi, S., & Khazaeili, M. (2019). An extended FMEA approach based on the Z-MOORA and fuzzy BWM for prioritization of failures. Applied Soft Computing, 81, 105505.
Glaser, F., Zimmermann, K., Haferkorn, M., Weber, M. C., & Siering, M. (2014). Bitcoin-asset or currency? revealing users' hidden intentions. Revealing Users' Hidden Intentions (April 15, 2014). ECIS.
Gurtu, A., & Johny, J. (2019). Potential of blockchain technology in supply chain management: a literature review. International Journal of Physical Distribution & Logistics Management.
Hald, K. S., & Kinra, A. (2019). How the blockchain enables and constrains supply chain performance. International Journal of Physical Distribution & Logistics Management.
Ivanov, D., & Dolgui, A. (2020). Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak. International Journal of Production Research, 58(10), 2904-2915.
Ivanov, D., & Dolgui, A. (2021). A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0. Production Planning & Control, 32(9), 775-788.
Jafarzadeh Ghoushchi, S., Bonab, S. R., & Ghiaci, A. M. (2023). A decision-making framework for COVID-19 infodemic management strategies evaluation in spherical fuzzy environment. Stochastic environmental research and risk assessment, 1-14.
Jafarzadeh Ghoushchi, S., Memarpour Ghiaci, A., Rahnamay Bonab, S., & Ranjbarzadeh, R. (2022). Barriers to circular economy implementation in designing of sustainable medical waste management systems using a new extended decision-making and FMEA models. Environmental Science and Pollution Research, 1-19.
Jafarzadeh Ghoushchi, S., Shaffiee Haghshenas, S., Memarpour Ghiaci, A., Guido, G., & Vitale, A. (2022). Road safety assessment and risks prioritization using an integrated SWARA and MARCOS approach under spherical fuzzy environment. Neural Computing and Applications, 1-19.
Khadke, U., & Parkhi, S. (2020). Implementation of blockchain in the humanitarian supply chainbenefits and blockades. Psychology and Education Journal, 57(9), 5098-5105.
Khan, M., Imtiaz, S., Parvaiz, G. S., Hussain, A., & Bae, J. (2021). Integration of internet-of-things with blockchain technology to enhance humanitarian logistics performance. IEEE Access, 9, 25422-25436.
Kolios, A. J., Umofia, A., & Shafiee, M. (2017). Failure mode and effects analysis using a fuzzy-TOPSIS method: a case study of subsea control module. International Journal of Multicriteria Decision Making, 7(1), 29-53.
Krombholz, K., Judmayer, A., Gusenbauer, M., & Weippl, E. (2016). The other side of the coin: User experiences with bitcoin security and privacy. Paper presented at the International conference on financial cryptography and data security.
Li, X., Li, H., Sun, B., & Wang, F. (2018). Assessing information security risk for an evolving smart city based on fuzzy and grey FMEA. Journal of Intelligent & Fuzzy Systems, 34(4), 2491-2501.
Lo, H.-W., & Liou, J. J. (2018). A novel multiple-criteria decision-making-based FMEA model for risk assessment. Applied Soft Computing, 73, 684-696.
Memarpour Ghiaci, A., Garg, H., & Jafarzadeh Ghoushchi, S. (2022). Improving emergency departments during COVID-19 pandemic: a simulation and MCDM approach with MARCOS methodology in an uncertain environment. Computational and Applied Mathematics, 41(8), 1-23.
Patil, A., Shardeo, V., Dwivedi, A., & Madaan, J. (2020). An integrated approach to model the blockchain implementation barriers in humanitarian supply chain. Journal of Global Operations and Strategic Sourcing.
Queiroz, M. M., Telles, R., & Bonilla, S. H. (2019). Blockchain and supply chain management integration: a systematic review of the literature. Supply Chain Management: An International Journal.
Rahimi, A., Akhavan, P., Philsofian, M., & Darabi, A. (2022). Investigating the Effect of using Blockchain Technology on Collaborative Interactions and Performance Improvement in the Defense Industry Supply Chain. Journal of Industrial Management Perspective, 12(1, Spring 2022), 109-134.
Sahebi, I. G., Masoomi, B., & Ghorbani, S. (2020). Expert oriented approach for analyzing the blockchain adoption barriers in humanitarian supply chain. Technology in Society, 63, 101427.
Sayyadi Tooranloo, H., & Ayatollah, A. S. (2017). Pathology the internet banking service quality using failure mode and effect analysis in interval-valued intuitionistic fuzzy environment. International Journal of Fuzzy Systems, 19(1), 109-123.
Schatsky, D., Arora, A., & Dongre, A. (2018). Blockchain and the five vectors of progress. Recuperado de https://www2. deloitte. com/us/en/insights/focus/signals-for-strategists/value-of-blockchain-applications-interoperability. html.
Seebacher, S., & Maleshkova, M. (2018). A model-driven approach for the description of blockchain business networks. Paper presented at the Proceedings of the 51st Hawaii international conference on system sciences.
Seyedsayamdost, E., & Vanderwal, P. (2020). From good governance to governance for good: blockchain for social impact. Journal of International Development, 32(6), 943-960.
Treiblmaier, H. (2021). Blockchain and crisis management. In: 2nd international conference on knowledge management, Blockchain and Economy.
Urquhart, A. (2016). The inefficiency of Bitcoin. Economics Letters, 148, 80-82.
Van Alstyne, M. (2014). Why Bitcoin has value. Communications of the ACM, 57(5), 30-32.
Waller, M. A., Van Hoek, R., Davletshin, M., & Fugate, B. (2019). Integrating blockchain into supply chain management: a toolkit for practical implementation: Kogan Page Publishers.
Wang, Y., Han, J. H., & Beynon-Davies, P. (2018). Understanding blockchain technology for future supply chains: a systematic literature review and research agenda. Supply Chain Management: An International Journal.
Wiatt, R. G. (2019). From the mainframe to the blockchain. Strategic Finance, 100(7), 26-35.
Wong, L.-W., Tan, G. W.-H., Lee, V.-H., Ooi, K.-B., & Sohal, A. (2020). Unearthing the determinants of Blockchain adoption in supply chain management. International Journal of Production Research, 58(7), 2100-2123.
Zavadskas, E. K., & Turskis, Z. (2010). A new additive ratio assessment (ARAS) method in multicriteria decision‐making. Technological and economic development of economy, 16(2), 159-172.
Zohar, A. (2015). Bitcoin: under the hood. Communications of the ACM, 58(9), 104-113.