Governments use online platforms to keep track of transactions in the supply chain (SC) of subsidized foods to prevent fraud. Although regular checks of warehouses and documents were conducted, current platforms failed to resolve the issue. Blockchain technology (BT) provides governments with the ability to access transparent and real-time data to address these challenges. In this paper, we examine the key challenges influencing the implementation of a BT platform for managing subsidized food products in Iran. The barriers appear to be interconnected. We present a model that integrates the Best-Worst method (BWM) for obtaining independent weights and the Weighted Influence Non-Linear Gauge System (WINGS) using a rescaling scheme for considering the interrelatedness between the criteria. Expert opinions and literature reviews are used to identify critical factors. According to the findings, the costs of implementing and maintaining the system, as well as the regular restructuring of government rules regarding the data to be collected, are the two main challenges of implementing this new technology. Moreover, there are concerns about the cooperation with downstream entities of SC, cultural differences among partners, and their knowledge level, which may affect the complexity of downstream implementation. The results of sensitivity analysis show that WINGS gives greater weight to factors that have more impact on others. Conversely, the weight of factors that are interwoven with other factors and factors that aren't influenced by other factors is reduced as compared to the independent relative importance obtained from BWM.
[1] Zhang, H., An, R., & Zhong, Q. (2019). Anti-corruption, government subsidies, and investment efficiency. China Journal of Accounting Research, 12(1), 113-133.
[2] Singh, B., Sharma, K. P., Sharma, N., & Kumar, P. (2021). Blockchain-based Claim Verification and Approval System for Disbursing Fertilizer Subsidy. Available at SSRN 3814155.
[3] Adewuyi, A. (2020). Challenges and prospects of renewable energy in Nigeria: A case of bioethanol and biodiesel production. Energy Reports, 6, 77-88.
[4] Bala, K., & Kaur, P. D. (2022). Transparent subsidized agri‐product distribution during pandemics with reputation based PoA blockchain. Concurrency and Computation: Practice and Experience, e6863.
[5] Farajzadeh, Z., & Bakhshoodeh, M. (2015). Economic and environmental analyses of Iranian energy subsidy reform using Computable General Equilibrium (CGE) model. Energy for Sustainable Development, 27, 147-154.
[6] Barkhordar, Z. A., Fakouriyan, S., & Sheykhha, S. (2018). The role of energy subsidy reform in energy efficiency enhancement: Lessons learnt and future potential for Iranian industries. Journal of Cleaner Production, 197, 542-550.
[7] Khalilian, S., & Yuzbashkandi, S. S. (2021). Analysis of vegetable oil demand and its price reform in Iran: using rural and urban household level data. International Journal of Agriculture Environment and Food Sciences, 5(1), 122-132.
[8] Mosavi, S. H. (2016). Energy price reform and food markets: The case of bread supply chain in Iran. Agricultural Economics, 47(2), 169-179.
[9] 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.
[10] Wang, B., Luo, W., Zhang, A., Tian, Z., & Li, Z. (2020). Blockchain-enabled circular supply chain management: A system architecture for fast fashion. Computers in Industry, 123, 103324.
[11] Cole, R., Stevenson, M. and Aitken, J. (2019), Blockchain technology: implications for operations and supply chain management, Supply Chain Management, 24 (4), 469-483.
[12] Kouhizadeh, M., Saberi, S., & Sarkis, J. (2021). Blockchain technology and the sustainable supply chain: Theoretically exploring adoption barriers. International Journal of Production Economics, 231, 107831.
[13] Kamble, S. S., Gunasekaran, A., & Sharma, R. (2020). Modeling the blockchain enabled traceability in agriculture supply chain. International Journal of Information Management, 52, 101967.
[14] Irannezhad, M., Shokouhyar, S., Ahmadi, S., & Papageorgiou, E. I. (2021). An integrated FCM-FBWM approach to assess and manage the readiness for blockchain incorporation in the supply chain. Applied Soft Computing, 112, 107832.
[15] Sternberg, H. S., Hofmann, E., & Roeck, D. (2021). The struggle is real: insights from a supply chain blockchain case. Journal of Business Logistics, 42(1), 71-87.
[16] Nguyen, S., Chen, P. S. L., & Du, Y. (2020). Risk identification and modeling for blockchain-enabled container shipping. International Journal of Physical Distribution & Logistics Management.
[17] Kshetri, N. (2021). Blockchain and sustainable supply chain management in developing countries. International Journal of Information Management, 60, 102376.
[18] Farooque, M., Jain, V., Zhang, A., & Li, Z. (2020). Fuzzy DEMATEL analysis of barriers to Blockchain-based life cycle assessment in China. Computers & Industrial Engineering, 147, 106684.
[19] Ali, M. H., Chung, L., Kumar, A., Zailani, S., & Tan, K. H. (2021). A sustainable Blockchain framework for the halal food supply chain: Lessons from Malaysia. Technological Forecasting and Social Change, 170, 120870.
[20] Budak, A., & Çoban, V. (2021). Evaluation of the impact of blockchain technology on supply chain using cognitive maps. Expert Systems with Applications, 184, 115455.
[21] Bamakan, S. M. H., Moghaddam, S. G., & Manshadi, S. D. (2021). Blockchain-enabled pharmaceutical cold chain: applications, key challenges, and future trends. Journal of Cleaner Production, 302, 127021.
[22] Özkan, B., Kaya, İ., Erdoğan, M., & Karaşan, A. (2019, July). Evaluating blockchain risks by using a MCDM methodology based on pythagorean fuzzy sets. In International conference on intelligent and fuzzy systems (pp. 935-943). Springer, Cham.
[23] Prewett, K. W., Prescott, G. , & Phillips, K. (2020). Blockchain adoption is inevitable—Barriers and risks remain. Journal of Corporate accounting & finance, 31(2), 21-28.
[24] Vafadarnikjoo, A., Badri Ahmadi, H., Liou, J. J., Botelho, T., & Chalvatzis, K. (2021). Analyzing blockchain adoption barriers in manufacturing supply chains by the neutrosophic analytic hierarchy process. Annals of Operations Research, 1-28.
[25] Mathivathanan, , Mathiyazhagan, K., Rana, N. P., Khorana, S., & Dwivedi, Y. K. (2021). Barriers to the adoption of blockchain technology in business supply chains: a total interpretive structural modelling (TISM) approach. International Journal of Production Research, 59(11), 3338-3359.
[26] Etemadi, N., Van Gelder, P., & Strozzi, F. (2021). An ism modeling of barriers for blockchain/distributed ledger technology adoption in supply chains towards cybersecurity. Sustainability, 13(9), 4672.
[27] 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.
[28] Baharmand, H., Saeed, N., Comes, T., & Lauras, M. (2021). Developing a framework for designing humanitarian blockchain projects. Computers in Industry, 131, 103487.
[29] Friedman, N., & Ormiston, J. (2022). Blockchain as a sustainability-oriented innovation?: Opportunities for and resistance to Blockchain technology as a driver of sustainability in global food supply chains. Technological Forecasting and Social Change, 175, 121403.
[30] Biswas, B., & Gupta, R. (2019). Analysis of barriers to implement blockchain in industry and service sectors. Computers & Industrial Engineering, 136, 225-241.
[32] Mi, X., Tang, M., Liao, H., Shen, W., & Lev, B. (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, 87, 205-225.
[33] Michnik, J., & Adamus-Matuszyńska, A. (2015). Structural analysis of problems in public relations. Multiple Criteria Decision Making, (10), 105-123.
[34] Banaś, D., & Michnik, J. (2019). Evaluation of the Impact of Strategic Offers on the Financial and Strategic Health of the Company—A Soft System Dynamics Approach. Mathematics, 7(2), 208.
[35] Michnik, J., & Grabowski, A. (2020). Modeling Uncertainty in the Wings Method Using Interval Arithmetic. International Journal of Information Technology & Decision Making, 19(01), 221–240.
[36] Kaviani, M.A., Tavana, M., Kumar, A., Michnik, J., Niknam, R. and Campos, E.A.R. (2020). An Integrated Framework for Evaluating the Barriers to Successful Implementation of Reverse Logistics in the Automotive Industry. Journal of Cleaner Production, https://doi.org/10.1016/j.jclepro.2020.122714.
[37] Wang, W., Tian, , Xi, W., Tan, Y. R., & Deng, Y. (2020). The influencing factors of China’s green building development: An analysis using RBF-WINGS method. Building and Environment, 107425.
[38] Tavana, M., Mousavi, H., Nasr, A. K., & Mina, H. (2021a). A Fuzzy Weighted Influence Non-linear Gauge System with Application to Advanced Technology Assessment at NASA. Expert Systems with Applications, In press.
[39] Michnik, J. (2013). Weighted Influence Non-linear Gauge System (WINGS)–An analysis method for the systems of interrelated components. European Journal of Operational Research, 228(3), 536-544.
[40] Tavana, M., Nasr, A. K., Mina, H., & Michnik, J. (2021b). A private sustainable partner selection model for green public-private partnerships and regional economic development. Socio-Economic Planning Sciences, 101189.
[41] Bozorgi-Amiri, A., Ranjbar, A., & Jamali, A. (2019). A Novel Hybrid MCDM Method for Optimal Location Selection of Free Trade Zones, Case Study: Mazandaran Province. Advances in Industrial Engineering, 53(3), 79-92.
[42] Barzinpour, F., & Karimi, S. (2014). Forecasting Effects of Scenarios of Subsides Removal on Residential Electricity Consumption by Artificial Neural Networks. Advances in Industrial Engineering, 48(Special Issue), 83-90.
Shamekhi Amiri, A., & Manavizadeh, N. (2022). Evaluating Barriers of Blockchain-Based Platforms Implementation for Subsidized Foods Supply Chains: A Hybrid Approach Based on BWM and WINGS Methods. Advances in Industrial Engineering, 56(2), 199-214. doi: 10.22059/aie.2022.341963.1834
MLA
Alireza Shamekhi Amiri; Neda Manavizadeh. "Evaluating Barriers of Blockchain-Based Platforms Implementation for Subsidized Foods Supply Chains: A Hybrid Approach Based on BWM and WINGS Methods", Advances in Industrial Engineering, 56, 2, 2022, 199-214. doi: 10.22059/aie.2022.341963.1834
HARVARD
Shamekhi Amiri, A., Manavizadeh, N. (2022). 'Evaluating Barriers of Blockchain-Based Platforms Implementation for Subsidized Foods Supply Chains: A Hybrid Approach Based on BWM and WINGS Methods', Advances in Industrial Engineering, 56(2), pp. 199-214. doi: 10.22059/aie.2022.341963.1834
VANCOUVER
Shamekhi Amiri, A., Manavizadeh, N. Evaluating Barriers of Blockchain-Based Platforms Implementation for Subsidized Foods Supply Chains: A Hybrid Approach Based on BWM and WINGS Methods. Advances in Industrial Engineering, 2022; 56(2): 199-214. doi: 10.22059/aie.2022.341963.1834