Investigating Strategies for Implementing Resilience Based on Industry 4.0 in the Electricity Supply Chain: A Combination of Soft and Hard Operational Research

Journal of Systems Thinking in Practice

دوره 3، شماره 2 - شماره پیاپی 9، شهریور 2024، صفحه 112-135   

نوع مقاله: Research Article

شناسه دیجیتال (DOI): 10.22067/jstinp.2024.86761.1092

نویسندگان

Mahmoud Reza Shahipasand¹؛ Nazanin Pilevari^{* 2}؛ Ali Rajabzadeh³

¹Industrial Management Department, Faculty of Management and Economics, Islamic Azad University, Science and Research branch, Tehran, Iran.

²Industrial Management Department, Faculty of Management and Economics, Islamic Azad University, West Tehran Branch, Tehran, Iran.

³Industrial Management Department, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran.

چکیده

Resilience is one of the most crucial parameters in the electricity supply chain, and the absence of this concept can lead to various issues in service provision. One perspective that can greatly contribute to resilience is utilizing the Industry 4.0 approach. This study examines the challenges and strategies for the flexibility of the electricity supply chain in the Industry 4.0 era. A descriptive-analytical method employing library research and field studies has been employed. Subsequently, using the factors and criteria obtained from Value-Focused Thinking (VFT) from Soft Operational Research and verification by literature, a fuzzy IVIF-WASPAS-based analysis was conducted. The decision-making team comprised internal experts in the electricity supply chain in Iran, focusing on the principles of resilience in the Industry 4.0 era to analyze key issues. A case study was also conducted within the electricity supply chain, incorporating insights from academic experts and the team's experiences. Strategies like smart network systems, blockchain technology, cybersecurity, and education are fundamental to enhancing the supply chain's flexibility. This study's findings indicate a long journey in developing Industry 4.0 in Iran's electricity supply chain. However, relying on the proposed strategies can minimize existing issues and propel the system toward growth.

کلیدواژه‌ها

Electricity supply chain؛ Industry 4.0؛ Fuzzy IVIF-WASPAS؛ Value-Focused thinking؛ Soft operational research

مراجع

 Ahmad, T., Zhu, H., Zhang, D., Tariq, R., Bassam, A., Ullah, F., AlGhamdi, A.S. and Alshamrani, S.S., 2022. Energetics Systems and artificial intelligence: Applications of industry 4.0. Energy Reports, 8, pp.334-361. https://doi.org/10.1016/j.egyr.2021.11.256.

Aityassine, F., Soumadi, M., Aldiabat, B., Al-Shorman, H., Akour, I., Alshurideh, M. and Al-Hawary, S., 2022. The effect of supply chain resilience on supply chain performance of chemical industrial companies. Uncertain Supply Chain Management, 10(4), pp.1271-1278.

Alamerew, Y.A. and Brissaud, D., 2020. Modelling reverse supply chain through system dynamics for realizing the transition towards the circular economy: A case study on electric vehicle batteries. Journal of Cleaner Production, 254, p.120025. https://doi.org/10.1016/j.jclepro.2020.120025.

BAS, E. 2013. The integrated framework for analysis of electricity supply chain using an integrated SWOT-fuzzy TOPSIS methodology combined with AHP: The case of Turkey. International journal of electrical power & energy systems, 44, 897-907. https://doi.org/10.1016/j.ijepes.2012.08.045.

Borazon, E.Q., Huang, Y.C. and Liu, J.M., 2022. Green market orientation and organizational performance in Taiwan’s electric and electronic industry: the mediating role of green supply chain management capability. Journal of Business & Industrial Marketing, 37(7), pp.1475-1496. https://doi.org/10.1108/JBIM-07-2020-0321.

Bressanelli, G., Pigosso, D.C., Saccani, N. and Perona, M., 2021. Enablers, levers and benefits of Circular Economy in the Electrical and Electronic Equipment supply chain: A literature review. Journal of Cleaner Production, 298, p.126819. https://doi.org/10.1016/j.jclepro.2021.126819.

Chen, W., Zhao, Q., Quayson, M., Du, H. and Wang, H., 2023. Electricity Quality Emergency Investment with a Bargaining Contract in the Electricity Supply Chain under the COVID-19 Pandemic. Emerging Markets Finance and Trade, 59(8), pp.2398-2421. https://doi.org/10.1080/1540496X.2022.2108317.

Chen, Z. and Fan, Z.P., 2023. Improvement strategies of battery driving range in an electric vehicle supply chain considering subsidy threshold and cost misreporting. Annals of Operations Research, 326(1), pp.89-113. https://doi.org/10.1007/s10479-020-03792-5.

Dubey, R., Bryde, D.J., Dwivedi, Y.K., Graham, G., Foropon, C. and Papadopoulos, T., 2023. Dynamic digital capabilities and supply chain resilience: The role of government effectiveness. International Journal of Production Economics, 258, p.108790. https://doi.org/10.1016/j.ijpe.2023.108790.

Gao, H., Chen, Y., Mei, S., Huang, S. and Xu, Y., 2017. Resilience-oriented pre-hurricane resource allocation in distribution systems considering electric buses. Proceedings of the IEEE, 105(7), pp.1214-1233. https://doi.org/10.1109/JPROC.2017.2666548.

Hosseini-Motlagh, S.M., Samani, M.R.G. and Shahbazbegian, V., 2020. Innovative strategy to design a mixed resilient-sustainable electricity supply chain network under uncertainty. Applied Energy, 280, p.115921. https://doi.org/10.1016/j.apenergy.2020.115921.

Hosseini, S., Ivanov, D. and Dolgui, A., 2019. Review of quantitative methods for supply chain resilience analysis. Transportation research part E: logistics and transportation review, 125, pp.285-307. https://doi.org/10.1016/j.tre.2019.03.001.

Ilbahar, E., 2022. Drought Vulnerability Assessment Based on IVIF AHP and IVIF WASPAS: A Case Study in Turkey. In Multi-Criteria Decision Analysis (pp. 107-121). CRC Press.

Khazaei, M., Hajiaghaei-Keshteli, M., Rajabzadeh Ghatari, A., Ramezani, M., Fooladvand, A. and Azar, A., 2023. A multi-criteria supplier evaluation and selection model without reducing the level of optimality. Soft Computing, 27(22), pp.17175-17188. https://doi.org/10.1007/s00500-023-08954-8.

Kimani, K., Oduol, V. and Langat, K., 2019. Cyber security challenges for IoT-based smart grid networks. International journal of critical infrastructure protection, 25, pp.36-49. https://doi.org/10.1016/j.ijcip.2019.01.001.

Lahtinen, E., Kivijärvi, L., Tatikonda, R., Väisänen, A., Rissanen, K. and Haukka, M., 2017. Selective recovery of gold from electronic waste using 3D-printed scavenger. ACS omega, 2(10), pp.7299-7304. https://doi.org/10.1021/acsomega.7b01215.

Lasi, H., Fettke, P., Kemper, H.G., Feld, T. and Hoffmann, M., 2014. Industry 4.0. Business & information systems engineering, 6, pp.239-242. https://doi.org/10.1007/s12599-014-0334-4.

Lotfi, R., Kargar, B., Rajabzadeh, M., Hesabi, F. and Özceylan, E., 2022. Hybrid fuzzy and data-driven robust optimization for resilience and sustainable health care supply chain with vendor-managed inventory approach. International Journal of Fuzzy Systems, 24(2), pp.1216-1231. https://doi.org/10.1007/s40815-021-01209-4.

Mastos, T.D., Nizamis, A., Terzi, S., Gkortzis, D., Papadopoulos, A., Tsagkalidis, N., Ioannidis, D., Votis, K. and Tzovaras, D., 2021. Introducing an application of an industry 4.0 solution for circular supply chain management. Journal of Cleaner Production, 300, p.126886. https://doi.org/10.1016/j.jclepro.2021.126886.

Mishra, U., Wu, J.Z. and Chiu, A.S.F., 2019. Effects of carbon-emission and setup cost reduction in a sustainable electrical energy supply chain inventory system. Energies, 12(7), p.1226. https://doi.org/10.3390/en12071226.

Oliveira-Pinto, S., Rosa-Santos, P. and Taveira-Pinto, F., 2019. Electricity supply to offshore oil and gas platforms from renewable ocean wave energy: Overview and case study analysis. Energy Conversion and Management, 186, pp.556-569. https://doi.org/10.1016/j.enconman.2019.02.050.

Pamucar, D., Torkayesh, A.E., Deveci, M. and Simic, V., 2022. Recovery center selection for end-of-life automotive lithium-ion batteries using an integrated fuzzy WASPAS approach. Expert Systems with Applications, 206, p.117827. https://doi.org/10.1016/j.eswa.2022.117827.

Paoli, L. and Gül, T., 2022. Electric cars fend off supply challenges to more than double global sales.

Putra, A.J., Abdillah, L.A. and Yudiastuti, H., 2016. Penentuan sekolah dasar negeri terbaik kota Palembang dengan metode weighted sum model (WSM) dan weighted product model (WPM) menggunakan visual basic. net 2015. URL: http://eprints.binadarma.ac.id/id/eprint/3239.

Queiroz, M.M., Telles, R. and Bonilla, S.H., 2020. Blockchain and supply chain management integration: a systematic review of the literature. Supply chain management: An international journal, 25(2), pp.241-254. https://doi.org/10.1108/SCM-03-2018-0143.

Ramezani, M., Khazaei, M., Gholian-Jouybari, F., Sandoval-Correa, A., Bonakdari, H. and Hajiaghaei-Keshteli, M., 2024. Turquoise hydrogen and waste optimization: A Bi-objective closed-loop and sustainable supply chain model for a case in Mexico. Renewable and Sustainable Energy Reviews, 195, p.114329. https://doi.org/10.1016/j.rser.2024.114329.

Richter, L., Lehna, M., Marchand, S., Scholz, C., Dreher, A., Klaiber, S. and Lenk, S., 2022. Artificial intelligence for electricity supply chain automation. Renewable and Sustainable Energy Reviews, 163, p.112459. https://doi.org/10.1016/j.rser.2022.112459.

Robert, M., Giuliani, P. and Gurau, C., 2022. Implementing industry 4.0 real-time performance management systems: the case of Schneider Electric. Production Planning & Control, 33(2-3), pp.244-260. https://doi.org/10.1080/09537287.2020.1810761.

Saghaei, M., Ghaderi, H. and Soleimani, H., 2020. Design and optimization of biomass electricity supply chain with uncertainty in material quality, availability and market demand. Energy, 197, p.117165. https://doi.org/10.1016/j.energy.2020.117165.

Taghipour, A., Fooladvand, A., Khazaei, M. and Ramezani, M., 2023. Criteria clustering and supplier segmentation based on sustainable shared value using BWM and PROMETHEE. Sustainability, 15(11), p.8670. https://doi.org/10.3390/su15118670.

Tsaramirsis, G., Kantaros, A., Al-Darraji, I., Piromalis, D., Apostolopoulos, C., Pavlopoulou, A., Alrammal, M., Ismail, Z., Buhari, S.M., Stojmenovic, M. and Tamimi, H., 2022. A modern approach towards an industry 4.0 model: From driving technologies to management. Journal of Sensors, 2022(1), p.5023011. https://doi.org/10.1155/2022/5023011.

Urciuoli, L., Mohanty, S., Hintsa, J. and Gerine Boekesteijn, E., 2014. The resilience of energy supply chains: a multiple case study approach on oil and gas supply chains to Europe. Supply Chain Management: An International Journal, 19(1), pp.46-63. https://doi.org/10.1108/SCM-09-2012-0307.

Vafadarnikjoo, A., Tavana, M., Chalvatzis, K. and Botelho, T., 2022. A socio-economic and environmental vulnerability assessment model with causal relationships in electric power supply chains. Socio-Economic Planning Sciences, 80, p.101156. https://doi.org/10.1016/j.seps.2021.101156.

Wang, C., Zhang, L., Zhou, P., Chang, Y., Zhou, D., Pang, M. and Yin, H., 2019. Assessing the environmental externalities for biomass-and coal-fired electricity generation in China: A supply chain perspective. Journal of Environmental Management, 246, pp.758-767. https://doi.org/10.1016/j.jenvman.2019.06.047.

Wang, L., Yu, L. and Ni, Z., 2022. A novel IVIF QFD considering both the correlations of customer requirements and the ranking uncertainty of technical attributes. Soft Computing, 26(9), pp.4199-4213. https://doi.org/10.1007/s00500-022-06892-5.

Wangsa, I.D. and Wee, H.M., 2019. The economical modelling of a distribution system for electricity supply chain. Energy Systems, 10, pp.415-435. https://doi.org/10.1007/s12667-018-0274-z.

Zamani, E.D., Smyth, C., Gupta, S. and Dennehy, D., 2023. Artificial intelligence and big data analytics for supply chain resilience: a systematic literature review. Annals of Operations Research, 327(2), pp.605-632. https://doi.org/10.1007/s10479-022-04983-y.

Zare, K., Mehri-Tekmeh, J. and Karimi, S., 2015. A SWOT framework for analyzing the electricity supply chain using an integrated AHP methodology combined with fuzzy-TOPSIS. International strategic management review, 3(1-2), pp.66-80. https://doi.org/10.1016/j.ism.2015.07.001.

Zavadskas, E.K., Antucheviciene, J., Hajiagha, S.H.R. and Hashemi, S.S., 2014. Extension of weighted aggregated sum product assessment with interval-valued intuitionistic fuzzy numbers (WASPAS-IVIF). Applied soft computing, 24, pp.1013-1021. https://doi.org/10.1016/j.asoc.2014.08.031.

Zhao, N., Hong, J. and Lau, K.H., 2023. Impact of supply chain digitalization on supply chain resilience and performance: A multi-mediation model. International Journal of Production Economics, 259, p.108817. https://doi.org/10.1016/j.ijpe.2023.108817.

Zhao, Y., Cao, Y., Shi, X., Wang, S., Yang, H., Shi, L., Li, H. and Zhang, J., 2021. Critical transmission paths and nodes of carbon emissions in electricity supply chain. Science of The Total Environment, 755, p.142530. https://doi.org/10.1016/j.scitotenv.2020.142530.