Sustainability Service Chain Capabilities in the Oil and Gas Industry: A Fuzzy Hybrid Approach SWARA-MABAC

Authors

DOI:

https://doi.org/10.31181/sor21202512

Keywords:

Service supply chain, Capabilities, Oil and gas industry, Fuzzy SWARA, Fuzzy MABAC

Abstract

This study aims to redefine the capabilities of the service supply chain in the Iranian oil and gas industry, where the concept of service chains has remained underdeveloped due to a traditional focus on conventional service chains. It seeks to compare the capabilities of the oil and gas service supply chain to those in other sectors and explore whether applied research is necessary to improve service chain performance. The study was conducted in three stages, involving ten petroleum and natural gas experts. The first stage focused on identifying key capabilities, yielding seven significant capabilities and 28 critical sub-capabilities. In the second stage, the SWARA fuzzy hybrid approach was employed to weigh and prioritize these capabilities, while the fuzzy MABAC methodology was used for strategic decision-making. Finally, the model’s sensitivity was assessed using fuzzy methods to validate the findings. The results highlight the highest priority capability for service providers: the ability to utilize information for updating information processing capacity in making decisions within the service supply chain. The selected location, G1, emerged as a key area of focus. This approach presents a novel method for optimizing service supply chain locations within the oil and gas sector. This paper introduces a unique approach to selecting optimal service supply chain locations in the oil and gas industry, addressing critical gaps in previous research. Employing advanced fuzzy hybrid methodologies provides valuable insights into improving service chain capabilities, offering a competitive advantage for companies operating in this sector.

Downloads

Download data is not yet available.

References

Komijan, A. R., Yazdi, A. K., Tan, Y., Ocampo, L., & Nasrollahpourniazi, F. (2024). Spherical Fuzzy Multicriteria Decision Making for Evaluating Healthcare Service Quality of Hospitals During the Global Pandemic. Int J Comput Intell Syst, 17, 105. https://doi.org/10.1007/s44196-024-00487-8

Zhou, X., Song, M., & Cui, L. (2020). Driving force for China’s economic development under Industry 4.0 and circular economy: Technological innovation or structural change?. Journal of Cleaner Production, 271, 122680. https://doi.org/10.1016/j.jclepro.2020.122680

Erol, İ., & Velioğlu, M. N. (2019). An investigation into sustainable supply chain management practices in a developing country. International Journal of eBusiness and eGovernment Studies, 11(2), 104-118. https://doi.org/10.34111/ijebeg.20191122

Karbassi Yazdi, A., Tan, Y., Spulbar, C., Birau, R., & Alfaro, J. (2022). An Approach for Supply Chain Management Contract Selection in the Oil and Gas Industry: Combination of Uncertainty and Multi-Criteria Decision-Making Methods. Mathematics, 10, 3230. https://doi.org/10.3390/math10183230

Zhao, R., Liu, Y., Zhang, N., & Huang, T. (2017). An optimization model for green supply chain management by using a big data analytic approach. Journal of Cleaner Production, 142, 1085-1097. https://doi.org/10.1016/j.jclepro.2016.03.006

Chirumalla, K. (2021). Building digitally-enabled process innovation in the process industries: A dynamic capabilities approach. Technovation, 105, 102256. https://doi.org/10.1016/j.technovation.2021.102256

Opazo-Basáez, M., Vendrell-Herrero, F., & Bustinza, O. F. (2022). Digital service innovation: a paradigm shift in technological innovation. Journal of Service Management, 33(1), 97-120. https://doi.org/10.1108/JOSM-11-2020-0427

Lin, J., Lin, S., Benitez, J., Luo, X. R., & Ajamieh, A. (2023). How to build supply chain resilience: the role of fit mechanisms between digitally-driven business capability and supply chain governance. Information & Management, 60(2), 103747. https://doi.org/10.1016/j.im.2022.103747

Ivanov, D., Tang, C. S., Dolgui, A., Battini, D., & Das, A. (2021). Researchers’ perspectives on Industry 4.0: multi-disciplinary analysis and opportunities for operations management. International Journal of Production Research, 59(7), 2055–2078. https://doi.org/10.1080/00207543.2020.1798035

Enz, M. G., & Lambert, D. M. (2023). A supply chain management framework for services. Journal of Business Logistics, 44(1), 11-36. https://doi.org/10.1111/jbl.12323

Habibah, N., & Kusumastuti, R. D. (2020). Determining criteria for supplier selection in the indonesian oil and gas industry. The South East Asian Journal of Management, 14(2), 5. https://doi.org/10.21002/seam.v14i2.12813

Haddad, A. N., da Costa, B. B., de Andrade, L. S., Hammad, A., & Soares, C. A. (2021). Application of fuzzy-TOPSIS method in supporting supplier selection with focus on HSE criteria: A case study in the oil and gas industry. Infrastructures, 6(8), 105. https://doi.org/10.3390/infrastructures6080105

A Kassem, M., Khoiry, M. A., & Hamzah, N. (2021). Theoretical review on critical risk factors in oil and gas construction projects in Yemen. Engineering, Construction and Architectural Management, 28(4), 934-968. https://doi.org/10.1108/ECAM-03-2019-0123

A Kassem, M., Khoiry, M. A., & Hamzah, N. (2020). Assessment of the effect of external risk factors on the success of an oil and gas construction project. Engineering, Construction and Architectural Management, 27(9), 2767-2793. https://doi.org/10.1108/ECAM-10-2019-0573

Yazdi, A. K., Wanke, P. F., Hanne, T., Abdi, F., & Sarfaraz, A. H. (2022). Supplier selection in the oil & gas industry: A comprehensive approach for Multi-Criteria Decision Analysis. Socio-Economic Planning Sciences, 101142. https://doi.org/10.1016/j.seps.2021.101142

Boon-itt, S., Wong, C. Y., & Wong, C. W. Y. (2017). Service supply chain management process capabilities: Measurement development. International Journal of Production Economics, 193, 1–11. https://doi.org/10.1016/j.ijpe.2017.06.024

Saleh, C., Assery, S., & Dzakiyullah, N. R. (2018). Supply Chain: Partnership, Capability and Performance (A Case Study on Service Companies at Yogyakarta Indonesia). Journal of Engineering and Applied Sciences, 13(6), 5391-5394.

Elgazzar, S., & Elzarka, S. (2017). Supply chain management in the service sector: an applied framework. The Business & Management Review, 8(5), 118-130.

Nürk, J. (2019). Smart information system capabilities of digital supply chain business models. European Journal of Business Science and Technology, 5(2), 143-184. https://doi.org/10.11118/ejobsat.v5i2.175

Kareem, M. A., & Kummitha, H. V. R. (2020). The impact of supply chain dynamic capabilities on operational performance. Organizacija, 53(4), 319-331.http://doi.org/10.2478/orga-2020-0021

Li, X., Yu, S., & Chu, J. (2018). Optimal selection of manufacturing services in cloud manufacturing: A novel hybrid MCDM approach based on rough ANP and rough TOPSIS. Journal of Intelligent & Fuzzy Systems, 34(6), 4041-4056. https://doi.org/10.3233/JIFS-171379

Yasmin, M., Tatoglu, E., Kilic, H. S., Zaim, S., & Delen, D. (2020). Big data analytics capabilities and firm performance: An integrated MCDM approach. Journal of Business Research, 114, 1–15. https://doi.org/10.1016/j.jbusres.2020.03.028

Song, D., Huang, W., & Xu, Y. (2008). Performance evaluation of professional service supply chain based upon DEA & AHP models. In 2008 IEEE International Conference on Service Operations and Logistics, and Informatics (Vol. 2, pp. 2210-2215). IEEE. https://doi.org/10.1109/SOLI.2008.4682902

Zhan, B., & Zeng, Y. (2011). Port service supply chain performance evaluation based on GRA. In 2011 IEEE 2nd International Conference on Computing, Control and Industrial Engineering (Vol. 2, pp. 421-424). IEEE. https://doi.org/10.1109/CCIENG.2011.6008154

Cho, D. W., Lee, Y. H., Ahn, S. H., & Hwang, M. K. (2012). A framework for measuring the performance of service supply chain management. Computers & Industrial Engineering, 62(3), 801–818. https://doi.org/10.1016/j.cie.2011.11.014

Shahin, A., Mehrparvar, E., & Shirouyehzad, H. (2013). Prioritisation of departments based on service quality dimensions in Isfahan Steel Company: a multiple criteria decision making approach. International Journal of Productivity and Quality Management, 11(1), 116-130. https://doi.org/10.1504/IJPQM.2013.050571

Özveri, O., Güçlü, P., & Aycin, E. (2015). Evaluation of service supply chain performance criteria with DANP method. ASSAM Uluslararası Hakemli Dergi, 2(4), 104-119.

Chithambaranathan, P., Subramanian, N., Gunasekaran, A., & Palaniappan, P. K. (2015). Service supply chain environmental performance evaluation using grey based hybrid MCDM approach. International Journal of Production Economics, 166, 163-176. https://doi.org/10.1016/j.ijpe.2015.01.002

Tseng, M. L., Lim, M. K., Wong, W. P., Chen, Y. C., & Zhan, Y. (2018). A framework for evaluating the performance of sustainable service supply chain management under uncertainty. International journal of production economics, 195, 359-372. https://doi.org/10.1016/j.ijpe.2016.09.002

Saragih, J., Tarigan, A., Silalahi, E. F., Wardati, J., & Pratama, I. (2020). Supply chain operational capability and supply chain operational performance: Does the supply chain management and supply chain integration matters. International Journal of Supply Chain Management, 9(4), 1222-1229.

Govindan, K., Mina, H., & Alavi, B. (2020). A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: A case study of coronavirus disease 2019 (COVID-19). Transportation Research Part E: Logistics and Transportation Review, 138, 101967. https://doi.org/10.1016/j.tre.2020.101967

Migdadi, Y. K. A. A., & Elzzqaibeh, D. A. S. I. (2018). The evaluation of green manufacturing strategies adopted by ISO 14001 certificate holders in Jordan. International Journal of Productivity and Quality Management, 23(1), 90-109. https://doi.org/10.1504/IJPQM.2018.088610

Adesanya, A., Yang, B., Bin Iqdara, F. W., & Yang, Y. (2020). Improving sustainability performance through supplier relationship management in the tobacco industry. Supply Chain Management: An International Journal, 25(4), 413-426. https://doi.org/10.1108/SCM-01-2018-0034

Asamoah, D., Agyei-Owusu, B., Andoh-Baidoo, F. K., & Ayaburi, E. (2021). Inter-organizational systems use and supply chain performance: Mediating role of supply chain management capabilities. International journal of information management, 58, 102195. https://doi.org/10.1016/j.ijinfomgt.2020.102195

Attaran, M. (2020). Digital technology enablers and their implications for supply chain management. In Supply Chain Forum: An International Journal (Vol. 21, No. 3, pp. 158-172). Taylor & Francis. https://doi.org/10.1080/16258312.2020.1751568

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. https://doi.org/10.3390/su132413950

Turskis, Z., Keršulienė, V., & Zavadskas, E. K. (2010). Selection of Rational Dispute Resolution Method by Applying New Step-Wise Weight Assessment Ratio Analysis. Journal of Business Economics and Management, 11(2), 243-258. https://doi.org/10.3846/jbem.2010.12

Gupta, P. (2017). Applications of Fuzzy Logic in Daily life. International Journal of Advanced Research in computer science, 8(5).

Mavi, R. K., & Standing, C. (2018). Critical success factors of sustainable project management in construction: A fuzzy DEMATEL-ANP approach. Journal of cleaner production, 194, 751-765. https://doi.org/10.1016/j.jclepro.2018.05.120

Pamučar, D., & Ćirović, G. (2015). The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison (MABAC). Expert systems with applications, 42(6), 3016-3028. https://doi.org/10.1016/j.eswa.2014.11.057

Published

2025-01-01

How to Cite

Mehdiabadi, A., Sadeghi, A. ., Karbassi Yazdi, A., & Tan, Y. (2025). Sustainability Service Chain Capabilities in the Oil and Gas Industry: A Fuzzy Hybrid Approach SWARA-MABAC. Spectrum of Operational Research, 2(1), 92-112. https://doi.org/10.31181/sor21202512