Implementation of Effective Supply Chain Management Practice in the National Oil Corporation in Developing Country: An Integrated BWM-AROMAN approach

Authors

DOI:

https://doi.org/10.31181/dma21202439

Keywords:

Supply chain management, Multiple criteria, Petroleum industry, BWM, AROMAN

Abstract

This study introduces an approach to assess the adoption of an effective supply chain management practice (SCMP) in the petroleum sector, a vital component of any country’s economy. Enhancing the sector’s performance requires an effective implementation of the SCMP to address performance-related issues in a sustainable manner. For that, the best-worst method (BWM) determined the weights of four identified challenges to effective SCMP in the sector, and subsequently, the alternative ranking order method accounting for two-step normalization (AROMAN) evaluated four alternatives to overcome these challenges. To show the applicability of our approach, the national oil corporation in Kenya is considered as a case study. Results show that collaboration between supply chain actors for the provision of transport and distribution is the most appropriate alternative for an effective SCMP for the national oil corporation.

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References

Geisler, E., & Wickramasinghe, N. (2015). Principles of knowledge management: Theory, practice, and cases. Routledge. https://doi.org/10.4324/9781315686448

Eberle, L. G., Sugiyama, H., & Schmidt, R. (2014). Improving lead time of pharmaceutical production processes using Monte Carlo simulation. Computers & chemical engineering, 68, 255-263. https://doi.org/10.1016/j.compchemeng.2014.05.017

Osoro, A. (2018). Challenges affecting performance of supply chain systems in the petroleum industry in Kenya COHRED-JKUAT].

Munir, M., Jajja, M. S. S., Chatha, K. A., & Farooq, S. (2020). Supply chain risk management and operational performance: The enabling role of supply chain integration. International Journal of Production Economics, 227, 107667. https://doi.org/10.1016/j.ijpe.2020.107667

Negri, M., Cagno, E., Colicchia, C., & Sarkis, J. (2021). Integrating sustainability and resilience in the supply chain: A systematic literature review and a research agenda. Business Strategy and The Environment, 30(7), 2858-2886. https://doi.org/10.1002/bse.2776

Kim, J. U., Kim, H. S., & Park, S. C. (2015). The mediating effects of bidirectional knowledge transfer on system implementation success. Asia pacific journal of information systems, 25(3), 445-472. https://doi.org/10.14329/apjis.2015.25.3.445

Hou, Y., Xiong, Y., Wang, X., & Liang, X. (2014). The effects of a trust mechanism on a dynamic supply chain network. Expert systems with applications, 41(6), 3060-3068. https://doi.org/10.1016/j.eswa.2013.10.037

Kinyua, G. M. (2015). Relationship between knowledge management and performance of commercial banks in Kenya. Kenyatta University.

Schrettle, S., Hinz, A., Scherrer-Rathje, M., & Friedli, T. (2014). Turning sustainability into action: Explaining firms' sustainability efforts and their impact on firm performance. International Journal of Production Economics, 147, 73-84. https://doi.org/10.1016/j.ijpe.2013.02.030

Kimani, C. (2013). Supply chain management challenges in Kenya petroleum industry: case of national oil corporation of Kenya. International Journal of Social Sciences and Entrepreneurship, 1(3), 231-246.

Luthra, S., Garg, D., & Haleem, A. (2013). Identifying and ranking of strategies to implement green supply chain management in Indian manufacturing industry using analytical hierarchy process. Journal of Industrial Engineering and Management, 6(4), 930-962. https://doi.org/10.3926/jiem.693

Agami, N., Saleh, M., & Rasmy, M. (2012). Supply chain performance measurement approaches: Review and classification. Journal of Organizational Management Studies, 2012, 1. https://doi.org/10.5171/2012.872753

Alquist, R., Kilian, L., & Vigfusson, R. J. (2013). Forecasting the price of oil. In Handbook of economic forecasting (Vol. 2, pp. 427-507). Elsevier. https://doi.org/10.1016/B978-0-444-53683-9.00008-6

Bartlomiejczuk, G. (2015). How do Recognition Programs Impact Employee Engagement and How have Companies with a Large Global Footprint Structured such Programs to Drive Results?

Hannum, W. (2016). BF Skinner's theory. Learning Theory Fundamentals website, accessed, 6.

Žižović, M., & Pamucar, D. (2019). New model for determining criteria weights: Level Based Weight Assessment (LBWA) model. Decision Making: Applications in Management and Engineering, 2(2), 126-137. https://doi.org/10.31181/dmame1902102z

Pamučar, D., Stević, Ž., & Sremac, S. (2018). A new model for determining weight coefficients of criteria in mcdm models: Full consistency method (fucom). Symmetry, 10(9), 393. https://doi.org/10.3390/sym10090393

Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57. https://doi.org/10.1016/j.omega.2014.11.009

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

Stević, Ž., Pamučar, D., Puška, A., & Chatterjee, P. (2020). Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to COmpromise solution (MARCOS). Computers & Industrial Engineering, 140, 106231. https://doi.org/10.1016/j.cie.2019.106231

Gigović, L., Pamučar, D., Bajić, Z., & Milićević, M. (2016). The combination of expert judgment and GIS-MAIRCA analysis for the selection of sites for ammunition depots. Sustainability, 8(4), 372. https://doi.org/10.3390/su8040372

Bošković, S., Švadlenka, L., Jovčić, S., Dobrodolac, M., Simić, V., & Bačanin, N. (2023). An Alternative Ranking Order Method Accounting for Two-Step Normalization (AROMAN)-A Case Study of the Electric Vehicle Selection Problem. IEEE Access. https://doi.org/10.1109/ACCESS.2023.3265818

Kumar, S., & Barua, M. K. (2021). Green Supply Chain Management in the Indian Petroleum Industry Using AHP-VIKOR Approaches. Emerging Frontiers in Operations and Supply Chain Management: Theory and Applications, 181-200. https://doi.org/10.1007/978-981-16-2774-3_9

Fazli, S., Kiani Mavi, R., & Vosooghidizaji, M. (2015). Crude oil supply chain risk management with DEMATEL-ANP. Operational Research, 15, 453-480. https://doi.org/10.1007/s12351-015-0182-0

Ahmad, W. N. K. W., Rezaei, J., Sadaghiani, S., & Tavasszy, L. A. (2017). Evaluation of the external forces affecting the sustainability of oil and gas supply chain using Best Worst Method. Journal of Cleaner Production, 153, 242-252. https://doi.org/10.1016/j.jclepro.2017.03.166

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(18), 3230. https://doi.org/10.3390/math10183230

Gardas, B. B., Raut, R. D., & Narkhede, B. (2019). Determinants of sustainable supply chain management: A case study from the oil and gas supply chain. Sustainable Production and Consumption, 17, 241-253. https://doi.org/10.1016/j.spc.2018.11.005

Kumar, S., & Barua, M. K. (2022). Modeling and investigating the interaction among risk factors of the sustainable petroleum supply chain. Resources Policy, 79, 102922. https://doi.org/10.1016/j.resourpol.2022.102922

Kumar, S., & Barua, M. K. (2022b). A modeling framework and analysis of challenges faced by the Indian petroleum supply chain. Energy, 239, 122299. https://doi.org/10.1016/j.energy.2021.122299

Nasri, S. A., Ehsani, B., Hosseininezhad, S. J., & Safaie, N. (2023). A sustainable supplier selection method using integrated Fuzzy DEMATEL-ANP-DEA approach (case study: Petroleum Industry). Environment, Development and Sustainability, 25(11), 12791-12827. https://doi.org/10.1007/s10668-022-02590-2

Gidiagba, J., Tartibu, L., & Okwu, M. (2023). Sustainable supplier selection in the oil and gas industry: An integrated multi-criteria decision-making approach. Procedia Computer Science, 217, 1243-1255. https://doi.org/10.1016/j.procs.2022.12.323

Wang, M., Ji, L., Xie, Y., & Huang, G. (2024). Regional bioethanol supply chain optimization with the integration of GIS-MCDM method and quantile-based scenario analysis. Journal of Environmental Management, 351, 119883. https://doi.org/10.1016/j.jenvman.2023.119883

Ransikarbum, K., Chanthakhot, W., Glimm, T., & Janmontree, J. (2023). Evaluation of Sourcing Decision for Hydrogen Supply Chain Using an Integrated Multi-Criteria Decision Analysis (MCDA) Tool. Resources, 12(4), 48. https://doi.org/10.3390/resources12040048

Adjei, A. N., & Ackah, I. (2023). External analysis of local content dynamics in the upstream petroleum sector of Ghana and Ivory Coast. The Extractive Industries and Society, 15, 101304. https://doi.org/10.1016/j.exis.2023.101304

Igoud, S., Zeriri, D., Boutra, B., Mameche, A., Benzegane, Y., Belloula, M., Benkara, L., Aoudjit, L., & Sebti, A. (2022). Compared efficiency of sustainable and conventional treatments of saline oily wastewater rejected by petroleum industry in Algerian Sahara. Petroleum Science and Technology, 40(1), 92-106. https://doi.org/10.1080/10916466.2021.2002358

Kanyako, V., & Kanyako, V. (2020). Managing Disputes in West Africa's Petroleum Industry. Oil Revenues, Security and Stability in West Africa, 165-190. https://doi.org/10.1007/978-3-030-37986-5_7

Ndamase, Z. (2020). An investigation into Data Governance challenges: A case study of a Petroleum Firm in the Sub-Saharan Africa Region.

Baig, M. M. U., Ali, Y., & Rehman, O. U. (2022). Enhancing resilience of oil supply chains in context of developing countries. Operational Research in Engineering Sciences: Theory and Applications, 5(1), 69-89. https://doi.org/10.31181/oresta210322091b

Okokpujie, I., Okonkwo, U., Akinlabi, E., Okokpujie, K., & Atayero, A. (2019). Multi-criteria decision analysis towards the selection of a perfect location for establishing crude oil refinery in Niger Delta Nigeria. Journal of Physics: Conference Series, https://doi.org/10.1088/1742-6596/1378/3/032100

Aroge, O. O. (2019). Assessment Of Disruption Risk In Supply Chain The Case Of Nigeria's Oil Industry University of Bradford].

Pamucar, D., Deveci, M., Gokasar, I., Brito-Parada, P. R., & Martínez, L. (2024). Evaluation of Process Technologies for Sustainable Mining Using Interval Rough Number Based Heronian and Power Averaging Functions. Knowledge-Based Systems, 111494. https://doi.org/10.1016/j.knosys.2024.111494

Moslem, S., Deveci, M., & Pilla, F. (2024). A novel best-worst method and Kendall model integration for optimal selection of digital voting tools to enhance citizen engagement in public decision making. Decision Analytics Journal, 10, 100378. https://doi.org/10.1016/j.dajour.2023.100378

Kiptum, C. K., Bouraima, M. B., Badi, I., Zonon, B. I. P., Ndiema, K. M., & Qiu, Y. (2023). Assessment of the Challenges to Urban Sustainable Development Using an Interval-Valued Fermatean Fuzzy Approach. Systemic Analytics, 1(1), 11-26. https://doi.org/10.31181/sa1120233

Badi, I., Bouraima, M. B., & Muhammad, L. J. (2022). The role of intelligent transportation systems in solving traffic problems and reducing environmental negative impact of urban transport. Decision Making and Analysis, 1(1), 1-9. https://doi.org/10.55976/dma.1202311371-9

Bouraima, M. B., Qiu, Y., Yusupov, B., & Ndjegwes, C. M. (2020). A study on the development strategy of the railway transportation system in the West African Economic and Monetary Union (WAEMU) based on the SWOT/AHP technique. Scientific African, 8, e00388. https://doi.org/10.1016/j.sciaf.2020.e00388

Kovač, M., Tadić, S., Krstić, M., & Bouraima, M. B. (2021). Novel Spherical Fuzzy MARCOS Method for Assessment of Drone-Based City Logistics Concepts. Complexity, 2021. https://doi.org/10.1155/2021/2374955

Bouraima, M. B., Jovčić, S., Švadlenka, L., Simic, V., Badi, I., & Maraka, N. D. (2024). An integrated multi-criteria approach to formulate and assess healthcare referral system strategies in developing countries. Healthcare Analytics, 100315. https://doi.org/10.1016/j.health.2024.100315

Moslem, S. (2023). A Novel Parsimonious Best Worst Method for Evaluating Travel Mode Choice. IEEE Access, 11, 16768-16773. https://doi.org/10.1109/ACCESS.2023.3242120

Radovanović, M., Petrovski, A., Cirkin, E., Behlić, A., Jokić, Ž., Chemezov, D., Hashimov, E. G., Bouraima, M. B., & Jana, C. (2024). Application of the new hybrid model LMAW-G-EDAS multi-criteria decision-making when choosing an assault rifle for the needs of the army. Journal of Decision Analytics and Intelligent Computing, 4(1), 16-31. https://doi.org/10.31181/jdaic10021012024r

Bouraima, M. B., Ayyildiz, E., Badi, I., Murat, M., Es, H. A., & Pamucar, D. (2024). A decision support system for assessing the barriers and policies for wind energy deployment. Renewable and sustainable energy reviews, 200, 114571. https://doi.org/10.1016/j.rser.2024.114571

Torkayesh, A. E., Pamucar, D., Ecer, F., & Chatterjee, P. (2021). An integrated BWM-LBWA-CoCoSo framework for evaluation of healthcare sectors in Eastern Europe. Socio-Economic Planning Sciences, 78, 101052. https://doi.org/10.1016/j.seps.2021.101052

Görçün, Ö. F., & Doğan, G. (2023). Mobile crane selection in project logistics operations using Best and Worst Method (BWM) and fuzzy Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS). Automation in Construction, 147, 104729. https://doi.org/10.1016/j.autcon.2022.104729

Stević, Ž., Pamučar, D., Subotić, M., Antuchevičiene, J., & Zavadskas, E. K. (2018). The location selection for roundabout construction using Rough BWM-Rough WASPAS approach based on a new Rough Hamy aggregator. Sustainability, 10(8), 2817. https://doi.org/10.3390/su10082817

Torkayesh, S. E., Iranizad, A., Torkayesh, A. E., & Basit, M. N. (2020). Application of BWM-WASPAS model for digital supplier selection problem: A case study in online retail shopping. Journal of Industrial Engineering and Decision Making, 1(1), 12-23. https://doi.org/10.31181/jiedm200101012t

Hashemkhani Zolfani, S., Yazdani, M., Ebadi Torkayesh, A., & Derakhti, A. (2020). Application of a gray-based decision support framework for location selection of a temporary hospital during COVID-19 pandemic. Symmetry, 12(6), 886. https://doi.org/10.3390/sym12060886

Alrasheedi, A. F., Mishra, A. R., Pamucar, D., Devi, S., & Cavallaro, F. Interval-valued intuitionistic fuzzy AROMAN method and its application in sustainable wastewater treatment technology selection. Journal of intelligent & fuzzy systems (Preprint), 1-24.

Pishahang, M., Jovcic, S., Hashemkhani Zolfani, S., Simic, V., & Görçün, Ö. F. (2023). MCDM-based wildfire risk assessment: a case study on the state of Arizona. Fire, 6(12), 449. https://doi.org/10.3390/fire6120449

Nikolić, I., Milutinović, J., Božanić, D., & Dobrodolac, M. (2023). Using an interval type-2 fuzzy AROMAN decision-making method to improve the sustainability of the postal network in rural areas. Mathematics, 11(14), 3105. https://doi.org/10.3390/math11143105

Čubranić-Dobrodolac, M., Jovčić, S., Bošković, S., & Babić, D. (2023). A decision-making model for professional drivers selection: A hybridized fuzzy-AROMAN-Fuller approach. Mathematics, 11(13), 2831. https://doi.org/10.3390/math11132831

Bošković, S., Švadlenka, L., Dobrodolac, M., Jovčić, S., & Zanne, M. (2023). An Extended AROMAN Method for Cargo Bike Delivery Concept Selection. Decis. Mak. Adv, 1, 1-9. https://doi.org/10.31181/v120231

Kara, K., Yalçın, G. C., Acar, A. Z., Simic, V., Konya, S., & Pamucar, D. (2024). The MEREC-AROMAN method for determining sustainable competitiveness levels: A case study for Turkey. Socio-Economic Planning Sciences, 91, 101762. https://doi.org/10.1016/j.seps.2023.101762

Dobrodolac, M., Bošković, S., Jovčić, S., & Lazarević, D. (2024). Sustainable Delivery Model Selection using AROMAN Approach. Decision Making Advances, 2(1), 73-82. https://doi.org/10.31181/dma21202429

Myers, M. B., & Cheung, M.-S. (2008). Sharing global supply chain knowledge. MIT Sloan management review.

Korucuk, S., Aytekin, A., & Moslem, S. (2024). A Novel Interval-Valued-q-rung Orthopair Fuzzy-Additive Ratio Assessment Model for Evaluating Logistics Service Quality. IEEE Access. https://doi.org/10.1109/ACCESS.2024.3384874

Korucuk, S., & Aytekin, A. (2024). Assessing the problems encountered by companies using e-logistics via a Polytopic fuzzy methodology: A case of Giresun province. Spectrum of Engineering and Management Sciences, 2(1), 36-45.

Aytekin, A., Korucuk, S., & Görçün, Ö. F. (2024). Determining the factors affecting transportation demand management and selecting the best strategy: A case study. Transport policy, 146, 150-166. https://doi.org/10.1016/j.tranpol.2023.11.003

Published

2024-06-10

How to Cite

Kiptum , C. K., Bouraima, M. B., Ibrahim , B., Oloketuyi , E. A., Makinde , O. O., & Qiu , Y. (2024). Implementation of Effective Supply Chain Management Practice in the National Oil Corporation in Developing Country: An Integrated BWM-AROMAN approach . Decision Making Advances, 2(1), 199–212. https://doi.org/10.31181/dma21202439