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An Approach for Supply Chain Management Contract Selection in the Oil and Gas Industry: Combination of Uncertainty and Multi-Criteria Decision-Making Methods

Author

Listed:
  • Amir Karbassi Yazdi

    (School of Engineering, Universidad Católica del Norte, Larrondo 1281, Coquimbo 1781421, Chile)

  • Yong Tan

    (School of Management, University of Bradford, Bradford BD7 1DP, UK)

  • Cristi Spulbar

    (Faculty of Economics and Business Administration, University of Craiova, 200585 Craiova, Romania)

  • Ramona Birau

    (Doctoral School of Economic Sciences, University of Craiova, 200585 Craiova, Romania)

  • Jorge Alfaro

    (School of Engineering, Universidad Católica del Norte, Larrondo 1281, Coquimbo 1781421, Chile)

Abstract

The oil and gas industry plays a significant role in the economies of many countries today. Due to various factors, including oil price fluctuations, wars, sanctions, and many other instances, selling and supplying these products at low prices is necessary. As a result, the global economy may suffer as well. Supply chain management is one way to reduce the prices of these products. This study was conducted to identify supply chain management contracts in the oil and gas industry. The paper presents an application of multi-criteria decision-making (MCDM) for coping with uncertainty. We contribute to the literature by proposing a new hybrid MCDM method with gray numbers for ranking supply chain management contracts in the oil and gas industry. The results show that the factors for evaluating supply chain management contracts must be selected, and then according to these factors, the supply chain management contracts must be chosen. As a result, we provide our customers with the best deals and help oil and gas companies minimize their costs.

Suggested Citation

  • Amir Karbassi Yazdi & Yong Tan & Cristi Spulbar & Ramona Birau & Jorge Alfaro, 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, MDPI, vol. 10(18), pages 1-20, September.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:18:p:3230-:d:907925
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    References listed on IDEAS

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    1. Cai, Jianhu & Hu, Xiaoqing & Tadikamalla, Pandu R. & Shang, Jennifer, 2017. "Flexible contract design for VMI supply chain with service-sensitive demand: Revenue-sharing and supplier subsidy," European Journal of Operational Research, Elsevier, vol. 261(1), pages 143-153.
    2. Ruml, Anette & Qaim, Matin, 2020. "Effects of marketing contracts and resource-providing contracts in the African small farm sector: Insights from oil palm production in Ghana," World Development, Elsevier, vol. 136(C).
    3. Farsi, Maryam & Erkoyuncu, John Ahmet, 2021. "An agent-based approach to quantify the uncertainty in Product-Service System contract decisions: A case study in the machine tool industry," International Journal of Production Economics, Elsevier, vol. 233(C).
    4. Aubrey Clark & Giovanni Reggiani, 2021. "Contracts for acquiring information," Papers 2103.03911, arXiv.org.
    5. Alexandre Dolgui & Dmitry Ivanov & Semyon Potryasaev & Boris Sokolov & Marina Ivanova & Frank Werner, 2020. "Blockchain-oriented dynamic modelling of smart contract design and execution in the supply chain," International Journal of Production Research, Taylor & Francis Journals, vol. 58(7), pages 2184-2199, April.
    6. Norman Dalkey & Olaf Helmer, 1963. "An Experimental Application of the DELPHI Method to the Use of Experts," Management Science, INFORMS, vol. 9(3), pages 458-467, April.
    7. Li, Jianbin & Luo, Xiaomeng & Wang, Qifei & Zhou, Weihua, 2021. "Supply chain coordination through capacity reservation contract and quantity flexibility contract," Omega, Elsevier, vol. 99(C).
    8. Rezaei, Jafar, 2016. "Best-worst multi-criteria decision-making method: Some properties and a linear model," Omega, Elsevier, vol. 64(C), pages 126-130.
    9. Rezaei, Jafar, 2015. "Best-worst multi-criteria decision-making method," Omega, Elsevier, vol. 53(C), pages 49-57.
    10. Hamilton, James D., 2003. "What is an oil shock?," Journal of Econometrics, Elsevier, vol. 113(2), pages 363-398, April.
    11. Jiahua Zhang & Lian Qi & Shilu Tong, 2021. "Dynamic Contract under Quick Response in a Supply Chain with Information Asymmetry," Production and Operations Management, Production and Operations Management Society, vol. 30(5), pages 1273-1289, May.
    12. Yucekaya, A., 2022. "Electricity trading for coal-fired power plants in Turkish power market considering uncertainty in spot, derivatives and bilateral contract market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    13. Ni, Jian & Zhao, Jun & Chu, Lap Keung, 2021. "Supply contracting and process innovation in a dynamic supply chain with information asymmetry," European Journal of Operational Research, Elsevier, vol. 288(2), pages 552-562.
    14. Hu, Kuo-Jen & Yu, Vincent F., 2016. "An integrated approach for the electronic contract manufacturer selection problem," Omega, Elsevier, vol. 62(C), pages 68-81.
    15. Zhou, Yong-Wu & Lin, Xiaogang & Zhong, Yuanguang & Xie, Wei, 2019. "Contract selection for a multi-service sharing platform with self-scheduling capacity," Omega, Elsevier, vol. 86(C), pages 198-217.
    16. Cheaitou, Ali & Larbi, Rim & Al Housani, Bashayer, 2019. "Decision making framework for tender evaluation and contractor selection in public organizations with risk considerations," Socio-Economic Planning Sciences, Elsevier, vol. 68(C).
    17. Hadi Sarvari & Daniel W. M. Chan & Behrouz Ashrafi & Timothy O. Olawumi & Nerija Banaitiene, 2021. "Prioritization of Contracting Methods for Water and Wastewater Projects Using the Fuzzy Analytic Hierarchy Process Method," Energies, MDPI, vol. 14(22), pages 1-18, November.
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    1. Daniel Fernández & Álvaro Rodríguez-Prieto & Ana M. Camacho, 2022. "Optimal Parameters Selection in Advanced Multi-Metallic Co-Extrusion Based on Independent MCDM Analytical Approaches and Numerical Simulation," Mathematics, MDPI, vol. 10(23), pages 1-26, November.

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