Supply Chain Management Contract Selection: History
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Contributor:
Amir Karbassi Yazdi
,
Yong Tan
,
Cristi Spulbar
,
Ramona Birau
,
Jorge Alfaro

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.

  • contract selection
  • supply chain management (SCM)
  • best–worst method (BWM)
  • measurement of choices and their ranking as a compromise solution (MARCOS) method

1. Introduction

The oil and gas (O&G) industry is one of the most important economic sectors that contributes to a country’s income [1]. The income derived from the sector can further facilitate infrastructure construction [2]. Due to the fact that there is a level of cost involved in the extraction and maintenance of O&G, the price will increase accordingly based on the cost [3].
A supply chain refers to a chain of activities involved in transferring the raw materials from the suppliers to the end users, in which cost reduction and customer satisfaction improvement are also considered. Many companies have tried to find ways to maximize their profits through engaging in appropriate contracts [4]. The successful implementation of this is attributed to a number of factors. Therefore, to select the proper contract, factors, including both fixed and variable factors, need to be considered, such as information, human resources (HR), the time needed to purchase equipment, time, and quality, among others [5][6].
It is difficult to identify the right contractor among the many that offer various services [7]. It is imperative to consider a variety of factors before choosing a contract. Multi-criteria decision-making (MCDM) is one way to help decision-makers (DMs) make informed decisions. Decision-making based on multi-criteria is categorized into two main categories: MCDA and MODM. MCDM was used to make decisions. A pairwise comparison method and a decision matrix method are both included in MCDM. Some examples of the former include the analytic hierarchical process (AHP); network analytical process (ANP); and measuring attractiveness by categorical-based evaluation techniques (MACBETH), while some examples of the latter include: the technique for order of preference by similarity to ideal solution (TOPSIS), Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR), multi-objective optimization by ratio analysis plus the full multiplicative form (MULTIMOORA); and measurement of choices and ranking according to compromise solution (MARCOS), etc. The decision matrix and pairwise comparison methods were used in the research. Making decisions can be challenging in the modern world since numerous factors must be considered. The factors considered also pertain to uncertainty alternatives. Uncertainty has always been a concern for researchers. One example is using gray numbers. Researchers will explain why gray numbers are better than fuzzy set numbers in this section [8][9].

2. Supply Chain Management Contract Selection in the Oil and Gas Industry

Selecting a SCM contract is one of the most common topics in the research area of SCM [10][11] because this is closely related to the performance of the company [12]. The existing research studies have conducted research on this topic across different economic sectors [13][14][15].
Researchers were intrigued by Dolgui et al.’s [16] method of building a SCM contract by using blockchain and dynamic modeling. Mathematical modeling was used to determine the most effective smart contract. The Chinese blockchain was explored by Haque et al. [15] to design intelligent contracts in the oil industry’s SCM. Mohammed [16] provided an overview of the use of AHP and Delphi in the Bangladeshi SCM. The study considered several factors, including responsiveness, distortion of information, excess inventory, uncertainty, volatility of demand, and flexibility.
The selection of contracts in the water services was provided by Saravi et al. [17] under the fuzzy AHP (FAHP). Some issues were considered, including organization, management, the project’s purpose, finance, contract, and law. Following this, 18 subcategories were created based on the subdivision of each category, and then the Delphi method was used for the screening purpose. To assign a score to each contract, the FAHP software is used for grading purposes based on the performance. The efficiency of the BWM in selecting appropriate contracts was investigated by Faraji et al. [18]; the study demonstrated this for the onshore drilling projects in the oil industry. Four factors were considered: cost, environment, time, and quality.
To select the LNG contract, two methods were utilized by Yazdi et al. [19], including the mixed-integer linear programming (MILP) and the linear programming technique for multidimensional analysis of preference (LINMAP). Three factors were considered—evaporation rate, quality, and price. Based on the characteristics, the selection of a construction contract for a given project using AHP was discussed by Abdullah et al. [20]. Based on the unit prices, types of additional costs, and ten factors from the cross-sectional categories, the contracts were prioritized according to the factors and categories identified. The results show that the unit price contract was deemed the best. Several criteria were evaluated by Giri et al. [21] to select the most appropriate contract. A few factors were considered, including organization, quality, and price. In evaluating these factors, the engineering department determined the most crucial factor in selecting a particular contract.
The optimal strategy for selecting the most appropriate contract in the O&G industry can also be determined by using the ANP (Jesus et al. [22]). Four categories are outlined—the organization’s structure, the type of contract, the characteristics of the project, and the contracting process. The sub-factors reflect the specific aspects of each category. Afterward, the sub-factors within each category are prioritized by the AHP. The important contribution of contract selection to the construction industry’s success was demonstrated by Taye et al. [23]. The company status, the context of the project, and the project manager were the three factors considered. Torkayesh [14] used a hybrid approach combining the BWM with gray MARCOS to locate the most suitable locations for the disposal of healthcare waste. Initially, the locations were selected based on GIS information. Following the extraction of the factors affecting them, the BWM prioritized those factors. Lastly, they were ranked by G-MARCOS based on the factors that affected their performance. Using the gray theory and MARCOS, Badi and Pamucar ranked the supplier selection in the iron industry. To determine the validity of their method, they ranked these suppliers and then performed a sensitivity analysis. Using the hybrid MCDM methods, such as the BWM and gray MOORA, Celikbilek [17] determined which type of public transportation was the most suitable for Budapest. Fazollahtabar [18] demonstrated how to evaluate these vendors and determine the best provider. Zhang et al. [19] selected production with the intuitionistic fuzzy TODIM method. This research was conducted on a mobile phone to find the purchasing preferences and the factors that affected them. Zhang et al. [20] applied the interval fuzzy TOPSIS type 2 in the Beijing subway via utility theory. In their research, the operations risk factors were extracted and prioritized for risk reduction.
Table 1 shows the factors of the contract selection (Phase I).
Table 1. Factors of the SCM contract selection.
References Factor
[21][22] Flexibility
[23][24] Volatility of demand (not fixed demand)
[25][26] Uncertainty (change all of the factors that are related to the contract)
[27][28] Excess inventory
[29][30] Distortion of information
[31][32] Responsiveness
[33][34] Cost
[33][35] Quality
[36][37] Organization
[38][39] Contracting process
[40][41] Project characteristics
[42][43] Type of contract
[37][44] Organization structure
[45][46] Company status
[47] Tariff and green standard

This entry is adapted from the peer-reviewed paper 10.3390/math10183230

References

  1. Youssouf, M.A.L.I. Effects of World Oil Price Movement on Macroeconomic Performance in Saudi ARABIA. 2019. Available online: http://psasir.upm.edu.my/id/eprint/83078/ (accessed on 15 July 2022).
  2. Bradshaw, M.; van de Graaf, T.; Connolly, R. Preparing for the new oil order? Saudi Arabia and Russia. Energy Strateg. Rev. 2019, 26, 100374.
  3. Hamilton, J.D. What is an oil shock? J. Econom. 2003, 113, 363–398.
  4. Yazdi, A.K.; Wanke, P.F.; Hanne, T.; Bottani, E. A decision-support approach under uncertainty for evaluating reverse logistics capabilities of healthcare providers in Iran. J. Enterp. Inf. Manag. 2020, 33, 991–1022.
  5. Yazdi, A.K.; Wanke, P.F.; Hanne, T.; Abdi, F.; Sarfaraz, A.H. Supplier selection in the oil & gas industry: A comprehensive approach for Multi-Criteria Decision Analysis. Socioecon. Plann. Sci. 2021, 79, 101142.
  6. Poberschnigg, T.F.d.; Pimenta, M.L.; Hilletofth, P. How can cross-functional integration support the development of resilience capabilities? The case of collaboration in the automotive industry. Supply Chain Manag. Int. J. 2020, 25, 789–801.
  7. Governatori, G.; Idelberger, F.; Milosevic, Z.; Riveret, R.; Sartor, G.; Xu, X. On legal contracts, imperative and declarative smart contracts, blockchain systems. Artif. Intell. Law 2018, 26, 377–409.
  8. Bakhat, R.; Rajaa, M. Developing a novel Grey integrated multi-criteria approach for enhancing the supplier selection procedure: A real-world case of Textile Company. Decis. Sci. Lett. 2019, 8, 211–224.
  9. Aggarwal, S.; Srivastava, M.K. A grey-based DEMATEL model for building collaborative resilience in supply chain. Int. J. Qual. Reliab. Manag. 2019.
  10. Elabed, S.; Shamayleh, A.; Daghfous, A. Sustainability-oriented innovation in the health care supply chain. Comput. Ind. Eng. 2021, 160, 107564.
  11. Behnke, K.; Janssen, M. Boundary conditions for traceability in food supply chains using blockchain technology. Int. J. Inf. Manag. 2020, 52, 101969.
  12. Bocek, T.; Stiller, B. Smart contracts—Blockchains in the wings. In Digital Marketplaces Unleashed; Springer: Berlin/Heidelberg, Germany, 2018; pp. 169–184.
  13. Shermin, V. Disrupting governance with blockchains and smart contracts. Strateg. Chang. 2017, 26, 499–509.
  14. Hu, K.-J.; Vincent, F.Y. An integrated approach for the electronic contract manufacturer selection problem. Omega 2016, 62, 68–81.
  15. Walter, J. Safety management at the frontier: Cooperation with contractors in oil and gas companies. Saf. Sci. 2017, 91, 394–404.
  16. Dolgui, A.; Ivanov, D.; Potryasaev, S.; Sokolov, B.; Ivanova, M.; Werner, F. Blockchain-oriented dynamic modelling of smart contract design and execution in the supply chain. Int. J. Prod. Res. 2020, 58, 2184–2199.
  17. Çelikbilek, Y.; Moslem, S.; Duleba, S. A combined grey multi criteria decision making model to evaluate public transportation systems. Evol. Syst. 2022, 1–15.
  18. Fazlollahtabar, H.; Kazemitash, N. Green supplier selection based on the information system performance evaluation using the integrated Best-Worst Method. Facta Univ. Ser. Mech. Eng. 2021, 19, 345–360.
  19. Zhang, Z.; Guo, J.; Zhang, H.; Zhou, L.; Wang, M. Product selection based on sentiment analysis of online reviews: An intuitionistic fuzzy TODIM method. Complex Intell. Syst. 2022, 8, 3349–3362.
  20. Zhang, Z.; Zhao, X.; Qin, Y.; Si, H.; Zhou, L. Interval type-2 fuzzy TOPSIS approach with utility theory for subway station operational risk evaluation. J. Ambient Intell. Humaniz. Comput. 2021, 1–15.
  21. Li, J.; Luo, X.; Wang, Q.; Zhou, W. Supply chain coordination through capacity reservation contract and quantity flexibility contract. Omega 2021, 99, 102195.
  22. Farahani, M.H.; Dawande, M.; Gurnani, H.; Janakiraman, G. Better to Bend than to Break: Sharing Supply Risk Using the Supply-Flexibility Contract. Manuf. Serv. Oper. Manag. 2021, 23, 1257–1274.
  23. Kantari, L.A.; Pujawan, I.N.; Arvitrida, N.I.; Hilletofth, P. Mixing contract-based and on-demand sourcing of transportation services for improved supply chain performance under supply uncertainties. Int. J. Syst. Sci. Oper. Logist. 2021, 1–17.
  24. Avinadav, T.; Chernonog, T.; Meilijson, I.; Perlman, Y. A consignment contract with revenue sharing between an app developer and a distribution platform. Int. J. Prod. Econ. 2022, 243, 108322.
  25. Yucekaya, A. Electricity trading for coal-fired power plants in Turkish power market considering uncertainty in spot, derivatives and bilateral contract market. Renew. Sustain. Energy Rev. 2022, 159, 112189.
  26. Farsi, M.; Erkoyuncu, J.A. An agent-based approach to quantify the uncertainty in Product-Service System contract decisions: A case study in the machine tool industry. Int. J. Prod. Econ. 2021, 233, 108014.
  27. Li, Y.; Deng, S.; Zhang, Y.; Liu, B. Coordinating the retail supply chain with item-level RFID and excess inventory under a revenue-cost-sharing contract. Int. Trans. Oper. Res. 2021, 28, 1505–1525.
  28. Chakraborty, A.; Verma, N.K.; Chatterjee, A.K. A Single Supplier Multi Buyer Supply Chain Coordination under Vendor-managed Inventory: Ensuring Buyers’ Interests in a Decentralized Setting. IIM Kozhikode Soc. Manag. Rev. 2022, 22779752211072936.
  29. Clark, A.; Reggiani, G. Contracts for acquiring information. arXiv 2021, arXiv:2103.03911.
  30. Zhang, J.; Qi, L.; Tong, S. Dynamic contract under quick response in a supply chain with information asymmetry. Prod. Oper. Manag. 2021, 30, 1273–1289.
  31. Ebekozien, A.; Aigbavboa, C.; Nwaole, A.N.C.; Dako, E.O.; Awo-Osagie, A.I. Quantity surveyor’s ethical responsiveness on construction projects: Issues and solutions. Int. J. Build. Pathol. Adapt. 2021.
  32. Aïd, R.; Possamaï, D.; Touzi, N. Optimal electricity demand response contracting with responsiveness incentives. Math. Oper. Res. 2022.
  33. Akin, F.D.; Polat, G.; Turkoglu, H.; Damci, A. A crashing-based time-cost trade-off model considering quality cost and contract clauses. Int. J. Constr. Manag. 2021, 1–10.
  34. Samanta, B.; Giri, B.C. A two-echelon supply chain model with price and warranty dependent demand and pro-rata warranty policy under cost sharing contract. Decis. Mak. Appl. Manag. Eng. 2021, 4, 47–75.
  35. Lee, J.H.; Mistur, E.; Liu, L.; Ashuri, B. Determining contract requirements for quality assurance program in innovative project delivery. In Proceedings of the Construction Research Congress 2022, Arlington, VA, USA, 9–12 March 2022; pp. 179–188.
  36. Bugrov, O.; Bugrova, O. Formalization of Selection of Contract-Organizational Project Delivery Strategy. 2018. Available online: https://iopscience.iop.org/article/10.1088/1755-1315/304/3/032001/meta (accessed on 10 July 2022).
  37. Cheaitou, A.; Larbi, R.; Al Housani, B. Decision making framework for tender evaluation and contractor selection in public organizations with risk considerations. Socioecon. Plann. Sci. 2019, 68, 100620.
  38. Ni, J.; Zhao, J.; Chu, L.K. Supply contracting and process innovation in a dynamic supply chain with information asymmetry. Eur. J. Oper. Res. 2021, 288, 552–562.
  39. Sarvari, H.; Chan, D.W.M.; Ashrafi, B.; Olawumi, T.O.; Banaitiene, N. Prioritization of Contracting Methods for Water and Wastewater Projects Using the Fuzzy Analytic Hierarchy Process Method. Energies 2021, 14, 7815.
  40. Novikov, S.V.; Dmitriev, O.N. Vision of genesis of presentation of Hi-Tech project during competitive selection. Russ. Eng. Res. 2018, 38, 320–322.
  41. Ding, J.; Zhai, W.; Wang, Z.; Zhang, K.; Cai, J. Modelling and Design Analysis of Contract Payment Methods in Civil Engineering Projects. In Proceedings of the IOP Conference Series: Earth and Environmental Science, Moscow, Russia, 27 May–6 June 2019; Volume 304, p. 32001.
  42. Camci, A.; Çimen, Ö.; Gül, S. Selection of contract type in construction projects using spherical AHP method. In Proceedings of the International Online Conference on Intelligent Decision Science, Istanbul, Turkey, 7–8 August 2020; pp. 531–547.
  43. Zhou, Y.-W.; Lin, X.; Zhong, Y.; Xie, W. Contract selection for a multi-service sharing platform with self-scheduling capacity. Omega 2019, 86, 198–217.
  44. Zhao, X.; Gu, B.; Gao, F.; Chen, S. Matching model of energy supply and demand of the integrated energy system in coastal areas. J. Coast. Res. 2020, 103, 983–989.
  45. Carbonara, N.; Pellegrino, R. Public-private partnerships for energy efficiency projects: A win-win model to choose the energy performance contracting structure. J. Clean. Prod. 2018, 170, 1064–1075.
  46. Ruml, A.; Qaim, M. Effects of marketing contracts and resource-providing contracts in the African small farm sector: Insights from oil palm production in Ghana. World Dev. 2020, 136, 105110.
  47. Toktaş-Palut, P. An integrated contract for coordinating a three-stage green forward and reverse supply chain under fairness concerns. J. Clean. Prod. 2021, 279, 123735.
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