Selecting a SCM contract is one of the most common topics in the research area of SCM [13,14] because this is closely related to the performance of the company [15]. The existing research studies have conducted research on this topic across different economic sectors [16,17,18].

We were intrigued by Dolgui et al.’s [19] 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. [18] to design intelligent contracts in the oil industry’s SCM. Mohammed [19] 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. [20] 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. [21]; 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. [22], 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. [23]. 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. [24] 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. [25]). 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. [26]. The company status, the context of the project, and the project manager were the three factors considered. Torkayesh [17] 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 [20] determined which type of public transportation was the most suitable for Budapest. Fazollahtabar [21] demonstrated how to evaluate these vendors and determine the best provider. Zhang et al. [22] selected production with the intuitionistic fuzzy TODIM method. This study was conducted on a mobile phone to find the purchasing preferences and the factors that affected them. Zhang et al. [23] 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).