Selecting appropriate suppliers is one of the most critical decisions in supply chain management, as it can directly influence organizational efficiency, flexibility, and competitiveness. In the power industry, where timely provision of high-quality equipment, parts, and services is essential, poor supplier selection can lead to project delays, higher maintenance costs, service disruptions, and reduced customer satisfaction [1]. Therefore, supplier selection is not merely an operational issue but a strategic decision that strengthens competitive advantage and reduces overall supply chain costs. This decision affects not only purchase price but also product quality, delivery reliability, and the organization’s ability to respond flexibly to changing market demands. In many industries, a substantial portion of production costs is associated with the procurement of raw materials and components; hence, accurate and systematic supplier evaluation can significantly reduce risks and avoid unnecessary costs [2].

In the power industry, procured equipment must comply with strict technical standards and safety requirements. Even minor errors in supplier selection can reduce grid reliability, increase maintenance time, and create potential safety hazards. For this reason, developing scientific, structured frameworks for supplier evaluation is essential not only for economic reasons but also for operational performance and system safety [3]. In recent years, numerous studies have addressed supplier evaluation and ranking in various industries; however, research in the power sector, particularly those employing advanced fuzzy-based methods, remains limited.

This study identifies effective criteria for evaluating and ranking suppliers in the power industry and determines their relative importance using the Analytical Hierarchy Process (AHP). Subsequently, the study selects seven suppliers of the Mazandaran Regional Electricity Company as a case study and ranks them using the fuzzy TOPKOR method. The main innovation of this research lies in the comprehensive identification of 33 evaluation criteria for suppliers in the power industry and the application of the fuzzy TOPKOR method in this domain. The main research question is: Which criteria are effective for evaluating and ranking suppliers in the power industry, and how can they be used to rank suppliers of the Mazandaran Regional Electricity Company?

The sub-research questions are as follows:

I. What Criteria are identified for supplier evaluation in the power industry?

II. What is the relative importance of each evaluation criterion?

III. What are the performance levels and scores of suppliers based on the identified criteria?

The paper is organized as follows. Section 2 reviews the relevant literature, Section 3 describes the research methodology, Section 4 presents the results, and Section 5 concludes the study and provides suggestions for future research.

Selecting appropriate suppliers is one of the most important managerial issues in supply chains across industries, including the power industry, where uncertainty and complex decision environments make the problem inherently multidimensional. Therefore, numerous studies have used Multi-Criteria Decision-Making (MCDM) methods such as AHP, Differential Evolution Algorithm (DEA), fuzzy methods, and hybrid models to address supplier selection problems in different contexts.

Hendiani et al. [4] investigated sustainable supplier selection. They developed a new measurement and ranking approach based on the Multi-Attributive Rank Calculation Operation with Simple Ratio (MARCOS) method for the healthcare industry. This method improves decision-making by considering ideal and anti-ideal solutions from the initial matrix formation stage, providing more accurate utility assessment, introducing new utility aggregation mechanisms, and maintaining stability even with many criteria and alternatives. The study emphasized the importance of supplier selection in medical organizations and highlighted sustainability as a critical strategic issue that has been insufficiently implemented in the private healthcare sector. Their case study ranked eight suppliers based on 21 sustainability criteria, and comparative analysis with six other MCDM methods, along with sensitivity analysis, confirmed the validity and superiority of the MARCOS approach. In another study, Wang and Chen [5], focusing on supply chain resilience during the COVID-19 crisis, proposed a hybrid framework combining AHP, bi-objective Mixed-Integer Nonlinear Programming (MINLP), and Genetic Algorithm (GA) to support alternative supplier selection. Their model generated diverse supplier portfolios while maintaining optimality, enhancing decision-making flexibility, and improving computational efficiency by 75% through the use of GA. Similarly, Sharafi et al. [6] proposed a computational framework for green supplier selection in the automotive industry based on expert judgment aggregation and Fuzzy DEA, further enhanced with Fuzzy Combinative Distance-Based Assessment (CODAS) to address ranking limitations and improve discrimination among efficient suppliers. The results showed that the hybrid approach provides a more complete and distinct ranking of alternatives.

Nasri et al. [7] developed a multi-stage integrated framework for sustainable supplier selection in the oil industry using Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL), Analytic Network Process (ANP), and DEA to balance economic, social, and environmental objectives. Their results identified interdependencies among criteria, used these relationships to compute accurate weights, and validated the model through a case study involving fifteen oil supply companies using the Andersen–Petersen model for final efficiency evaluation.

Więckowski et al. [8] critically examined classical MCDA approaches and highlighted the challenge of aligning decision models with dynamic customer preferences in uncertain environments. They identified a theoretical gap in integrating ideal solutions with personalized preferences. They proposed a hybrid approach that combines preference-ranking mechanisms with ideal-solution-based methods, enabling more personalized and accurate decision-making, as validated in electric vehicle selection. Their findings demonstrated that the proposed framework improves interpretability and decision accuracy compared to advanced machine learning models.

Bayanati et al. [9] investigated innovative supplier selection, focusing on inter-organizational collaboration, using a hybrid Delphi, Fuzzy DEMATEL, and Fuzzy Analytic Network Process (FANP) approach. Their findings revealed that while supplier willingness plays a central role in system interactions, supplier capability is the most influential criterion in weighting, followed by willingness and supply risk, with geographical and organizational proximity identified as key sub-criteria driving innovative supplier selection.

Finally, Abdulla and Baryannis [10] proposed a hybrid framework integrating interpretable artificial intelligence techniques with classical MCDM methods to address complexity in large-scale procurement environments. Their model reduces dimensionality while preserving interpretability and stakeholder acceptance, and validation through case studies in the oil, gas, and aerospace industries showed performance comparable to advanced machine learning models, with the added advantage of transparency and alignment with managerial decision-making processes. Overall, the literature indicates that supplier selection is a complex and multidimensional problem, and fuzzy and MCDM-based approaches play a crucial role in handling uncertainty and human judgment. However, despite significant advancements, comprehensive studies that simultaneously consider a large set of evaluation criteria (such as 33 criteria) and apply the fuzzy TOPKOR method in the power industry remain limited, which is the research gap this study addresses.

This article focuses on developing evaluation and ranking criteria for suppliers in the power industry. This section describes the main stages of the research, the statistical sample, and the data collection methods used in the study.

This section presents the results of implementing the research steps. Table 2 presents the criteria and their corresponding weights obtained using the AHP method. The fuzzy and defuzzified decision matrices are also provided in Tables 3 and 4, respectively. Furthermore, the supplier ranking results obtained using the TOPKOR method are presented in Table 5.

Selecting appropriate suppliers in the power industry is strategically important because this sector is infrastructure-oriented and highly sensitive. The results showed that applying MCDM approaches, particularly integrating the AHP and Fuzzy TOPSIS methods, provides an efficient, systematic tool for supplier evaluation and ranking. The findings indicate that qualitative and performance-related criteria, such as quality, reliability, and on-time delivery, play a more decisive role than purely economic criteria in the supplier selection process within the power industry. This finding suggests that excessive emphasis on cost-related factors, without adequate consideration of technical and operational criteria, may pose significant risks to the sustainable operation of power networks. The proposed model can serve as a practical decision-making framework for companies operating in the power sector, particularly electricity distribution companies and regional power utilities, and can be employed in supplier selection, periodic evaluation, and supplier performance management processes.

For future research, advanced MCDM methods such as the Best–Worst Method (BWM), DEMATEL, ANP, and gray theory-based approaches may be integrated for criteria weighting, and their results may be compared with those obtained using the AHP method. Furthermore, supplier ranking can be conducted using alternative methods such as VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), Weighted Aggregated Sum Product Assessment (WASPAS), Complex Proportional Assessment (COPRAS), and MARCOS, and the degree of convergence or divergence between their results and those of the Fuzzy TOPKOR method can be examined. In addition, applying the proposed framework to other regional electricity companies across Iran and comparing the results may help enhance the generalizability and external validity of the proposed model.

The authors sincerely appreciate everyone who contributed to completing this research.

This research was conducted without financial support from any public, private, or non-profit organization.

The data used in this study are available from the corresponding author upon reasonable request.

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