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Evaluation of Barriers to the Integration of Renewable Energy Technologies into Industries in Türkiye
(MDPI, 2026) Caloglu Buyukselcuk, Elif; Turan, Hakan
The transition to renewable energy technologies is one of the most important ways to achieve the sustainable development goals (SDGs) of affordable and clean energy (SDG7); industry, innovation and infrastructure (SDG9); responsible production and consumption (SDG12); and climate action (SDG13). The widespread use of renewable energy technologies in developing countries will reduce dependence on imported fossil resources, increase industrial competitiveness, and support low-carbon development. Despite all their advantages, the integration of renewable energy technologies into industrial and domestic systems in developing countries remains slow due to a number of barriers. Financial constraints, technical and technological deficiencies, political restrictions and uncertainties, and organizational and managerial inadequacies are some of the barriers to the widespread adoption of renewable energy technologies. This study aims to identify, classify, and prioritize the barriers to the implementation of renewable energy technologies by applying multi-criteria decision-making methods in a fuzzy environment, with T & uuml;rkiye considered as a case study. The relative importance of the barriers identified using the Single-Valued Spherical Fuzzy SWARA method was assessed, and their interconnections and significance were systematically demonstrated. The findings will contribute to the development of policy and management strategies aligned with global sustainability goals, thereby facilitating a more effective and equitable transition to clean and resilient energy systems.
Evaluation of the Barriers to Maintenance 4.0 for the Textile Industry via Pythagorean Fuzzy SWARA
(MDPI, 2025) Turan, Hakan; Buyukselcuk, Elif Caloglu; Çaloğlu Büyükselçuk, Elif
Maintenance 4.0 studies have become a focus for managers and employees when developing effective and efficient maintenance policies. In this study, the barriers to Maintenance 4.0 applications in the textile industry are investigated, and these barriers are weighted using the Stepwise Weight Assessment Ratio Analysis (SWARA) method based on Pythagorean fuzzy numbers. Solutions to address these barriers are presented. As a result of this study, Organizational and Managerial emerged as the most important main criterion. Operational was identified as the second most significant main criterion, followed by Technical Competence. Data-Related and Cybersecurity ranked fourth in terms of importance. On the other hand, Human Resources and Training and Financial were found to be the least important main criteria. These two criteria received lower importance scores compared to the others, with Financial being the criterion with the lowest overall significance. Sensitivity analyses were performed for six different scenarios by changing the importance weights of the decision-makers. The ranking of the criteria only slightly changed with the weights; this means that the results obtained in Case 1 are robust and reliable. Even in Case 6, where the expert weight ratios were completely reversed, the results did not change significantly. This highlights an important point regarding the reliability of the assessment.
Citation - WoS: 11
Citation - Scopus: 15
Evaluation of Industrial Iot Service Providers With Topsis Based on Circular Intuitionistic Fuzzy Sets
(Tech Science Press, 2024) Buyukselcuk, Elif Caloglu
Industrial Internet of Things (IIoT) service providers have become increasingly important in the manufacturing industry due to their ability to gather and process vast amounts of data from connected devices, enabling manufacturers to improve operational efficiency, reduce costs, and enhance product quality. These platforms provide manufacturers with real-time visibility into their production processes and supply chains, allowing them to optimize operations and make informed decisions. In addition, IIoT service providers can help manufacturers create new revenue streams through the development of innovative products and services and enable them to leverage the benefits of emerging technologies such as Artificial Intelligence (AI) and machine learning. Overall, the implementation of IIoT platforms in the manufacturing industry is crucial for companies seeking to remain competitive and meet the ever-increasing demands of customers in the digital age. In this study, the evaluation criteria to be considered in the selection of IIoT service provider in small and medium-sized (SME) manufacturing enterprises will be determined and IIoT service providers alternatives will be evaluated using the technique for order preference by similarity to an ideal solution (TOPSIS) method based on circular intuitionistic fuzzy sets. Based on the assessments conducted in accordance with the literature review and expert consultations, a set of 8 selection criteria has been established. These criteria encompass industry expertise, customer support, flexibility and scalability, security, cost-effectiveness, reliability, data analytics, as well as compatibility and usability. Upon evaluating these criteria, it was observed that the security criterion holds the highest significance, succeeded by cost-effectiveness, data analytics, flexibility and scalability, reliability, and customer support criteria, in descending order of importance. Following the evaluation of seven distinct alternatives against these criteria, it was deduced that the A6 alternative, a German service provider, emerged as the most favorable option. The identical issue was addressed utilizing sensitivity analysis alongside various multi-criteria decision-making (MCDM) methods, and after comprehensive evaluation, the outcomes were assessed. Spearman's correlation coefficient was computed to ascertain the association between the rankings derived from solving the problem using diverse MCDM methods.

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