Evaluation of Industrial IoT Service Providers with TOPSIS Based on Circular Intuitionistic Fuzzy Sets

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.