## 1 Introduction  
Rapid advances in technology have resulted in the flourishing of the electronics and information industries; as such, the market demand for capacitors increases daily. Capacitors are a type of passive component similar to batteries. They can store electrical currents temporarily as well as block direct currents, couple alternating currents, store energy, and serve as bypasses. There are numerous types of capacitors, each with a wide range of applications; for example, high-voltage power film capacitors are commonly utilized in aircraft, ocean-going vessels, and high-frequency communication products. Low-quality high voltage power film capacitors working under high voltage lead to an increased dissipation factor (DF), which damages the internal medium. Continued use under such circumstances can lead to overly-high operating temperatures in the capacitors which result in deformation and burnout. Mild cases may lead to energy drops or failure, while severe cases may result in an explosion. Suppliers must therefore be selected with care. In traditional supply chain management, members of the supply chain view each other competitively, and the overall concern is keeping costs down. However, if suppliers are selected based on cost alone, a hostile relationship is created between the supplier and buyer, negatively affecting product quality and service [1]. Therefore, companies must evaluate suppliers from multiple angles to protect each other's interests and create good and lasting partnerships [2; 3; 4]. In general, managers often deliver their judgments based on individual subjective preferences or experience, or adopt Likert-type questionnaires to score supplier characteristics. However, many researchers [5; 6; 7] have argued that the Likert scale cannot appropriately reflect the feelings of an evaluator toward certain quantitative indices. For instance, an evaluator may think that the level of service provided by a supplier falls somewhere between acceptable and satisfactory. This brings uncertainty to the decision-making process. Information loss may also take place when the opinions of decision-makers are combined. As a consequence, it is difficult for company managers to collect accurate information by which they can select suitable suppliers, hindering their ability to formulate appropriate management strategies. Fuzzy theory can be used to define data or statuses that cannot be described in detail, such as color preferences or age groups. Their purpose is to assist decision makers or researchers in expressing the information conveyed by their data in a more precise manner [8]. One approach in fuzzy theory, called the membership function, presents the degrees of fuzziness in the research subjects using values between 0 and 1. This approach enumerates subjective judgments so that the results better match human thinking patterns. Fuzzy theory can be used to define the grade of membership of different actors in different groups, and the factors (criteria) defining this membership are not necessarily only linguistic. Fuzzy theory has an extremely wide range of applications. Fuzzy theory research can be found in fields ranging from engineering and technology to social sciences, such as robot control [9], signal and information processing [10], education [11], and decision-making [12]. In this study, fuzzy theory is commonly applied to describe linguistic ambiguity [12; 13]; however, previous studies have neglected to deal with cognitive differences among evaluators in decision-making processes that rely on the Likert scale (that is, variability within the data set). In the past, several multi criteria group decision making (MCGDM) approaches have been employed that address the problem of heterogeneity in evaluations and their consensus [14]; however, these approaches have some disadvantages, such as a lack of data interpretation (that is, how much variation or dispersion is in the data set) and a higher degree of imprecision compared to the traditional statistical methods.