2.1. Supplier Selection
Suppliers play crucial roles in supply chain management, producing components, ensuring product quality, and indirectly managing and assisting with the operational costs of their partners [15
]. The quality level of their products determines the degree to which the quality of the final product can be guaranteed and the ability of all the members of the supply chain to control costs. For this reason, the selection of the right suppliers is vital for companies. Supplier selection is a decision-making process that involves a number of steps and several criteria—both quantitative and qualitative. Table 1
summarizes important criteria associated with supplier selection. These criteria are as follows:
Cost/Price: the procurement costs, labor costs, material costs, and transportation costs of a company;
Quality: the quality of supplier products;
Delivery period: the time to delivery of the suppliers;
Technology: the implementation of modern technology;
Relations: the duration of collaboration and the closeness of relationships with the suppliers;
Service: the ability of the suppliers to support and coordinate with products or technologies;
Communication: the ability of the suppliers to respond regarding products or services and communicate with internal and external partners;
Green: whether the products or packaging of the suppliers have green marks and follow the 3 Rs (reduce, reuse and recycle);
Sustainability: the impact of the suppliers (or their products) on society, the economy, and the environment.
In addition to conventional quantitative indices such as the quality, delivery period, and cost/price, this also includes qualitative indices such as supplier service, relations, communication, green, and sustainability. The four criteria of cost/price, quality, delivery and service are the most commonly used factors for the supplier evaluation and selection problem.
Researchers have applied a number of evaluation standards to this problem, including the multiple-criteria decision-making (MCDM) model [16
], the analytic hierarchy process (AHP) [17
], the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) [18
], and grey analysis [19
]. However, most of these methods use crisp numbers to process decision-making information and cannot process uncertain or imprecise information. Furthermore, the imprecision of the information source, including unquantifiable information, incomplete information, and some unknown information, makes it even more difficult to efficiently select suppliers. Thus, these approaches have been ineffective for supplier selection problems due to the high degree of uncertainty in supplier selection. For this, a key issue is how to evaluate and select the most suitable supplier for companies.
2.2. Fuzzy Theory in Supplier Selection
Decisions can be described as the final outcome of certain psychological and reasoning processes. They are often based on the judgment of experts and/or the values of stakeholders. Decisions may be influenced by subjectivity and uncertainty, and experts therefore tend to rely on their own standards. However, this expert subjectivity constitutes the main flaw in their decision-making process. Zadeh [35
] stated that human thinking and decision-making processes often include impressions, emotions, and intuition. Therefore, as long as there are people involved, uncertainty and ambiguity will be present. This means that traditional two-value logic rarely applies in practice. To solve this problem, Zadeh [8
] presented a fuzzy set and the concept of membership grades. Thereafter, various researchers have tackled the supplier evaluation and selection problem in fuzzy environments. Karsak and Dursun [36
] presented an integrated fuzzy MCDM method using quality function deployment (QFD) to create a two-tuple linguistic representation model to assess supplier selection in a private hospital in Istanbul. Lima-Junior and Carpinetti [20
] utilized a multi-criteria approach based on fuzzy QFD to screen and select the best supplier for a company in the automotive industry. Parkouhi and Ghadikolaei [37
] applied the fuzzy analytic network process (ANP) and grey ViseKriterijumska optimizacija i Kompromisno Resenje (VIKOR in Serbian, which means multi-criteria optimization and compromise solution in English) techniques to choose the optimal supplier for firms in the wood and paper industry. Banaeian et al. [38
] introduced a fuzzy group decision-making method using TOPSIS, VIKOR, and grey relational analysis (GRA) methods to assess green supplier selection for the agri-food industry. Chen et al. [39
] used the six sigma quality indices (SSQIs) to develop a fuzzy green supplier selection model for the performance measurement of thin-film-transistor liquid-crystal display (TFT-LCD) panel manufacturers. Feng et al. [40
] developed an integrated fuzzy grey TOPSIS method for supplier assessment and selection for a collaborative manufacturing company. Gupta et al. [41
] utilized an integrated fuzzy AHP with multi-attributive border approximation area comparison (MABAC), weighted aggregated sum-product assessment (WASPAS), and TOPSIS to find the most suitable green suppliers for the automotive industry.
In many real-world situations, fuzzy linguistic data are often included in a supplier selection environment since human judgement is usually vague and the decision-making context involves complexity and uncertainty. Under such circumstances, linguistic labels are more appropriate than quantitative values for the description of expert preferences or evaluations. In fuzzy theory, linguistic labels can be expressed as fuzzy numbers using conversion scales. By converting linguistic labels into fuzzy numbers, the fuzzy concepts of “Good” and “Bad” can be converted into clear and computable values. For example, the linguistic labels “Very Good (VG)”, “Good (G)”, “Normal (N)”, “Poor (P)”, and “Very Poor (VP)” can be converted to the following triangular fuzzy numbers: “VG (7, 9, 10)”, “G (5, 7, 9)”, “N (3, 5, 7)”, “P (1, 3, 5)”, and “VP (0, 1, 3)”. Table 2
shows these linguistic labels and their corresponding fuzzy numbers [42
]. The fuzzy numbers can then be converted into an integrated crisp value through defuzzification.
Although these approaches are helpful, supplier selection involves knowledge from multiple fields, and cognitive differences and information loss may take place when the opinions of decision-makers are combined (that is, how much variation or dispersion is in the data set). From a statistical perspective, data with an overly-high standard deviation may indicate the existence of confusion or uncertainty by the decision-maker and/or the problem or issue. If companies do not address this, the resulting decision may be poor and affect the operating performance of the company. Thus, companies need an effective approach to address the aforementioned issues.
To demonstrate the superiority of the proposed approach, we compared it to other existing supplier selection methods, the results of which are shown in Table 3
. We selected the following existing methods for comparison: TOPSIS [19
], ANP [43
], Decision Making Trial and Evaluation Laboratory (DEMATEL) [44
], and the Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE) [45
], AHP [46
]. The studies conducted by Chen and Zou [19
], Bakeshlou et al. [43
], and Krishankumar et al. [45
] all used numerical simulations to verify the validity of the methods they proposed, and the evaluation criteria adopted by Chen and Zou [19
] had no reference basis and were not established by experts. Thus, whether these methods are reasonable is open to question. Furthermore, the investigation conducted by Hu et al. [44
] did not take uncertainty or the fuzzy linguistics of evaluators into account. Above all, none of these studies considered cognitive differences among evaluators. Therefore, the proposed method of this study is more reasonable and valid for supplier selection.