Francisco Rodrigues Lima Junior

A comparison between Fuzzy AHP and Fuzzy TOPSIS methods to supplier selection

Abstract Supplier selection has become a very critical activity to the performance of organizations and supply chains. Studies presented in the literature propose the use of the methods Fuzzy TOPSIS (Fuzzy Technique for Order of Preference by Similarity to Ideal Solution) and Fuzzy AHP (Fuzzy Analytic Hierarchy Process) to aid the supplier selection decision process. However, there are no comparative studies of these two methods when applied to the problem of supplier selection. Thus, this paper presents a comparative analysis of these two methods in the context of supplier selection decision making. The comparison was made based on the factors: adequacy to changes of alternatives or criteria; agility in the decision process; computational complexity; adequacy to support group decision making; the number of alternative suppliers and criteria; and modeling of uncertainty. As an illustrative example, both methods were applied to the selection of suppliers of a company in the automotive production chain. In addition, computational tests were performed considering several scenarios of supplier selection. The results have shown that both methods are suitable for the problem of supplier selection, particularly to supporting group decision making and modeling of uncertainty. However, the comparative analysis has shown that the Fuzzy TOPSIS method is better suited to the problem of supplier selection in regard to changes of alternatives and criteria, agility and number of criteria and alternative suppliers. Thus, this comparative study contributes to helping researchers and practitioners to choose more effective approaches for supplier selection. Suggestions of further work are also proposed so as to make these methods more adequate to the problem of supplier selection.

Uma comparação entre os métodos TOPSIS e Fuzzy-TOPSIS no apoio à tomada de decisão multicritério para seleção de fornecedores

Supplier selection is the most critical activity of purchasing and influences both the quality of manufactured products and the buyers performance. In the literature, dozens of multicriteria methods have been explored to support decision making for supplier selection. Among such methods, TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) and Fuzzy-TOPSIS (an adaptation of the first) have been often used because of their simplicity of usage and ability to simultaneously evaluate an unlimited number of alternatives and criteria. Although many authors have suggested the adoption of TOPSIS and Fuzzy-TOPSIS to deal with the supplier selection problem, advantages of usage and limitations of both methods (when they are applied to this problem domain) have not been discussed in the literature. To fill such a gap, this study compares TOPSIS and Fuzzy-TOPSIS methods concerning computational complexity, structure of the algorithms and results from their application to the same real case of supplier selection. The methods were implemented through MATLAB(r) and applied to supplier selection in an automotive company. The results show that TOPSIS requires less effort for data collection and computer processing. However, Fuzzy-TOPSIS does not suffer ranking reversal and is suitable to model qualitative and imprecise information. The results can guide researchers and practitioners in choosing the most appropriate method to support the supplier selection problem at hand.

Métodos de decisão multicritério para seleção de fornecedores: um panorama do estado da arte

Supplier selection is a very important activity in supply chain management, and it directly influences the performance of organizations, product quality, and other factors related to the production process. Supplier selection has been addressed as a multicriteria decision problem that considers that the requirements of the buyers should focus on criteria used to evaluate the supplier options available. Various multicriteria decision methods or a combination of them have been proposed to tackle this problem. Three articles were identified during a literature review on this topic. The most recent article cited studies from 2000 to 2008. This paper also includes the review of other 120 articles that were retrieved from the literature search using different national and international data bases. Based on the articles reviewed, the most commonly used multicriteria decision methods, context of applications, study sources, among other relevant information. The results show that methods capable of dealing with uncertainty and qualitative information are the most common. Also, it seems that there is a gap between real world needs and the proposed theoretical models. Finally, the present study identifies areas that need further research.

Proposal of a model for sales and operations planning (S&OP) maturity evaluation

A successful S&OP implementation can bring many benefits to organizations, and after its implementation, the S&OP process can evolve and reach higher levels of maturity. Considering that, only through a measurement system, goals and benefits can be achieved, thus, it is essential to assess S&OP maturity level. Many papers on literature adopt a quantitative perspective on S&OP, but just few of them deal with uncertainty present in S&OP decision-making, such as maturity model assessment process that carries subjectivity and uncertainty. Thus, this study proposes a decision making model based on fuzzy theory to evaluate and to categorize S&OP maturity levels and to suggest strategies to increase S&OP maturity. A pilot application was conducted in two manufacturing organizations that have implemented the S&OP process. The results, according to the performance presented, suggest different actions must be taken in terms of ensuring enablers to S&OP implementation.