The difficulty of choosing suppliers of raw shrimp makes selecting raw material suppliers in the fisheries sector a multi-criteria decision-making problem. The aim of this research is to develop an optimal supplier selection model for the shrimp industry within a fuzzy environment. FANP and WASPAS methods are combined in this study to develop a fuzzy MCDM model to support the supplier selection process in the shrimp industry. FANP and WASPAS methods were chosen due to their availability in many decision-making software, which allows the proposed model to be easily applied in practical situations.
The FANP-WASPAS model can support optimal decision-making because it considers problems based on many criteria and allows the decision-makers to check the correlation between criteria. It also considers the ambiguity, uncertainty, and subjectivity of many different decision makers. Therefore, the model in this study can support companies in the shrimp industry in making optimal decisions regarding supplier selection.
The EU is currently the fourth largest shrimp consumer market purchasing from Vietnam, after the US, Japan, and China. However, the EU has strict requirements for shrimp products imported from other countries. To meet the above requirements, frozen shrimp exporters must improve production systems, improve product quality, and optimally select suitable raw suppliers.
As the consumers and governments become increasingly concerned with the sustainability of products, it is extremely important for exporters to identify optimal frozen shrimp suppliers who can satisfy the requirements of EU importers.
Therefore, the frozen shrimp supplier evaluation and selection process is a decision-making process that involves multiple criteria, which can be quantitative or qualitative in nature.
Multi-criteria decision-making processes can be supported using MCDM methods such as a Combined Compromise Solution (CoCoSo), Data Envelopment Analysis (DEA), or Multi-criteria Optimization and Compromise Solution (VIKOR).
In many cases, the selection criteria may consist not only of quantitative, but also qualitative, criteria. In these cases, fuzzy theory is integrated into the MCDM method to create fuzzy MCDM models to support the decision-making processes within uncertain decision making environments.
This study aims to develop an MCDM model based on Fuzzy Analytical Network Process (FANP) and Weighted Aggregated Sum Product Assessment (WASPAS) to support the frozen shrimp supplier evaluation and selection process within a fuzzy environment.
In the past few decades literature has analyzed and employed MCDM models to support supplier evaluation and selection processes in different industries to address criteria in both quantitative and qualitative forms.
Each of these models are unique and different from each other as each model uses a unique set of criteria or uses distinct MCDM methods. In some instances, these MCDM methods are applied in combination with fuzzy set theory to solve decision-making problems with qualitative criteria.
MCDM methods are frequently employed in different decision-making problems in different industries and sectors. The MIVES multi-criteria analysis network which combined multi-attribute utility theory (MAUT) and MDCM is also applied in different decision-making problems such as public investment projects evaluation and selection problem and assessment process of urban-pavement conditions.
Miranda-Ackerman et al (2019). developed a green supplier selection model in agrofood industry supply chains based on TOPSIS method in combination with a multi-objective decision-making model.
Alamanos et al (2018). employed four MCDM techniques—multi attribute utility theory (MAUT), AHP, TOPSIS and ELECTRE— to create a multi-criteria analysis tool to support the water resource management strategies evaluation process.
Karacan et al (2020). introduced a novel approach to the chickpea cultivars selection problem under stress conditions using FAHP and goal programming technique. In supply chain management, MCDM models are commonly employed in decision support systems.
“One of the common use cases of these systems is to solve supplier selection problems.”
While there have been multiple MCDM models introduced to support supplier selection problems, none of these is developed for the frozen shrimp industry, especially under un- certain decision-making environment.
Martinez-Cordero (2004) developed a MCDM model to evaluate and select sustainable shrimp farming method. Gangadharan et al (2016). employed an AHP-based decision-making support model for the ground water vulnerability assessment process of shrimp farming area.
“This research study’s goal is to develop a robust and effective supplier selection decision support tool for frozen shrimp exporters under fuzzy environment by combining FANP and WASPAS methods.”
The FANP method is chosen due to its advantages over FAHP in complex decision-making problems where there is dependency between criteria. Furthermore, FANP and WAPAS methods are also easy-to-understand and readily available in many decisions support software, which increase the proposed model usability.
Fuzzy Analytic Network Process (FANP) Model
The combination of fuzzy theory and ANP/AHP methods is widely applied in similar decision-making problems. The FANP method is chosen to calculate the criteria weights in this study due to its ability to handle interdependent criteria which is common in supplier selection problems, as well as its ability to represent the uncertain nature of the decision making process.
Furthermore, FANP method is also widely available in different decision-making software which helps improve the proposed method’s usability.
Theoretical weaknesses of the AHP/ANP are primarily: the rank reversal problem, the priorities derivation method, and the comparison scale. Solving a reversal problem and performing a preferences aggregation with the use of a left eigenvector method should, as a result, produce a reverse sequence of elements which were pairwise- compared in a matrix.
Therefore, it is important to check the consistency of the pairwise comparison matrix to ensure that the model can perform adequately. The FANP model is applied to calculate the weights of the selection criteria and sub criteria.
Weighted Aggregated Sum Product Assessment (WASPAS)
The WASPAS method is applied to calculate the ranking of the alternatives due to the method’s simplicity and easy-to-understand nature which adds to the proposed model’s applicability. In the WASPAS method, each alternative ranking score is the product of the scale rating of each criterion of strength by the criterion’s significance weight.
From the reference documents and expert analysis, the authors identified a list of criteria. In this case study five main criteria with 16 sub-criteria and seven potential suppliers are identified.
After the supplier selection criteria and potential suppliers are identified, the decision- makers compare the attributes related to the criterion.
Then, the pairwise comparison matrix is constructed, and the weight vector of each matrix is determined. All properties are compared against each individual criterion. The fuzzy pairwise comparison matrix between main criteria is calculated.
In the next step the fuzzy pairwise comparison matrix between the main criteria is converted into crisp numbers using the triangular fuzzy number method. After weights of the sub-criteria are determined by FANP, how to choose the best supplier WASPAS is developed.
The WASPAS method will be used to select the best supplier after receiving the comparison weights criteria from the FANP model results. The proposed model’s rationality and stability are verified using the concept of sensitivity analysis.
In this case, the resolving coefficient values (λ) are used to test the reliability of the proposed approach between λ = 0.1 and λ = 1.
According to the results, Supplier 3 (S3) is consistently the best alternative, and the remaining six suppliers are not optimal in any case. The alternatives are ranked as S3 > S2 > S7 > S1 > S4 > S5 > S6. Therefore, it is confirmed that the proposed model can be applied to real–world cases.
The research has successfully created a hybrid MCDM model using FANP and WASPAS to assist the supplier evaluation and selection process in the shrimp industry.
Selecting suppliers is an important decision-making problem that can boost business and increase profits in the shrimp industry. However, the supplier selection process tends to rely, mostly, on the decision-maker’s experience which creates inaccuracy and ambiguity.
The FANP-WASPAS model can support optimal decision-making because it considers problems based on many criteria and allows the decision-makers to check the correlation between criteria. It also considers the ambiguity, uncertainty, and subjectivity of many different decision makers.
“Therefore, the model in this study can support companies in the shrimp industry in making optimal decisions regarding supplier selection.”
Although the study is only applicable to the shrimp industry in Vietnam, the proposed model can be adapted and modified to support other industries in different countries as a resource in solving MCDM problems.
A potential application is the development of fuzzy MCDM models based on the proposed method to support the supplier selection processes for different Vietnamese exported aquatic products to the EU market, such as pangasius and tuna.
Future research can look into different methods to handle the uncertainty of supplier selection processes, such as the integration of D numbers into MCDM models and perform a comparative analysis of different models to identify the optimal support tool for the supplier selection problems of supply chains.
This is a summarized version developed by the editorial team of Aquaculture Magazine based on the review article titled “MULTI-CRITERIA DECISION-MAKING METHODS IN FUZZY DECISION PROBLEMS: A CASE STUDY IN THE FROZEN SHRIMP INDUSTRY” developed by: CHIA-NAN WANG, VAN THANH NGUYEN, JUI-CHUNG KAO, CHIH-CHENG CHEN, AND VIET TINH NGUYEN.
The original article was published on FEBRUARY 2021, through MOLECULAR DIVERSITY PRESERVATION INTERNATIONAL (MDPI) under the use of a creative commons open access license.
The full version can be accessed freely online through this link: https://doi.org/10.3390/sym13030370