A decision support system for supplier selection based on a strategy-aligned fuzzy SMART approach

Chou, Shuo‐Yan; Chang, Yuen‐Haw

doi:10.1016/j.eswa.2007.03.001

Cited by 293 publications

(157 citation statements)

References 45 publications

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“…Various conceptual or empirical methodologies regarding this topic have been developed [20][21][22]. The complexity of the supplier selection problem and the uncertain qualitative factors that are involved in this problem led to the development of techniques that cope with fuzziness by applying fuzzy logic methods [23][24][25][26]. These methods can handle and manipulate fuzziness in a manner that leads to meaningful results.…”

Section: "Buy National" Policies and Lack Of Monitoring Public Procurmentioning

confidence: 99%

Monitoring Public Procurement Using a Fuzzy Logic System

Mardas

Tsadiras²

2015

J Stock Forex Trad

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The problem of monitoring public procurement is a vital issue, especially in the European Union (EU), where the free movement of goods is finally established by the "Single European Act". A monitoring system that could be applied after public procurement contracts have been awarded, would examine and identify the existence both, of corruption and of "buy national" policies and practices (i.e. policies favouring domestic suppliers). In this article, the use of a fuzzy logic system as a monitoring mechanism for public procurement is proposed. Using this system, national or EU surveillance authorities can sort the contractors in a series of categories, identifying the degree of their efficiency. The design and implementation of the proposed fuzzy logic system is presented, followed by demonstrative examples of its operation. This system, which applies mainly to goods and services, can also be used as a decision support system at the stage of the selection process, assisting the choice of the best contractor.

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Section: "Buy National" Policies and Lack Of Monitoring Public Procurmentioning

confidence: 99%

Monitoring Public Procurement Using a Fuzzy Logic System

Mardas

Tsadiras²

2015

J Stock Forex Trad

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“…According to Chou & Chang (2008) studies, a nine-scale linguistic variable fuzzy number is implemented. This linguistic scales and corresponding triangular fuzzy numbers for the rating of alternatives show in Table 1.…”

Section: The Proposed Studymentioning

confidence: 99%

“…Chang and Hung (2010) presented a study of using rough set theory to create the supplier selection model and decision-making rules. Chou and Chang (2008) presented a strategy-aligned fuzzy simple multi-attribute rating technique (SMART) method for handling the supplier/vendor selection problem from the perspective of strategic management of the supply chain (SC). The majority of supplier rating systems reach their optimal solutions without looking at firm's operations management (OM)/SC strategy.…”

Section: Introductionmentioning

confidence: 99%

Ranking green electronic supplier in auto industry using fuzzy Delphi

Saeidi¹,

Ghasemi²,

Fakhari³

et al. 2014

10.5267/j.msl

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This paper presents a study to rank different green supplier involved in supplement of electronic parts in auto industry using fuzzy Delphi. The proposed study uses the fuzzy analytic hierarchy process for weighing criteria and VIKOR method to rank the suppliers. The results show that measures of corporate social responsibility, environmental management system, green procurement and green production are the most important criteria for green supplier selection. In addition, product quality, online tracking of orders, delivery time and quality of online information are the most important criteria for choosing a green electronic supplier.

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“…The current studies of food supply chain mainly stay in from the Angle of the qualitative to analysis the risk factors, and puts forward some countermeasures to prevent or solve the risk. Such as Xiaolin Chen, Junwen Feng [3][4] shows that the food supply chain and the general industry supply chain is different, they think the food *Address correspondence to these authors at the Huanghe Science and Technology College, China; Tel: +358-6-3247476; Fax: +358-6-3247457; E-mail: hunter2011@foxmail.com supply chain risk mainly includes the technology risk, information risk, coordination, quality safety risk. Liang Xing and Yanhong Zeng analyze Shenzhen has taken some measures, in order to strengthen the management of production source, to ensure the quality and safety of agricultural products (food), mainly from the following five aspects: accelerate the process of agricultural standardization; implementation the whole process supervision of inputs, establish a product traceability system, and establish agricultural production operator self-discipline mechanism, establish a sound system of agricultural product quality safety risk assessment (food).…”

Section: Introductionmentioning

confidence: 99%

Sports Food Safety Supervision Based on AHP and Interval Fuzzy TOPSIS

Sun¹

2015

TOCSJ

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Abstract:In recent years, China's food safety problems emerge in endlessly, from the "Sanlu milk powder contains melamine", "Shuanghui contains clenbuterol", to Wal-Mart false green pork, the occurrence of these events, makes the "safe" on the tip of the tongue is more and more attention. Sports food safety supervision system is established in accordance with the Law of Food Safety in China. This is a joint system of government supervision and food trade association selfregulation. Sports food supply chain's each link is closely related to the safety of the athletes, strengthen the risk management in the process of food supply chain operation is to avoid the urgent requirements of food safety and quality accident. The implementation of accurate, comprehensive and rapid analysis of supply chain risk assessment is a key link in the process of risk management. In the paper, a methodology based on AHP (Analytic Hierarchy Process) and interval fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) is used for analyzing the safety supervision of sports foods. The proposed approach determines different suppliers and uses various criteria to rank different alternatives. The preliminary results indicate that the proposed model of this paper is capable of determining appropriate suppliers in sports food industry.

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A decision support system for supplier selection based on a strategy-aligned fuzzy SMART approach

Cited by 293 publications

References 45 publications

Monitoring Public Procurement Using a Fuzzy Logic System

Monitoring Public Procurement Using a Fuzzy Logic System

Ranking green electronic supplier in auto industry using fuzzy Delphi

Sports Food Safety Supervision Based on AHP and Interval Fuzzy TOPSIS

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