The Application of a Decision - making Approach based on Fuzzy ANP and TOPSIS for Selecting a Strategic Supplier
Author(s) -
Rajesri Govindaraju,
Muhammad Ikhsan Akbar,
Leksananto Gondodiwiryo,
Tota Simatupang
Publication year - 2015
Publication title -
journal of engineering and technological sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.202
H-Index - 14
eISSN - 2338-5502
pISSN - 2337-5779
DOI - 10.5614/j.eng.technol.sci.2015.47.4.5
Subject(s) - topsis , multiple criteria decision analysis , fuzzy logic , operations research , context (archaeology) , rank (graph theory) , selection (genetic algorithm) , computer science , group decision making , business , operations management , engineering , mathematics , artificial intelligence , paleontology , combinatorics , law , political science , biology
Supplier selection becomes very important when used in the context of strategic partnerships because of the long-term orientation of the relationship. This paper describes the application of a decision-making approach for selecting a strategic partner (supplier). The approach starts with defining a set of criteria that fits the company’s condition. In the next steps, a combination of fuzzy-ANP and TOPSIS methods is used to determine the weight for each criterion and rank all the alternatives. The application of the approach in an Indonesian manufacturing company showed that the three factors that got the highest weight were “geographical location”, “current operating performance”, and “reliability”. Geographical location got the highest weight because it affects many other factors such as reaction to changes in demand, after-sales service, and delivery lead-time. Application of the approach helps decision-makers to gain effectiveness and efficiency in the decision-making process because it facilitates them to express their group’s collective preferences while also providing opportunities for members to express their individual preferences. Future research can be directed at combining qualitative and quantitative criteria to develop the best criteria and methods for the selection of the best suppliers based on fuzzy ANP and TOPSIS
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