Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/39260
Title: Use of an analytic network process and monte carlo analysis in new product formula selection decisions
Authors: Wudhikarn,R.
Chakpitak,N.
Neubert,G.
Keywords: Management Science and Operations Research
Issue Date: 1-Jan-2015
Publisher: World Scientific Publishing Co. Pte Ltd
Abstract: © 2015 World Scientific Publishing Co. and Operational Research Society of Singapore. One crucial matter in routine life is decision-making. Although decision-making in everyday life appears to be easily dealt with by one-on-one comparisons, the same is not true with decision-making in business. In business management decisions, problems are more complicated than those in individual life because most business situations fall into the category of multi-criteria decision-making (MCDM) problems. Therefore, appropriate multi-criteria decision methods should be carefully selected to solve these problems. This study uses the analytic network process (ANP), one of the widely available multi-criteria decision methods. However, this popular method generally ignores the uncertainty inherent in the input data. Therefore, this paper proposes an improved process that considers uncertainty by using Monte Carlo analysis with input values then applied to the ANP procedures. This proposed method is implemented by a real business that produces roof tiles; the primary goal of the study is to select among newly developed roof formulas by considering the uncertainty and interrelation among decision criteria and elements as well as alternatives. The outcomes of study accurately rank the new product formulas. Furthermore, the results of improved method differ the rankings produced by the original ANP. The observed dissimilarities mainly result from uncertainty consideration discussed in this study.
URI: http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84928485058&origin=inward
http://cmuir.cmu.ac.th/handle/6653943832/39260
ISSN: 02175959
Appears in Collections:CAMT: Journal Articles

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