Application of Grey Relational Analysis to Decision-Making during Product Development
Shih-Wen Hsiao 1  
,  
Hsin-Hung Lin 1
,  
 
 
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1
Department of Industrial Design, National Cheng Kung University, Tainan City, Taiwan, ROC
2
Department of Creative Product Design, Asia University China Medical University, China Medical University Hospital, Department of Medical Research, Taichung , Taiwan
CORRESPONDING AUTHOR
Shih-Wen Hsiao   

Department of Industrial Design, National Cheng Kung University, Tainan City, Taiwan, ROC, No. 1 University Road, 70101 Tainan, Taiwan
Publish date: 2017-06-15
 
EURASIA J. Math., Sci Tech. Ed 2017;13(6):2581–2600
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ABSTRACT
A multi- attribute decision-making (MADM) approach was proposed in this study as a prediction method that differs from the conventional production and design methods for a product. When a client has different dimensional requirements, this approach can quickly provide a company with design decisions for each product. The production factors of a product are usually complex and difficult to predict. A focus group that comprises seven professional product designers was formed in this study to determine those exact assessment criteria based on their practical experiences of pneumatic door closer designs. These criteria include operability, manufacturability, style, creativity, and cost. We recommend using grey-level designs to assess and resolve the product design and production planning problems. A case study on pneumatic door closers was conducted and a weighted value was assigned to each of the assessment criteria. New product series were created for the verification of the proposed design approach. In a grey-level design assessment, the design ideas of clients and designers are represented by grey levels. After that, a grey relational analysis is used to determine those factors that are valued by clients for predicting the priority of each of the design elements for a product series. The proposed approach can assist designers in predicting a product’s design quality and recommend the optimal alternative within a product series.
eISSN:1305-8223
ISSN:1305-8215