M. Galetto, F. Franceschini, D. Maisano, L. Mastrogiacomo
{"title":"Engineering characteristics prioritisation in QFD using ordinal scales: a robustness analysis","authors":"M. Galetto, F. Franceschini, D. Maisano, L. Mastrogiacomo","doi":"10.1504/EJIE.2018.090617","DOIUrl":null,"url":null,"abstract":"Quality function deployment (QFD) is a management tool used for the design of new products/services and the related production/supply processes. One of the goals of the method is to translate the customer requirements (CRs) into measurable engineering characteristics (ECs) of the new product/service and prioritise them, basing on their relationships with CRs and the related importances. To this purpose, the current scientific literature encompasses several alternative approaches (the most used is the independent scoring method – ISM), in most of which cardinal properties are arbitrarily attributed to data collected on ordinal scales. This paper describes and discusses a new approach based on ME-MCDM (multi expert/multiple criteria decision making) techniques, which do not require any debatable ordinal to cardinal conversion. The theoretical principles and the robustness of the method are presented and tested through some application examples related to a well-known case study reported in the scientific literature. [Received 5 May 2015; Revised 7 November 2017; Accepted 9 November 2017]","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":null,"pages":null},"PeriodicalIF":16.4000,"publicationDate":"2018-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1504/EJIE.2018.090617","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Accounts of Chemical Research","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1504/EJIE.2018.090617","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 5
Abstract
Quality function deployment (QFD) is a management tool used for the design of new products/services and the related production/supply processes. One of the goals of the method is to translate the customer requirements (CRs) into measurable engineering characteristics (ECs) of the new product/service and prioritise them, basing on their relationships with CRs and the related importances. To this purpose, the current scientific literature encompasses several alternative approaches (the most used is the independent scoring method – ISM), in most of which cardinal properties are arbitrarily attributed to data collected on ordinal scales. This paper describes and discusses a new approach based on ME-MCDM (multi expert/multiple criteria decision making) techniques, which do not require any debatable ordinal to cardinal conversion. The theoretical principles and the robustness of the method are presented and tested through some application examples related to a well-known case study reported in the scientific literature. [Received 5 May 2015; Revised 7 November 2017; Accepted 9 November 2017]
期刊介绍:
Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance.
Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.