Ordinal aggregation operators to support the engineering characteristic prioritization in QFD

Ordinal aggregation operators to support the engineering characteristic prioritization in QFD Quality function deployment (QFD) is a consolidated management tool for supporting the design of new products/services and the relevant production/supply processes, starting from the so-called voice of the customer (VoC). QFD includes several operative phases, ranging from the VoC collection to the definition of the technical features of production/supply processes. The first phase entails the construction of the so-called house of quality (HoQ), i.e., a planning matrix, which translates the customer requirements (CRs) into measurable engineering characteristics (ECs) of the product/service. One of the main goals of this phase is the definition of relationships between CRs and ECs, and the prioritization of these ECs, taking account of (i) their relationships with CRs and (ii) the importance of the related CRs. Given that data are collected from customers through questionnaires or interviews, both of these inputs are based on linguistic/ordinal scales. In the traditional approach, represented by the independent scoring method (ISM), ordinal data are arbitrarily enriched with cardinal properties. The current scientific literature encompasses a number of alternative approaches but, even for most of them, cardinal properties are mistakenly attributed to data collected on ordinal scales. This paper proposes a method based on a consolidated ME-MCDM (multi expert / multiple criteria decision making) technique, which is able to perform the EC prioritization without incurring in the aforementioned issue. This method is able to aggregate data evaluated on ordinal scales, overcoming controversial assumptions of data cardinality and avoiding any arbitrary and/or artificial “scalarization” of the data. On the other hand, its application is relatively simple and intuitional, compared to other proposed approaches alternative to the ISM, which often are conceptually complicated and difficult to implement. Furthermore, the proposed method can be effectively used when both CR importances and relationship matrix coefficients are rated on different ordinal scales and, being easily automatable, it can be effortlessly integrated into existing QFD software applications. In the paper, after a general description of the theoretical principle of the method, several application examples are presented and discussed. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The International Journal of Advanced Manufacturing Technology Springer Journals

Ordinal aggregation operators to support the engineering characteristic prioritization in QFD

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Publisher
Springer London
Copyright
Copyright © 2017 by Springer-Verlag London
Subject
Engineering; Industrial and Production Engineering; Media Management; Mechanical Engineering; Computer-Aided Engineering (CAD, CAE) and Design
ISSN
0268-3768
eISSN
1433-3015
D.O.I.
10.1007/s00170-017-0040-8
Publisher site
See Article on Publisher Site

Abstract

Quality function deployment (QFD) is a consolidated management tool for supporting the design of new products/services and the relevant production/supply processes, starting from the so-called voice of the customer (VoC). QFD includes several operative phases, ranging from the VoC collection to the definition of the technical features of production/supply processes. The first phase entails the construction of the so-called house of quality (HoQ), i.e., a planning matrix, which translates the customer requirements (CRs) into measurable engineering characteristics (ECs) of the product/service. One of the main goals of this phase is the definition of relationships between CRs and ECs, and the prioritization of these ECs, taking account of (i) their relationships with CRs and (ii) the importance of the related CRs. Given that data are collected from customers through questionnaires or interviews, both of these inputs are based on linguistic/ordinal scales. In the traditional approach, represented by the independent scoring method (ISM), ordinal data are arbitrarily enriched with cardinal properties. The current scientific literature encompasses a number of alternative approaches but, even for most of them, cardinal properties are mistakenly attributed to data collected on ordinal scales. This paper proposes a method based on a consolidated ME-MCDM (multi expert / multiple criteria decision making) technique, which is able to perform the EC prioritization without incurring in the aforementioned issue. This method is able to aggregate data evaluated on ordinal scales, overcoming controversial assumptions of data cardinality and avoiding any arbitrary and/or artificial “scalarization” of the data. On the other hand, its application is relatively simple and intuitional, compared to other proposed approaches alternative to the ISM, which often are conceptually complicated and difficult to implement. Furthermore, the proposed method can be effectively used when both CR importances and relationship matrix coefficients are rated on different ordinal scales and, being easily automatable, it can be effortlessly integrated into existing QFD software applications. In the paper, after a general description of the theoretical principle of the method, several application examples are presented and discussed.

Journal

The International Journal of Advanced Manufacturing TechnologySpringer Journals

Published: Feb 7, 2017

References

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