Faithfully obtaining design specifications from customer requirements is essential for successful designs. The natural lingual, inexact, incomplete, and vague attributes of customer requirements make it very difficult to map customer requirements to design specifications. In general design process, the design specifications are determined by designers based on their experience and intuition, and often a certain target value is set for a specification. However, it is on one hand very difficult, on the other hand unreasonable, so a suitable limit range rather than a certain value is preferred at the beginning of design, especially at the concept design process. In this paper, a simplified systematic approach of transforming customer requirements to design specifications is proposed. First, a two-stepped clustering approach for grouping customer requirements and design specifications based on the house of quality matrix is presented, by which the mapping is limited to within each group. To further simplify the inference mapping rules of customer requirements and design specifications, the minimal condition inference mapping rules for each design specification are extracted based on rough set theory. In the end, a suitable value range is determined for a specification by applying the fuzzy rule matrix.
1.
Jiao
, J. X.
, and Zhang
, Y. Y.
, 2005, “Product Portfolio Identification Based on Association Rule Mining
,” Comput.-Aided Des.
0010-4485, 37
, pp. 149
–172
.2.
Christopher
, M. C.
, McDonald
, M.
, and Wills
, G.
, 1980, Introducing Marketing
, Pan
, London
.3.
Tarasewich
, P.
, and Nair
, S. K.
, 2001, “Designer-Moderated Product Design
,” IEEE Trans. Eng. Manage.
0018-9391, 48
(2
), pp. 175
–188
.4.
Tseng
, M. M.
, and Jiao
, J. X.
, 1997, “A Variant Approach to Product Definition by Recognizing Functional Requirement Patterns
,” Comput. Ind. Eng.
0360-8352, 33
(3–4
), pp. 629
–633
.5.
Karsak
, E. E.
, 2004, “Fuzzy Multiple Objective Programming Framework to Prioritize Design Requirements in Quality Function Deployment
,” Comput. Ind. Eng.
0360-8352, 47
(2–3
), pp. 149
–163
.6.
Clausing
, D.
, 1994, Total Quality Development: A Step-by-Step Guide to World-Class Concurrent Engineering
, ASME
, New York
.7.
Hauser
, J. R.
, and Clausing
, D.
, 1988, “The House of Quality
,” Harvard Bus. Rev.
0017-8012, 66
(3
), pp. 63
–73
.8.
Akao
, Y.
, 1990, Quality Function Deployment: Integrating Customer Requirements into Product Design
, Productivity
, Cambridge
.9.
Chan
, L. K.
, and Wu
, M. L.
, 2002, “Quality Function Deployment: A Literature Review
,” Eur. J. Oper. Res.
0377-2217, 143
, pp. 463
–497
.10.
Chan
, L. K.
, and Wu
, M. L.
, 2002, “Quality Function Deployment: A Comprehensive Review of Its Concepts and Methods
,” Qual. Eng.
0898-2112, 15
(1
), pp. 23
–35
.11.
Masud
, A. S. M.
, and Dean
, E. B.
, 1993, “Using Fuzzy Sets in Quality Function Deployment
,” Proceedings of the Second Industrial Engineering Research Conference
, Los Angeles, CA, May 26–27.12.
Khoo
, L. P.
, and Ho
, N. C.
, 1996, “Framework of A Fuzzy Quality Function Deployment System
,” Int. J. Prod. Res.
0020-7543, 34
(2
), pp. 299
–311
.13.
Kalargeros
, N.
, and Gao
, J. X.
, 1998, “QFD: Focusing on Its Simplification and Easy Computerization Using Fuzzy Logic Principles
,” Int. J. Veh. Des.
0143-3369, 19
(3
), pp. 315
–325
.14.
Chan
, L. K.
, Kao
, H. P.
, Ng
, A.
, and Wu
, M. L.
, 1999, “Rating the Importance of Customer Needs in Quality Function Deployment by Fuzzy and Entropy Methods
,” Int. J. Prod. Res.
0020-7543, 37
(11
), pp. 2499
–2518
.15.
Wang
, J.
, 1999, “Fuzzy Outranking Approach to Prioritize Design Requirements in Quality Function Deployment
,” Int. J. Prod. Res.
0020-7543, 37
(4
), pp. 899
–916
.16.
Kim
, K. J.
, Moskowitz
, H.
, Dhingra
, A.
, and Evans
, G.
, 2000, “Fuzzy Multicriteria Models for Quality Function Deployment
,” Eur. J. Oper. Res.
0377-2217, 121
(3
), pp. 504
–518
.17.
Sohn
, S. Y.
, and Choi
, I. S.
, 2001, “Fuzzy QFD for Supply Chain Management With Reliability Consideration
,” Reliab. Eng. Syst. Saf.
0951-8320, 72
(3
), pp. 327
–334
.18.
Vanegas
, L. V.
, and Labib
, A. W.
, 2001, “A Fuzzy Quality Function Deployment (FQFD) Model For Deriving Optimum Targets
,” Int. J. Prod. Res.
0020-7543, 39
(1
), pp. 99
–120
.19.
Dawson
, D.
, and Askin
, R. G.
, 1999, “Optimal New Product Design Using Quality Function Deployment With Empirical Value Functions
,” Qual. Reliab. Eng. Int
0748-8017, 15
(1
), pp. 17
–32
.20.
Moskowitz
, H.
, and Kim
, K. J.
, 1997, “QFD Optimizer: A Novice Friendly Quality Function Deployment Decision Support System for Optimizing Product Designs
,” Comput. Ind. Eng.
0360-8352, 32
(3
), pp. 641
–655
.21.
Cristiano
, J. J.
, White
, C. C.
, and Liker
, J. K.
, 2001, “Application of Multiattribute Decision Analysis to Quality Function Deployment for Target Setting
,” IEEE Trans. Syst. Man Cybern., Part C Appl. Rev.
1094-6977, 31
(3
), pp. 366
–382
.22.
Harding
, J. A.
, Popplewell
, K.
, and Omar
, A. R.
, 2001, “An Intelligent Information Framework Relating CRs and Product Characteristics
,” Comput. Ind.
, 44
(1
), pp. 51
–65
. 0166-361523.
Fung
, R. Y. K.
, Law
, D. S. T.
, and Ip
, W. H.
, 1999, “Design Targets Determination for Inter-Dependent Product Attributes in QFD Using Fuzzy Inference
,” Integr. Manuf. Syst.
0957-6061, 10
(6
), pp. 376
–384
.24.
Shin
, J. S.
, and Kim
, K. J.
, 2000, “Complexity Reduction of A Design Problem in QFD Using Decomposition
,” J. Intell. Manuf.
0956-5515, 11
(4
), pp. 339
–354
.25.
Pawlak
, Z.
, 1982, “Rough Sets
,” Int. J. Comput. Inf. Sci.
0091-7036, 11
(5
), pp. 341
–356
.26.
Pawlak
, Z.
, 1991, Rough Sets
, Kluwer Academic
, Dordrecht
.27.
Pawlak
, Z.
, and Skowron
, A.
, 2007, “Rudiments of Rough Sets
,” Inf. Sci.
, 177
, pp. 3
–27
. 0020-025528.
Ziarko
, W.
, 1994, “Rough Sets and Knowledge Discovery: An Overview
,” Rough Sets, Fuzzy Sets and Knowledge Discovery
, Proceedings of the International Workshop on Rough Sets and Knowledge Discovery (RSKD ‘93)
, Springer Verlag
, London
, pp. 11
–15
.29.
Yao
, Y. Y.
, Wong
, S. K. M.
, and Lin
, T. Y. A.
, 1997, “Review of Rough Sets Models
,” Rough Sets and Data Mining – Analysis for Imprecise Data
, Y. T.
Lin
and N.
Cercone
, eds., Kluwer
, Boston, MA
pp. 47
–67
.30.
Pawlak
, Z.
, 2004, “Some Issues on Rough Sets
,” Transactions on Rough Sets I
, J. F.
Peters
and A.
Skowron
, eds., Springer-Verlag
, Heidelberg, Berlin
, pp. 1
–58
.31.
Komorowski
, J.
, Pawlak
, Z.
, Polkowski
, L.
, and Skowron
, A.
, 1999, “Rough Sets: A Tutorial
,” Rough Fuzzy Hybridization
, S. K.
Pal
and A.
Skowron
, eds., Springer
, Singapore
, pp. 3
–98
.32.
Polkowski
, L.
, and Skowron
, A.
, 1998, Rough Sets in Knowledge Discovery 1: Methodology and Applications
, Physica-Verlag
, Heidelberg
.33.
Polkowski
, L.
, and Skowron
, A.
, 1998, Rough Sets in Knowledge Discovery 2: Applications
, Physica-Verlag
, Heidelberg
.34.
Polkowski
, L.
, Tsumoto
, S.
, and Lin
, T. Y.
, 2000, Rough Set Methods and Applications
, Physica-Verlag
, Heidelberg
.35.
Kryszkiewicz
, M.
, 1999, “Rules in Incomplete Information Systems
,” Inf. Sci.
, 113
(3–4
), pp. 271
–292
. 0020-025536.
Leung
, Y.
, Wu
, W. Z.
, and Zhang
, W. X.
, 2006, “Knowledge Acquisition in Incomplete Information Systems: A Rough Set Approach
,” Eur. J. Oper. Res.
0377-2217, 168
(1
), pp. 164
–180
.37.
Mi
, J. S.
, Wu
, W. Z.
, and Zhang
, W. X.
, 2004, “Approaches to Knowledge Reductions Based on Variable Precision Rough Sets Model
,” Inf. Sci.
, 159
(3–4
), pp. 255
–272
. 0020-025538.
Skowron
, A.
, and Rauszer
, C.
, 1992, The Discernibility Matrices and Functions in Information Systems, Intelligent Decision Support-Handbook of Applications and Advances of the Rough Sets Theory
, R.
Slowinski
, ed., Kluwer Academic
, London
, pp. 331
–362
.39.
Slezak
, D.
, and Ziarko
, W.
, 2005, “The Investigation of the Bayesian Rough Set Model
,” Int. J. Approx. Reason.
0888-613X, 40
(1–2
), pp. 81
–91
.40.
Slowinski
, R.
, and Vanderpooten
, D.
, 2000, “A Generalized Definition of Rough Approximations Based on Similarity
,” IEEE Trans. Knowl. Data Eng.
1041-4347, 12
, pp. 331
–336
.41.
Stefanowski
, J.
, 1998, “On Rough Set Based Approaches to Induction of Decision Rules
,” Rough Sets in Knowledge Discovery 1
, L.
Polkowski
and A.
Skowron
, eds., Physica-Verlag
, Heidelberg
, pp. 500
–529
.42.
Stefanowski
, J.
, and Vanderpooten
, D.
, 1994, “A General Two Stage Approach to Rule Induction for Examples
,” Rough Sets, Fuzzy Sets and Knowledge Discovery
, W.
Ziarko
, ed., Springer-Verlag
, Berlin
, pp. 317
–325
.43.
Wu
, W. Z.
, Zhang
, W. X.
, and Li
, H. Z.
, 2003, “Knowledge Acquisition in Incomplete Fuzzy Information Systems Via Rough Set Approach
,” Expert Syst.
, 20
(5
), pp. 280
–286
. 0266-472044.
Zhang
, W. X.
, Mi
, J. S.
, and Wu
, W. Z.
, 2003, “Approaches to Knowledge Reductions in Inconsistent Systems
,” Int. J. Intell. Syst.
0884-8173, 18
, pp. 989
–1000
.45.
46.
Fung
, R. Y. K.
, Popplewell
, K.
, and Xie
, J.
, 1998, “An Intelligent Hybrid System for Customer Requirements Analysis and Product Attribute Targets Determination
,” Int. J. Prod. Res.
0020-7543, 36
(1
), pp. 13
–34
.47.
Thurston
, D.
, and Locascio
, A.
, 1994, “Quantifying the House of Quality for Optimal Product Design
,” Proceedings of the ASME Design Theory and Methodology Conference
, Vol. DE-68
, pp. 43
–54
.Copyright © 2009
by American Society of Mechanical Engineers
You do not currently have access to this content.
