It must be emphasized that the IDCR algorithm gives a ranking order of design characteristics using only its ability to manage ordinal information. It also avoids the risk of steering the design in an arbitrary way, depending on the conversion scale used to transform the R matrix symbols.
IDCR data are determined by asking customers to express their judgments without forcing them to reason with conventional unfamiliar scales.
A second issue that must be highlighted is that the IDCR algorithm can be easily automated. It can be insertable in generic commercial SW packages, or integrable with other QFD packages [Buede, 1992].
As regards the traditional approach, IDCR bases its operation on a procedure that is not too stiff and restrictive. For example, it allows managing veto situations.
FIGURE 8.2 Outranking graph for the technical design characteristics of a pencil.
FIGURE 8.3 Outranking graph contraction for the presence of a cycle in the Figure 8.2. SL3216-ch08-Frame Page 113 Monday, October 29, 2001 6:40 PM
114 Advanced Quality Function Deployment
Its apparent heaviness, due to the comparison of all pairs of alternatives, finds its justification in the nonsymmetrical influence, which can exercise the indifference relation on the decision maker’s final decision.
Finally, with reference to the computational aspects we can observe that: • The IDCR algorithm stops, in any case, after m iterations.
• IDCR computational complexity is, in the worst case, o(n5 + n2m), with
m and n the number of criteria and the number of alternatives, respectively. 8.6 CONCLUSIONS
The chapter presents a method for facilitating the prioritization of technical design characteristics of a product or service during the QFD planning process. It is appli- cable in those contexts where it is not easy to get information or knowledge from the customer.
Based on the interaction with customers (decision makers), the algorithm faces all situations in which the customers are not able to give a score to their requirements on conventional scales. Besides, the algorithm avoids an inappropriate conversion of qualitative information contained in the relationship matrix.
Although the IDCR method determines a spontaneous relationship with customers, it can present some applicability limits when they are not easily achievable (e.g., customers of wide consumption goods).
REFERENCES
Akao, Y. (1990), QFD: Integrating Customer Requirements into Product Design, Productivity Press, Cambridge, MA.
Buede, D.M. (1992), Software review: overview of the MCDA software market, J. Multi-
Criteria Decision Anal., 1(1), 59–61.
Cohen, L. (1995), Quality Function Deployment: How to Make QFD Work for You, Addison- Wesley, Reading MA.
Dyer, J.S. (1990), Remarks on the analytic hierarchy process, Manage. Sci., 36(3), 249–258. Ertas, A. and Jones, J.C. (1993), The Engineering Design Process, John Wiley & Sons, New
York.
Franceschini, F. (1998), Quality Function Deployment: uno strumento concettuale per coniugare qualità e innovazione, Ed. Il Sole 24 ORE Libri, Milano.
Franceschini, F. and Rossetto, S. (1995), QFD: the problem of comparing technical/engineering design requirements, Res. Eng. Design, 7, 270–278.
Franceschini, F. and Rossetto, S. (1997), Design for quality: selecting product’s technical features, Qual. Eng., 9(4), 681–688.
Franceschini, F. and Rossetto, S. (1998), QFD: how to improve its use, Total Qual. Manage., 9(6), 491–500.
Franceschini, F. and Rupil, A. (1999), Rating scales and prioritization in QFD, Int. J. Qual.
Reliability Manage., 16(1), 85–97.
Fraser, N.M. (1994), Ordinal preference representations, Theory Decision, 36(1), 45–67.
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Griffin, A. and Hauser, J. (1992), Patterns of communication among marketing, engineering and manufacturing — a comparison between two new product teams, Manage. Sci., 38(3), 360–373.
Hauser, J. and Clausing, D. (1988), The house of quality, Harv. Bus. Rev., 66(3), 63–73. Larichev, O.I., Moshkovich, H.M., Mechitov, A.J., and Olson, D.L. (1993), Experiments
comparing qualitative approaches to rank ordering of multiattribute alternatives,
J. Multi-Criteria Decision Anal., 2(1), 5–26.
Larichev, O.I., Olson, D.L., Moshkovich, H.M., and Mechitov, A.J. (1995), Numerical vs. cardinal measurements in multiattribute decision making: how exact is enough, Org.
Behav. Hum. Decision Processes, 64(1), 9–21.
Ostanello, A. (1985), Outranking methods, in Multiple Criteria Decision Methods and
Application, Fandel, G. and Spronk, J., Eds., Springer-Verlag, Berlin, pp. 41–60.
Pahl, G. and Beitz, W. (1996), Engineering Design, Springer-Verlag, Berlin.
Roy, B. (1991), The outranking approach and the foundations of ELECTRE methods, Theory
Decision, 31(1), 49–73.
Roy, B. (1996), Multicriteria Methodology for Decision Aiding, Kluwer Academic, Dordrecht. Saaty, T.L. (1990), Multicriteria Decision Making: The Analytic Hierarchy Process, 2nd ed.,
RWS Publications, Pittsburgh.
Sullivan, L. (1986), Quality function deployment, Qual. Prog., 19(6), 39–50.
Urban, G.L. and Hauser, J.R. (1993), Design and Marketing of New Products, Prentice Hall International, Englewood Cliffs, NJ.
Vansnick, J.C. (1986), On the problem of weights in multiple criteria decision making (the noncompensatory approach), Eur. J. Operational Res., 24, 288–294.
Vincke, P. (1992), Multicriteria Decision Aid, John Wiley & Sons, Chichester.
Wasserman, G.S. (1993), On how to prioritize design requirements during the QFD planning process, IIE Trans., 25(3), 59–65.
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How to Improve
the Use of Quality
Function Deployment
9.1 INTRODUCTIONIn the previous chapters, we have seen that quality function deployment (QFD) is certainly an innovative tool for the development of a new product and many enter- prises have decided to use it in such a way to improve their design cycle [Akao, 1990; ASI, 1987; Urban and Hauser, 1993]. Nevertheless, the obtained results have fallen short of expectations.
There are many causes of this only partial outcome [Cohen, 1995; Zairi and
Youssef, 1995]. Besides natural suspiciousness toward the use of new methodologies, the main highlighted problems are:
• Cultural barriers that thwart the creation of project teams able to use QFD • Lack of friendly tools able to reduce the training time
• Exponential growth of managerial difficulties connected with the increased size of design projects
Although the tool has been brilliantly employed in many applications [Sullivan, 1986; Akao, 1992; Griffin and Hauser, 1992; Franceschini and Rossetto, 1995a;
Glushkovsky et al., 1995], giving a definitive shape to the concept of customer-
orienteddesign [ASI, 1987; Hauser and Clausing, 1988; Franceschini and Rossetto, 1995b], in other situations it has given unsuitable responses.
By leaving organizational problems out of consideration, the real Achilles’ heel of QFD is the management of designs of large size, which involves both a high number of customer requirements and a high number of technical characteristics. A preliminary way to overcome this problem is the decomposition of a project into a set of subprojects. This solution, however, is only a palliative, because it does not solve the root problem of managing and analyzing large relationship matrices.
A solution could be that of introducing some tools able to automate some activities that are carried out manually today, and to simplify the analysis of information contained in the house of quality (HoQ). Tools of this kind are, for example, methods for clustering technical characteristics [Kihara, Hutchinson, and Dimancescu, 1994],
computerized methods, group decision support system (GDSS), quantitative engi-
neering analysis techniques [Maier, 1995], and methods for setting up the design with reference to the competition [Franceschini and Rossetto, 1998, see also Chapter 6].
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In addition to this summary list, two other methods are proposed in this chapter, the first directed toward the simplification of correlation matrix building and the second, to the determination of the minimum set covering of characteristics able to globally answer all customer requirements.