to define the prioritization of technical/engineering design characteristic for a new product. Product designers need to know how to make tradeoffs in selecting ...
Rating scales and prioritization in QFD
Rating scales and prioritization in QFD
Fiorenzo Franceschini and Alessandro Rupil Politecnico di Torino, Torino, Italy
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Keywords Design, Quality functions deployment, Rating, Vehicles Abstract Presents some remarks about the use of rating scales in quality function deployment (QFD). Data collection and their correct interpretation are fundamental for the proper application of this tool as a support for designing activities. Particular attention is given to the critical aspects and consequences resulting from an incorrect use of rating scales. The paper illustrates how the priority rank of design characteristics can change depending on the type of scale used. Practical effects of these issues are finally shown on a real case concerning the design of a climatic control system for commercial vehicles.
1. Introduction The paper analyses the use of rating scales in quality function deployment (QFD). In more detail, it exhibits some potential problems that can arise during the elaboration and interpretation of collected data. QFD is a tool which is able to ensure that the voice of the customer is deployed throughout the product planning and design stages (Balthazard and Gargeya, 1995; Franceschini and Rossetto, 1997; Hauser and Clausing, 1988; Zairi and Youssef, 1995). It provides a list of activities and a graphic representation of the design deployment that allows one to see the relationships between goals (“whats”) and means to realize them (“hows”). At present QFD is utilized in the following ways: •
by customers and design teamworks to gather product information in a structured way;
•
to analyse customer expectations and the characteristics of competitive products;
•
to define the prioritization of technical/engineering design characteristic for a new product.
Product designers need to know how to make tradeoffs in selecting design characteristics, which result in the highest level of customer satisfaction. The larger the contribution, the more influence the design characteristic has on customer satisfaction. QFD uses four “houses” (Figure 1) (Cohen, 1995) to integrate the information needs of marketing, engineering, R&D, manufacturing and management. It is well-known by its first house, the house of quality, described in Figure 1.
International Journal of Quality & Reliability Management, Vol. 16 No. 1, 1999, pp. 85-97, © MCB University Press, 0265-671X
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The house of quality allows the collection and organization of information about the offered, perceived and expected quality of a product (Akao, 1990; Franceschini, 1998; Franceschini and Rossetto, 1995a and 1995b). According to the traditional elaboration scheme, named independent scoring method (Akao, 1990; Cohen, 1995), the information collected by QFD must be available in a numerical form. Therefore the degrees of importance of customer requirements, their satisfaction levels, and the relationships with design characteristics need to be “quantified”. However, degrees of importance of customer requirements, their satisfaction levels and requirements-design characteristics relationships are not measured according to the classical paradigm[1] as design characteristics. For these variables ratings are exclusively given on conventional scales. As a further step collected information is managed and interpreted as a numerical value. With reference to “legitimacy” and correctness of this interpretation see also Franceschini and Rossetto (1995a, 1995c, 1998); and Fraser (1994). The numerical scaling of information gives rise to two basic problems. The first concerns the introduction of an arbitrary metric to codify gathered data. The second is related to the assumption that each scale can be interpreted in the same way by all evaluators (Franceschini and Rossetto, 1998). The scaling operation could produce, therefore, a total or partial “distortion” of the collected information. The core of the problem is that, usually, the size of these distortions is not known. The consequences of this conversion can be easily understood. A conceptual pattern scheme of the transformations sustained by information provided by customers in the QFD process is shown in Figure 2. Customers release their information by means of verbal judgements (variable rating). This information is then arbitrarily converted into numerical values (scaling). The critical points can be summarized in the following questions: • Does the information managed by QFD really represent what customers would like to express? • Do ratings obtained by using the QFD process have enough “properties” to be treated with the “traditional scheme”?
design characteristics
customer reqmnts
Figure 1. The four houses of QFD
I
house of quality
part characteristics
design charact.
II
parts deployment
process and quality control parameters
critical steps of the process
parts charact.
III
process planning
critical steps of the process
IV
process and quality control
In the rest of the paper we will investigate these issues and their practical Rating scales and consequences. In more detail, the paper shows how the priority rank of design prioritization in characteristics can change depending on the type of scale used. QFD An example concerning the design of a climatic control system for commercial vehicles is then analysed. 2. Proportional and linear interval scales In marketing research the problem of rating is widely treated. A lot of methods and a set of rating scales have been developed to make the gathering and displaying of information easier (Urban and Hauser, 1993). Customer answers depend on scale properties and their interpretation. So, it is possible to minimize the judgement distortions and obtain information which is closer to reality, choosing the most suitable rating scale. These ratings are generally expressed in a verbal form, by choosing an answer among a finite set of possibilities (itemized scale) (Rupil, 1996). The assignment of numbers to ratings (scaling) must be done in such a way that some particular properties are preserved. Let us consider, for instance, a variable defined on three levels: “low”, “medium” and “high”. Let us define as m (low), m (medium), m (high) the corresponding numbers assigned to each level: • If “low”, “medium” and “high” are three different judgements, it is sufficient to define three numbers such that: m(low) ≠ m(medium) ≠ m(high) The scale with this property is named nominal. • If the three judgements are ordered in such a way that “high” is greater than “medium”, and “medium” is greater than “low”, the numbers must satisfy the following property: m(low) w55 being w40 = 153.78 and w55 = 113.28. Condition 1b The relationship between the two DCs priorities remains the same as the previous condition: w40 > w55 ; where now w40 = 87.09 and w55 = 76.98. It can be noticed that, although the relation w40 > w55 is still true, the mutual distance between the two absolute weights has significantly changed. Condition 2 Before proceeding to the determination of the priorities of the two DCs, we must determine the value of X which generate the rank reversal. Let us identify the two column vectors DC40 and DC55, with r40 and r55:
Considering a positive value of X, being Σ (ri55 – ri40) > 0, there exists at least a i value X such that the rank of the two DCs reverses. Being Σ(ri55 – ri40) = 2, the condition for X is: i
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Now we proceed with the new scaling for the ratings assigned. Let us suppose we have a numerical conversion of the itemized scale with a series in which X=10 units (X > 5.05). Figure 4 illustrates this new scale. The absolute and relative importances pai’ and di’ obtained with this new scale are the following: paT , = [15.7, 15.7, 15.6, 13.9, 15.6, 16.0, 14.9, 15.5, 15.8, 15.9, 15.4, 15.4, 14.3, 14.6, 15.1, 15.2, 13.5, 12.9, 15.9, 15.3, 15.4, 14.5, 16.3, 15.6, 16.5, 15.8]; (4e) T d , = [3.9, 4.0, 3.9, 3.5, 3.9, 4.0, 3.8, 3.9, 4.0, 4.0, 3.9, 3.9, 3.6, 3.7, 3.8, 3.8, 3.4, 3.3, 4.0, 3.9, 3.9, 3.6, 4.1, 3.9, 4.2, 4.0]. (4f) The new priorities for the two DCs are: (4g) w40= 82.94 and w55= 84.52 We may notice that the rank of the two DCs is reversed: w40 < w55 (4h) Condition 3 Using the numerical series 1, 3, 9 for the DCj–CRi relationships’ scaling, and the 11-17 series for the scaling of CRi importances, we obtain the following new priorities: w40= 146.25 and w55= 127.28 from which results again: w40 > w55
Figure 4. The modified scale used for the ratings of the importance of customer requirements with a translation of X = 10 units
(4i)
11
None
12
Very Low
13
Low
14
Medium
15
High
16
Very High
17
Perfect
Concluding, we observe that the translation of the origin of the scale for Rating scales and DCj – CRi relationships, using any terms of equispaced numbers (for instance: 1, prioritization in 3, 5), does not cause any variation of DCs priorities. QFD (4j)
95 (4k) In this case the comparison between priorities does not depend on the X value. 6. Conclusions Scaling of ratings in QFD is a very critical operation. The paper shows how ratings derived from a linear interval scale can lead to a wrong priority rank of design characteristics, if they are interpreted as deriving from a proportional scale. Results obtained applying conditions 1a and 2 in the example of a design of a new climatic control system show how DC priorities can change up to reverse their ranks. Conditions 2 and 3 highlight furthermore that also DC j – CRi relationships scaling can influence DCs ranking. On the basis of these considerations, for future research, we will be looking to improve methods able to make the determination of importance ratings easier. The constant-sum measurement technique, for example, might limit the problem of rating CRs importances. We are also thinking about introducing more formal approaches based on Multiple Criteria Decision Aiding (MCDA) methods, which seem to be useful for capturing the real contents of the design process, without providing an unforced evaluation of QFD data (Franceschini and Rossetto, 1995c). Note 1. For these variables you cannot use defined measurement units and metrological traceability chains as for physical quantities. References Akao, Y. (1990), QFD: Integrating Customer Requirements into Product Design, Productivity Press, Cambridge. Anderberg, M.R. (1973), Cluster Analysis for Applications, Academic Press, New York, NY. Balthazard, P.A. and Gargeya, V.B. (1995), “Reinforcing QFD with group support systems: computer-supported collaboration for quality in design”, International Journal of Quality & Reliability Management, Vol. 12 No. 6, pp. 43-62.
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Cohen, L. (1995), Quality Function Deployment: How to Make QFD Work for You, AddisonWesley, Reading, MA. Edwards, A.L. (1957), Techniques of Attitude Scale Construction, Appleton-Century-Crofts Inc., New York, NY. Franceschini, F. (1998), Quality Function Deployment: Uno Strumento Concettuale per Coniugare Qualità e Innovazione, Ed. Il Sole 24 ORE Libri, Milano.
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Franceschini, F. and Rossetto, S. (1995a), “Qualità, QFD e Cliente: la scelta degli attributi del prodotto”, Automazione & Strumentazione, Vol. XLIII No. 10, pp. 55-61. Franceschini F. and Rossetto S. (1995b), “Quality and innovation: a conceptual model of their interaction”, Total Quality Management, Vol. 6 No. 3, pp. 221-9. Franceschini, F. and Rossetto S. (1995c), “QFD: the problem of comparing technical-engineering design requirements”, Research in Engineering Design, Vol. 7, pp. 270-8. Franceschini, F. and Rossetto, S. (1997), “Design for quality: selecting products’ technical features”, Quality Engineering, Vol. 9 No. 4, pp. 681-8. Franceschini, F. and Rossetto, S. (1998), “On-line service quality control: the ‘qualitometro’ method”, Quality Engineering, Vol. 10 No. 4, pp. 633-43. Fraser, N.M. (1994), “Ordinal preference representations”, Theory and Decision, Vol. 36 No. 1, pp. 45-67. Hauser, J. and Clausing, D. (1988), “The House of Quality”, Harvard Business Review, Vol. 66 No. 3, pp. 63-73. Rupil, A. (1996), Metodi e tecniche per la misurazione della qualità degli attributi di un prodotto: un’applicazione nel settore dei veicoli industriali, Thesis Degree, Politecnico di Torino. Urban, G.L. and Hauser J.R. (1993), Design and Marketing of New Products, Prentice-Hall International, NJ. Wasserman, G.S. (1993), “On how to prioritize design requirements during the QFD planning process”, IIE Transaction, Vol. 25 No. 3, pp. 59-65. Zairi, M. and Youssef, M.A. (1995), “Quality function deployment: a main pillar for successful total quality management and product development”, International Journal of Quality & Reliability Management, Vol. 12 No. 6, pp. 9-23.
Rating scales and prioritization in QFD
Level 1
Easy to use
Health
Comfort (safety)
Safety Low utiliz. costs
Level 2
Level 3
air diffuser
1 2
good
comprehensible controls
a
manoeuvrability
easy to use controls
b +A +A
40 41 42 43
c +A +A
need for
external temperature signalling
d
information
defunct light
e
defence against
air blocking of...
f
pollution
automatic recircle
g
need to
purify air in recircle
h
breathe
smells elimination
i
clean air
pollen and dust elimination
j
good
homogeneous air diffusion
k
+A +A +B
air
air jets to individuals
l
+C
diffusion
flow differentiated
m
bilevel*
n
+B
good
input of the favourite temperature
o
+A +C
climatic
maintain input temp. in all cond.
p
+C +B
conditions
different temp. for each traveller
q
warmed chairs
r
high
instantaneous defrost
s
+C +C
performances
ideal temp. in little time
t
+C +C
noiseless
u
start the climatic plant when...
v
internal misting
w
solutions for power losses
x
reliability
y
easy maintenance
z
feet-defrost flow rate
97
53 54 55 56 57
comfortable controls
visibility
defrost flow-rate
openings regulation flow-rate
openings regulation
openings position
effec. of installation
care of effect. of stratification stratif.
load reduction
heating power
heating quickness
knob diameter
heater
Level 1 Level 2
manoeuvrability
Level 3
CUSTOMER REQTS
user interface
shape of knobs
DESIGN CHARACTERISTICS
Appendix
+A
+C +B
+C +B +B +B +A +A +A
+B +B
+C +C
+B +B +A +C +B
+C +C +A +A
Table AI. “House of Quality” for the deployment of a design of a new climatic control system for commercial vehicles
