A systematic approach to quality function deployment with a full ...

Table 7
Crisp and fuzzy initial technical ratings of the nine HOWs
L.-K. Chan, M.-L. Wu / Omega 33 (2005) 119 – 139 133
HOWs Initial technical ratings Scaled initial technical ratings
Crisp (tn) Fuzzy (˜tn) Crisp Fuzzy
H1 34.1866 [23.2850, 47.4773] 0.8683 [0.4309, 0.8785]
H2 33.9720 [23.0639, 47.2691] 0.8629 [0.4268, 0.8746]
H3 18.8076 [9.9597, 30.0446] 0.4777 [0.1843, 0.5559]
H4 17.9852 [9.1375, 29.2221] 0.4568 [0.1691, 0.5407]
H5 39.3707 [27.0869, 54.0437] 1.0000 [0.5012, 1.0000]
H6 23.5959 [13.7766, 35.8043] 0.5993 [0.2549, 0.6625]
H7 23.8702 [13.8342, 36.2954] 0.6063 [0.2560, 0.6716]
H8 31.3584 [20.3964, 44.7095] 0.7965 [0.3774, 0.8273]
H9 32.6375 [21.3973, 46.2669] 0.8290 [0.3959, 0.8561]
Table 8
Technical competitive analysis, goals and improvement ratios for HOWs
HOWs Measurement Technical comparison Technical competitive Goal Improvement
units matrix (Y =[ynl] 9×4 ) priority rating (zn) (bn) ratio (vn)
C1 C2 C3 C4
H1 ml 11 13 8 9 0.1115 9 1.2222
H2 g 6 9 9 10 0.1116 8 1.3333
H3 mg 20 25 15 30 0.1104 25 1.2500
H4 g 250 350 300 300 0.1124 300 1.2000
H5 ml 7 11 10 10 0.1119 9 1.2857
H6 h 8 10 5 6 0.1101 6 1.3333
H7 min 2.5 2 1.5 3 0.1104 2 1.2500
H8 min 3 4 3.5 3.5 0.1125 3.5 1.1667
H9 s 20 30 15 15 0.1093 15 1.3333
It is noticed from (19) and (20) that the crisp and fuzzy ratings exhibit the same ranking order. Both sets of ratings
indicate that H5 is of the highest initial importance, followed by H1;H2 and H9.
The crisp and fuzzy initial technical ratings of the nine HOWs are shown in Table 7. Also shown there are the scaled
crisp and fuzzy ratings that are easier to be compared. Same as in the case for the WHATs’ nal importance ratings
(Table 4), crisp initial technical ratings tend to be close to the upper bounds and far away from the lower bounds of the
corresponding fuzzy ratings, indicating more exibility and higher reliability represented by the fuzzy ratings.
Step 8: Now turn to technical competitive analysis which is to nd and establish competitive advantages or to further
enhance the existing advantages for restaurant C1, through comparing all the restaurants’ similar fried Chinese vegetables in
terms of their technical performance on the nine identi ed HOWs. Although it is always not easy to acquire the technical
performance levels of competitors’ products on the HOWs, restaurant C1 must try all the means to obtain this valuable
information in order to know its technical strengths and weaknesses and hence to improve or enhance its competitiveness.
Through a lot of e orts restaurant C1 obtains all the technical parameters of its own and its competitors’ fried Chinese
vegetables in terms of the nine HOWs. This information forms a technical comparison matrix Y =[ynl] 9×4 as shown in
Table 8 where, for example, amount of soy sauce the four restaurants use to make fried Chinese vegetables (H1) are 11,
13, 8 and 9 ml, respectively, which form the rst column of the technical comparison matrix Y.
Applying entropy method to Y in the same manner as in customer competitive analysis (Step 3), technical competitive
priority ratings can be obtained for restaurant C1’s fried Chinese vegetable on the nine HOWs:
z =(z1;z2;:::;z9)=(0:1115; 0:1116; 0:1104; 0:1124; 0:1119; 0:1101; 0:1104; 0:1125; 0:1093):
These ratings are shown in Table 8 from which we know that H8 and H4 are of the highest competitive priorities.
According to the technical performance of its own and the other three restaurants’ fried Chinese vegetables in terms of
the nine HOWs, restaurant C1 could set technical performance goal on each of the HOWs for its fried Chinese vegetable