Supplier Quality Evaluation Using a Fuzzy Multi Criteria Decision Making Approach - Complex System Modelling and Control Through Intelligent Soft Computations - page 211

Fig. 1 CCi scores for the
three suppliers

Expt1

Expt2

Expt23

0.6

Expt22 Expt3

Expt4

0.5

Expt21

0.4

0.3

Expt20 Expt5

0.2

Expt19 Expt6

0.1

0

Expt18 Expt7

Expt17 Expt8

Expt16 Expt9

Expt15 Expt10

Expt11
Expt12

Expt14

Expt13

A1 A2 A3

Table 13 Crisp weight values
Criteria D1 D2 D3 Aggregate score Crisp score

C1 H M VH (3, 7, 9) 6.667

C2 VL VH L (1, 4.333, 9) 4.556

C3 L L VH (1, 5, 9) 5

C4 VL H VH (1, 5.667, 9) 5.444

C5 VL M H (1, 4.333, 9) 4.556

C6 M VH M (3, 6.333, 9) 6.222

C7 H H H (5, 7, 9) 7

C8 M M H (3, 5.667, 9) 5.778

C9 VH H H (5, 7.667, 9) 7.444

C10 M H H (3, 6.333, 9) 6.222

C11 VH H H (5, 7.667, 9) 7.444

C12 VH H H (5, 7.667, 9) 7.444

C13 M VH M (3, 6.333, 9) 6.222

C14 H H H (5, 7, 9) 7

C15 L H VL (1, 3.667, 9) 4.111

C16 M VL VH (1, 5, 9) 5

C17 H M L (1, 5, 9) 5

three alternatives calculated using Eq. ( 17 ) can be seen in the last row of Table 15 .
It can be seen that alternative A3 scores highest followed by A1 and A2. These
results are in agreement with Fuzzy TOPSIS results and hence validate the pro-
posed model for supplier quality evaluation.