30 Material Criticality Assessment for Business Purposes …
463
Table 30.10 Importance
weights of six risk metrics
Risk metric
Importance weight
Abundance risk
Small (0.1, 0.2, 0.3, 0.5)
Conflict material risk
Big (0.5, 0.7, 0.8, 0.9)
Environmental country risk
Fair (0.3, 0.5, 0.5, 0.7)
Price volatility risk
Very big (0.8, 0.9, 1.0, 1.0)
Sourcing and geopolitical risk
Fair (0.3, 0.5, 0.5, 0.7)
Monopoly of supply risk
Fair (0.3, 0.5, 0.5, 0.7)
X =
⎡
⎢
⎣
x L ,1 , x L ,2 , . . . x L ,n
x M,1 , x M,2 , . . . x M,n
x R,1 , x R,2 , . . . x R,n
⎤
⎥
⎦ ∗
⎡
⎢
⎢
⎢
⎣
W L ,1 , W L M,1 , W RM,1 , W R,1
W L ,2 , W L M,2 , W RM,2 , W R,2
. . .
W L ,n , W L M,n , W RM,n , W R,n
⎤
⎥
⎥
⎥
⎦
=
⎡
⎢
⎢
⎢
⎣
x L ,1 ∗ W L ,1 + x M,1 ∗ W L M,1 + x M,1 ∗ W RM,1 + x R,1 ∗ W R,1
x L ,2 ∗ W L ,2 + x M,2 ∗ W L M,2 + x M,2 ∗ W RM,2 + x R,2 ∗ W R,2
. . .
x L ,n ∗ W L ,1 + x M,n ∗ W L M,n + x M,n ∗ W RM,n + x R,n ∗ W R,n
⎤
⎥
⎥
⎥
⎦
in which x indicatesrisklevel, w indicates importance weight,
and n is the number o f riskmetrics.
(30.3)
Step 4. Construct the aggregated membership function for each material and rank
the fuzzy numbers as critical level (Table 30.11 and Fig. 30.1).
Therefore, the ordering of the critical level is:
C Yttrium > C Tungsten > C Cobalt
30.4 Discussion
Addressing material criticality for business benefits requires a full understanding of
material’s supply risk and economic vulnerability through the supply chain. Both
Table 30.11 Aggregated MF parameters for three critical materials
Material
MF parameters
a L
a LM
a RM
a R
Cobalt
1.9
6.4
7.0
13.7
Tungsten
4.1
9.8
10.6
18.3
Yttrium
5.9
12.2
13.0
19.2
463
Table 30.10 Importance
weights of six risk metrics
Risk metric
Importance weight
Abundance risk
Small (0.1, 0.2, 0.3, 0.5)
Conflict material risk
Big (0.5, 0.7, 0.8, 0.9)
Environmental country risk
Fair (0.3, 0.5, 0.5, 0.7)
Price volatility risk
Very big (0.8, 0.9, 1.0, 1.0)
Sourcing and geopolitical risk
Fair (0.3, 0.5, 0.5, 0.7)
Monopoly of supply risk
Fair (0.3, 0.5, 0.5, 0.7)
X =
⎡
⎢
⎣
x L ,1 , x L ,2 , . . . x L ,n
x M,1 , x M,2 , . . . x M,n
x R,1 , x R,2 , . . . x R,n
⎤
⎥
⎦ ∗
⎡
⎢
⎢
⎢
⎣
W L ,1 , W L M,1 , W RM,1 , W R,1
W L ,2 , W L M,2 , W RM,2 , W R,2
. . .
W L ,n , W L M,n , W RM,n , W R,n
⎤
⎥
⎥
⎥
⎦
=
⎡
⎢
⎢
⎢
⎣
x L ,1 ∗ W L ,1 + x M,1 ∗ W L M,1 + x M,1 ∗ W RM,1 + x R,1 ∗ W R,1
x L ,2 ∗ W L ,2 + x M,2 ∗ W L M,2 + x M,2 ∗ W RM,2 + x R,2 ∗ W R,2
. . .
x L ,n ∗ W L ,1 + x M,n ∗ W L M,n + x M,n ∗ W RM,n + x R,n ∗ W R,n
⎤
⎥
⎥
⎥
⎦
in which x indicatesrisklevel, w indicates importance weight,
and n is the number o f riskmetrics.
(30.3)
Step 4. Construct the aggregated membership function for each material and rank
the fuzzy numbers as critical level (Table 30.11 and Fig. 30.1).
Therefore, the ordering of the critical level is:
C Yttrium > C Tungsten > C Cobalt
30.4 Discussion
Addressing material criticality for business benefits requires a full understanding of
material’s supply risk and economic vulnerability through the supply chain. Both
Table 30.11 Aggregated MF parameters for three critical materials
Material
MF parameters
a L
a LM
a RM
a R
Cobalt
1.9
6.4
7.0
13.7
Tungsten
4.1
9.8
10.6
18.3
Yttrium
5.9
12.2
13.0
19.2