Information Technology Reference
In-Depth Information
In this article, we will Take a simple retailers Industry supply chain as example.
We will employ all the knowledge above for case study and test the feasibility of the-
ory or method.
3.1 The Establishment of Index System
The assessment of retail supply chain finance Operational risk is a multi-level and
multi-factor assessment, so we must build an evaluation index system to make a large
number of interrelated and mutually restraining factors get
layered
and well organized.
As supply chain finance Operational risk is interdependent and mutual influenced, the
establishment of index system should be related to other supply chain risk in order to
be more accurate. Therefore, the evaluation index of supply chain finance Operational
risk includes 6 terms of main indicators and 21 secondary indicators.
The first-lever factor set can set
and it can be divided
into four subsets which are classified by different secondly-attributions.
U
=
(
U
,
U
,
U
U
,
U
,
U
)
1
2
3
4
5
6
{
}
,
U
=
u
,
u
,
u
1
12
12
13
{
}
{
}
{
}
{
}
{
}
,
,
,
,
.
U
=
u
,
u
,
u
U
=
u
,
u
,
u
,
u
,
u
U
=
u
,
u
,
u
U
=
u
,
u
,
u
U
=
u
,
u
,
u
,
u
2
21
22
23
3
31
32
33
34
35
4
41
42
43
5
51
52
53
6
61
62
63
64
{
}
{
}
The evaluation set can set
.
V
=
V
,
V
,
V
,
U
=
A
,
B
,
C
,
D
1
2
3
4
3.2 Setting Up Weight Matrix by AHP
1) Set up judgment matrix of paired-comparisons
In order to carry out paired
-
comparisons on different indexes by using the 1-9 scale
method that is proposed by professor T.L. Satty, Then construct judgment matrix
n
)(
, in which is the total number of the evaluation indexes. We obtain the matrix
of paired-comparisons above by the questionnaire survey of the supply chain finance
Operational risk of retail industry.
A
=
a
ij
n
×
1
1
4
1
4
2
1
1
2
⎡
⎤
⎢
⎥
4
1
1
2
5
3
3
⎢
⎥
⎢
⎥
4
2
1
5
3
3
K
=
1
1
3
2
⎢
⎢
⎥
⎥
1
2
1
⎡
⎤
⎡
⎤
1
2
1
5
1
5
1
1
2
1
2
⎢
⎥
⎢
⎥
K
=
3
1
4
K
=
1
2
1
1
2
⎢
⎥
1
1
3
1
3
2
1
1
2
⎢
⎢
⎥
⎥
⎢
⎥
2
⎢
⎢
⎥
⎥
⎢
1
2
1
⎥
1
2
1
4
1
2
1
3
1
3
2
2
1
⎣
⎦
⎣
⎦
⎣
⎦
1
2
3
5
6
⎡
⎤
⎢
⎥
1
2
1
1
4
5
⎢
⎥
⎡
1
1
2
1
3
⎤
K
=
⎢
1
3
1
1
4
5
⎥
3
⎢
⎥
⎢
⎥
K
=
2
1
1
2
1
5
1
4
1
4
1
2
⎢
⎢
⎥
⎥
⎢
⎥
4
3
2
1
⎢
⎥
1
6
1
5
1
5
1
2
1
⎣
⎦
⎣
⎦
⎡
1
1
3
1
2
⎤
⎢
⎥
1
1
2
1
⎡
⎤
1
1
3
1
2
⎢
⎥
K
=
⎢
⎥
K
=
2
1
2
6
⎢
⎥
1
3
1
3
1
1
4
⎢
⎥
5
⎢
⎥
⎢
⎥
1
1
2
1
2
2
4
1
⎣
⎦
⎣
⎦
2) Calculate the largest characteristic root and eigenvector
On the basis of judgment matrix
A
, we can identify the largest characteristic
root
λ
=6.007 and its corresponding eigenvector
W
=
(
0
.
085
,
0
.
281
,
0
.
354
,
max
Therefore, we get the weight sorting of the relative importance on
the indexes of the same level relative to the indexes of level above.
T
0
.
055
,
0
.
093
,
.
132
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