4.1.3. Data deblurring process
In order to reflect the overall influence degree of each index on the
governance system and to clarify the specific values of the expert
scoring results, the integrated fuzzy values need to be defuzzified. The
defuzzification process takes the distribution pattern, shape and height
of the fuzzy numbers into consideration. According to Table 2, the
results of the above expert survey scoring table are transformed into
triangular fuzzy numbers, and the comprehensive fuzzy value of the
degree of influence is obtained based on equations (1)-(3). Where,
indicates the number of experts who participated in the questionnaire;
indicates the serial number of experts who provided data.
Since the CFCS (Converting Fuzzy data into Crisp Scores) method can
effectively distinguish two fuzzy numbers with the same exact value
(Lina, Guotao & Jue, 2021). Therefore, in this study, CFCS method is
used for defuzzification, and the combined fuzzy values are regarded as
the weight values of indexes for the governance system, as follows.
Step 1 . Fuzzy number normalization.
Step 2. The upper limit value is normalized to the lower limit
value.
Step 3. Calculate the weight value.
Among them, .
4.1.4. Direct relation matrix
According to the initial fuzzy weights of indexes for supply chain data
governance optimization, the DEMATEL model is introduced to optimize the
initial index weights, so as to weaken the subjective determination of
data results caused by expert scoring.
In view of the wide scope of supply chain data governance under the
industrial Internet, the complexity of the correlation between relevant
stakeholders, the interference of the overlapping influence of indexes
when experts score, and the difficulty of ensuring the independence of
indexes, the study optimizes the data sources based on the original
DEMATEL method, and points the experts’ scoring to a single evaluation
target, which circumvents the tedious operational problems caused by
experts due to the importance of multiple indexes for two-by-two
comparison. It also reduces the chance of misjudgment due to the lack of
independence among the elements. The direct relation matrix is set as to
characterize the influence of the indexon, and the data source is the
ratio of the initial fuzzy weights of each index, i.e..
Where,[1,24]. Therefore, the final direct relation matrix is
established (Table A1).
4.1.5. Integrated relationship matrix
The direct relation matrix is normalized according to Equation (10) to
obtain the normalization matrix .
Where,denotes the number of indexes. In order to represent the degree of
direct and indirect influence among the indexes of supply chain data
governance, Matlab software is further used to calculate the integrated
relationship matrix of based on Equation(11) (Table A2). Where is the
unit matrix.
4.1.6. Influence index ranking analysis
After determining the integrated relationship matrix , the influence
degree of each factor, the influenced degree , the centrality degree
(+), and the reason degree (-) are calculated according to
equations(12)-(13), where the influence degree indicates the
comprehensive influence degree of each index in the comprehensive
influence matrix on all other indexes; the influenced degree indicates
the comprehensive influence degree of each column of indexes inon other
indexes; the centrality degree indicates the importance degree of
indexes in the whole evaluation system; the reason degree is used to
portray the contribution degree of an index to the formation of the
evaluation system, that is, the degree of interrelationship between the
index and other indexes.
where is the element of the row and column of the integrated
relationship matrix.