Proceedings of the 8th International Conference on Intellectual ...

Ridha Derrouiche et al.
experience and turnover. The maximum holistic performance score is 68.0651 from cluster A (best
relationship) while <strong>the</strong> minimum <strong>of</strong> holistic performance score is 39.5327 from cluster B (worst
relationship). Primarily, <strong>the</strong> difference <strong>of</strong> top minor perspective values between cluster A (best
relationship) and cluster B (worst relationship) can provide and guide <strong>the</strong> domain managers. One <strong>of</strong>
main contribution <strong>of</strong> performance cluster model is to answer <strong>the</strong> question “Where we are in industry in
term <strong>of</strong> B2B-SCM performance”.
Figure 5: PMM and performance cluster model
If <strong>the</strong>y are member <strong>of</strong> cluster B (worst relationship), <strong>the</strong>y should improve some <strong>of</strong> minor perspective
performance such as engagement & commitment, confidence, power exerted, finance and <strong>the</strong> o<strong>the</strong>rs
in order to move <strong>the</strong>ir position to better position in <strong>the</strong> new cluster. This holistic performance
improvement tendency was proposed from <strong>the</strong> mean <strong>of</strong> each cluster analysis. In real practice, domain
managers should concern and take account into standard deviation <strong>of</strong> minor perspectives in each
cluster to pros and cons <strong>the</strong> expected results <strong>of</strong> mean analysis usage. To know and learn from <strong>the</strong>
repeated event and pattern which happen in <strong>the</strong>ir PMM model is <strong>the</strong> promising way to understand
why <strong>the</strong> hidden relationship among minor perspectives.
Fixed-width binning method was use as attribute type convertor from continuous (Variable) to discrete
value (Attribute). Five was assigned as bin number for this process. As a result, this stage obtain <strong>the</strong>
new discrete minor perspective factors, for example <strong>the</strong> holistic over performance was converted into
5 bins, Matrix plot among <strong>the</strong> holistic overall performance bin, cluster number, relationship type and
<strong>the</strong> top impact <strong>of</strong> minor perspective bin factors were conducted to investigate “why do <strong>the</strong>ir
performance perform like that?”. What is more, <strong>the</strong> domain mangers and experts in this B2B-SCM can
use <strong>the</strong> graphical matrix to find <strong>the</strong> approximated ways to move <strong>the</strong> performance position as <strong>the</strong>y
designed, for instance <strong>the</strong>ir present performance position are in cluster A with worst relationship; <strong>the</strong>y
can efficiently use this chart to understand how member in cluster A perform good performance in
term <strong>of</strong> B2B-SCM and sustain <strong>the</strong>ir best relationship by keeping <strong>the</strong> both level <strong>of</strong> engagement and IT
utilization at 5th bin. On <strong>the</strong> o<strong>the</strong>r hands, we can understand how member in cluster B perform bad
performance by using <strong>the</strong> matrix plot <strong>of</strong> worst relationship. We can notice that both level <strong>of</strong>
engagement and IT utilization <strong>of</strong> cluster B with worst relationship performed at 1st bin.
Finally, <strong>the</strong> sequencing association rules method was accomplished based on <strong>the</strong> two datasets. The
former was <strong>the</strong> best relationship and <strong>the</strong> later was <strong>the</strong> worst relationship data set to develop <strong>the</strong> B2B-
SCM performance improvement road map and recognize <strong>the</strong> top s<strong>of</strong>tness and glitches between
enterprise and its direct customers. In this research <strong>the</strong> value support and confidence was assigned
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