4.1 Discrepancy Analysis
In a case evolving framework, discrepancy measures the between-case variabil-ity of the case lifecycle trajectories. Therefore, higher discrepancy, for example, would reflect a greater level of uncertainty about the path followed by the cases.
The discrepancy of sequences will be defined from their pairwise dissimilarities.
Perhaps the most popular dissimilarity measure used for sequence analysis is the generalized Levenshtein distance. It is defined as the lowest cost of transform-ing one sequence into the other by means of state insertions–deletions and state substitutions.
In this section, we integrate the sequence discrepancy analysis with the re-gression tree method introduced in [10]. The intuition of this rere-gression tree
4
Fig. 3: A numerical scale for the Ping Pong behavior is preferable
recursively partition each node using values of another variable. At each node, the variable and the split are chosen in such a way that the resulting child nodes differ as much as possible from one another or have, more or less equivalently, lowest within-group discrepancy. The process is repeated on each new node until a certain stopping criterion is reached. For the implementation of this method, we used the TraMineR [11] package of R.
As illustrated in Fig. 4, both social patterns (Push to Front and Ping Pong) result in clustered behaviors. In particular, the first split is among cases that Ping Pong or not (0 and greater than 0). Cases of the later category (no Ping Pong) last significantly less and visit a lot less frequently the “Queued” status.
based on the Ping Pong behavior, but this time the critical value is two. Cases that Ping Pong more than twice spend an important percentage of their lifetime in a “Queued” status, and are naturally prolonged.
Fig. 4: Discrepancy Analysis for cases lifecycle trajectories
4.2 Binary Classification
Support Vector Machines (SVM) is one of the most well-known supervised clas-sification algorithms. It was originally proposed by Vapnik [12]. The intuition of SVM is that the goal is to get an hyperplane that optimally distinguishes two classes of data. The major advantage of SVM is its minimal generalization error (at least in the case of binary classification - two classes of data) reached computationally efficiently. The SVM is one of the most applied algorithm of robust optimization in data mining. For a thorough exploration of theoretical and practical issues, we cite the classic work [13] and the works of Trafalis et al.
[14] and Xu et al. [15]. We used 10-fold cross validation on a training data set of case-label pairs (xi, yi) , i = 1, . . . , 7, where xi2 <nand y 2 { 1, 1}7. Number 7 indicate that seven factors (Country, Impact, Line, Function, Organization,
behavior. We used a linear kernel, as implemented by the LIBSVM library [16].
The overall accuracy of the model (for all folds, both classes) was 89.48%, but what is more important is to try to explain the factors that appear to be the most critical. According to [17], in linear SVMs, the use of wi2can be justified as a feature ranking criterion. Therefore, the following interesting points emerged:
– We identified that there are 3 countries (China, Sweden and U.S.A.) whose support teams are more prone to Ping Pong.
– The impact of cases does not appear to have an effect
– Ping Pong appears the most when cases are initiated in the front line.
– There are some particular Function Divisions and Organizations that are more prone to Ping Pong behavior
– Pushing to Front seems to have a negative impact
– As expected, the number of events per case is the most critical predictor of Ping Pong behavior
Overall, this paper applied a process mining approach to explore a real case study with the goal to provide insights to this implicit business process and to raise the capability of the company to handle service requests. The results presented in the previous sections allow the company to reach evidence-based response policies. In addition, since the identified issues are localized (certain support teams, certain divisions etc.), the evidence provided could aid company’s decision about the teams’ structure.
Acknowledgement
This research has been co-financed by the European Union (European Social Fund) and Greek national funds through the Operational Program "Education and Lifelong Learning"
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