Abstract
Process mining is not only about discovery and conformance checking of business processes. It is also focused on enhancing processes to improve the business performance. While from a business perspective this third main stream is definitely as important as the others if not even more, little research work has been conducted. The existing body of work on process enhancement mainly focuses on ensuring that the process model is adapted to incorporate behavior that is observed in reality. It is less focused on improving the performance of the process. This paper reports on a methodology that creates an enhanced model with an improved performance level. The enhancements of the model limit incorporated behavior to only those parts that do not violate any business rules. Finally, the enhanced model is kept as close to the original model as possible. The practical relevance and feasibility of the methodology is assessed through two case studies. The result shows that the process models improved through our methodology, in comparison with state-of the art techniques, have improved KPI levels while still adhering to the desired prescriptive model.
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Notes
- 1.
In the algorithm, symbol \(\oplus \) identifies the concatenation of two sequences. Given an alignment \(\gamma \), \(\gamma (i)\) denotes the i-th element of the alignment.
- 2.
Here we talk in term of classification tree, which can be a decision or regression tree. Decision trees are used when the KPI values are discrete; otherwise, we use regression tree.
- 3.
Notation \(\{ d_1 \rightarrowtail c_1,\ldots ,d_n \rightarrowtail c_n\}\) indicates a function f with domain \(\{d_1,\ldots ,d_n\}\) in which \(f(d_1)=c_1, \ldots , f(d_n)=c_n\).
- 4.
Step 2.1 is a conceptual step. In practice, one does not need to recompute the alignments for the cluster logs as one can simply reuse the alignments obtained as result of Step 1.1.
- 5.
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Dees, M., de Leoni, M., Mannhardt, F. (2017). Enhancing Process Models to Improve Business Performance: A Methodology and Case Studies. In: Panetto, H., et al. On the Move to Meaningful Internet Systems. OTM 2017 Conferences. OTM 2017. Lecture Notes in Computer Science(), vol 10573. Springer, Cham. https://doi.org/10.1007/978-3-319-69462-7_15
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