Abstract
Process Performance Indicators (PPIs) are quantifiable metrics to evaluate the business process performance providing essential information for decision-making as regards to efficiency and effectiveness. Nowadays, customizable process models and PPIs are usually modeled separately, especially when dealing with PPIs variability. Likewise, modeling PPI variants with no explicit link with the related customizable process generates redundant models, making adjustment and maintenance difficult. The use of appropriate methods and tools is needed to enable the integration and support of PPIs variability in customizable process models. In this paper, we propose a method based on the Process Performance Indicator Calculation Tree (PPICT), which allows to model the PPIs variability linked to customizable processes modeled on the Business Process Feature Model (BPFM) approach. The Process Performance Indicator Calculation (PPIC) method supports PPIs variability modeling through five design stages, which concerns the PPICT design, the integration of PPICT-BMFM and the configuration of required PPIs aligned with process activities. The PPIC method is supported by a metamodel and a graphical notation. This method has been implemented in a prototype using the ADOxx platform. A partial user-centered evaluation of the PPICT use was carried out in a real utility distribution case to model PPIs variability linked to a customizable process model.
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Diaz, D., Cortes-Cornax, M., Front, A., Labbe, C., Faure, D. (2021). A Method for Modeling Process Performance Indicators Variability Integrated to Customizable Processes Models. In: Cherfi, S., Perini, A., Nurcan, S. (eds) Research Challenges in Information Science. RCIS 2021. Lecture Notes in Business Information Processing, vol 415. Springer, Cham. https://doi.org/10.1007/978-3-030-75018-3_5
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