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Title: Business process model plasticity: Measuring the capacity to redesign prior to implementation
Authors: Tsakalidis, George
Vergidis, Kostas
Tambouris, Efthimios
Type: Conference Paper
Subjects: FRASCATI::Natural sciences::Computer and information sciences
FRASCATI::Social sciences::Economics and Business::Business and Management
Keywords: Business Process Redesign
Activity Resequencing
Bender Method
Logistic Regression
Threshold Evaluation
Issue Date: 2021
First Page: 31
Last Page: 41
Volume Title: 2021 IEEE 23rd Conference on Business Informatics (CBI)
Abstract: During the last decades various approaches have been put forward to systematize Business Process Redesign (BPR) based on continuous process monitoring and analysis. The effectiveness of BPR was also facilitated by recent advances in process mining and analytics, given the influence of Big Data to the way organizations discover, monitor and improve their processes. This swift in research focus has left a considerable gap in literature regarding the evaluation of the redesign capacity of business process (BP) models prior to redesign method implementation. In this paper, we introduce the notion of BP model plasticity, i.e., a new process quality characteristic inspired by brain neuroplasticity that measures the capability of a BP model to be redesigned based on particular BPR heuristics. We claim that the applicability of BPR heuristics to process models is directly affected by internal measures related to each heuristic and their correlation may predict the BPR effectiveness. This paper applies a state-of-the-art method for calculating the model plasticity based on regression analysis and the application of the bender method on empirical data. The measurement is conducted on BPMN models from literature and the extracted thresholds proved effective on classifying the level of plasticity. The approach contributes to the BPR domain by offering a quantitative evaluation of thresholds that reflect the applicability of heuristics prior to implementation. This can prove a useful tool for the facilitation of inexperienced modelers towards more effective redesign decisions.
ISBN: 978-1-6654-2069-3
Other Identifiers: 10.1109/CBI52690.2021.00014
Appears in Collections:Department of Applied Informatics

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