Abstract
Dynamically allocating the most appropriate resource to execute the different activities of a business process is an important challenge in business process management. An ineffective allocation may lead to an inadequate resources usage, higher costs, or a poor process performance. Different approaches have been used to solve this challenge: data mining techniques, probabilistic allocation, or even manual allocation. However, there is a need for methods that support resource allocation based on multi-factor criteria. We propose a framework for recommending resource allocation based on Process Mining that does the recommendation at sub-process level, instead of activity-level. We introduce a resource process cube that provides a flexible, extensible and fine-grained mechanism to abstract historical information about past process executions. Then, several metrics are computed considering different criteria to obtain a final recommendation ranking based on the BPA algorithm. The approach is applied to a help desk scenario to demonstrate its usefulness.
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Acknowledgments
This work is partially supported by Comisión Nacional de Investigación Científica – CONICYT – Ministry of Education, Chile, Ph.D. Student Fellowships, and by University of Costa Rica Professor Fellowships.
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Arias, M., Rojas, E., Munoz-Gama, J., Sepúlveda, M. (2016). A Framework for Recommending Resource Allocation Based on Process Mining. In: Reichert, M., Reijers, H. (eds) Business Process Management Workshops. BPM 2016. Lecture Notes in Business Information Processing, vol 256. Springer, Cham. https://doi.org/10.1007/978-3-319-42887-1_37
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DOI: https://doi.org/10.1007/978-3-319-42887-1_37
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