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@inproceedings{51120, author = {Halaška, Michal and Šperka, Roman}, address = {Singapore}, booktitle = {Smart Innovation, Systems and Technologies}, doi = {http://dx.doi.org/10.1007/978-981-16-2994-5_41}, editor = {Jezic G., Chen-Burger J., Kusek M., Sperka R., Howlett R., Jain L}, keywords = {Process mining; TDABC; Costing systems; Time drivers; Loan process; Enterprise}, howpublished = {tištěná verze "print"}, language = {eng}, location = {Singapore}, pages = {489-499}, publisher = {Springer}, title = {TDABC and Estimation of Time Drivers Using Process Mining}, url = {https://link.springer.com/chapter/10.1007%2F978-981-16-2994-5_41}, year = {2021} }
TY - JOUR ID - 51120 AU - Halaška, Michal - Šperka, Roman PY - 2021 TI - TDABC and Estimation of Time Drivers Using Process Mining PB - Springer CY - Singapore KW - Process mining KW - TDABC KW - Costing systems KW - Time drivers KW - Loan process KW - Enterprise UR - https://link.springer.com/chapter/10.1007%2F978-981-16-2994-5_41 N2 - Costing systems play a crucial role in many managerial decisions; thus, it is crucial that costing systems provide appropriate information. Time-driven activity-based costing systems (TDABC) are successors of activity-based costing systems (ABC). ABCs were created in order to address shortcomings of traditional costing systems, while TDABCs were created to address mostly implementational shortcomings of ABCs. In this research, we focus on the advantages of integration of process mining (PM) and TDABC for estimation of activity durations used as time drivers for allocation of overhead costs. Thus, we have stated two research questions: (1) Can PM be used for estimation of time drivers? and (2) What are the benefits of using PM for the estimation of time drivers? To address these questions, we present a proof of concept, where we analyze two real-world datasets representing loan application process. Firstly, we clean both datasets, and then, we use PM techniques to discover process models representing the process. We show that PM can be used for time estimation and time drivers’ determination and that there are potential benefits to this approach. Furthermore, we discuss the possibility of using actual times instead of estimates. ER -
HALAŠKA, Michal a Roman ŠPERKA. TDABC and Estimation of Time Drivers Using Process Mining. In Jezic G., Chen-Burger J., Kusek M., Sperka R., Howlett R., Jain L. \textit{Smart Innovation, Systems and Technologies}. Singapore: Springer, 2021, s.~489-499. ISSN~2190-3018. Dostupné z: https://dx.doi.org/10.1007/978-981-16-2994-5\_{}41.
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