Abstract
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.
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Notes
- 1.
Apromore—process mining tool. http://apromore.org/platform/tools/, last accessed 2021/01/13.
- 2.
OMG—Business Process Model and Notation 2.0. https://www.omg.org/spec/BPMN/2.0/About-BPMN/, last accessed 2021/01/13.
- 3.
Observed period is 1/102011–14/3/2012.
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Halaška, M., Šperka, R. (2021). TDABC and Estimation of Time Drivers Using Process Mining. In: Jezic, G., Chen-Burger, J., Kusek, M., Sperka, R., Howlett, R.J., Jain, L.C. (eds) Agents and Multi-Agent Systems: Technologies and Applications 2021. Smart Innovation, Systems and Technologies, vol 241. Springer, Singapore. https://doi.org/10.1007/978-981-16-2994-5_41
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