HALAŠKA, Michal and 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. Smart Innovation, Systems and Technologies. Singapore: Springer, 2021, p. 489-499. ISSN 2190-3018. Available from: https://dx.doi.org/10.1007/978-981-16-2994-5_41.
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Basic information
Original name TDABC and Estimation of Time Drivers Using Process Mining
Authors HALAŠKA, Michal (203 Czech Republic, belonging to the institution) and Roman ŠPERKA (703 Slovakia, guarantor, belonging to the institution).
Edition Singapore, Smart Innovation, Systems and Technologies, p. 489-499, 11 pp. 2021.
Publisher Springer
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 50204 Business and management
Country of publisher Singapore
Confidentiality degree is not subject to a state or trade secret
Publication form printed version "print"
WWW URL
RIV identification code RIV/47813059:19520/21:A0000203
Organization unit School of Business Administration in Karvina
ISSN 2190-3018
Doi http://dx.doi.org/10.1007/978-981-16-2994-5_41
Keywords in English Process mining; TDABC; Costing systems; Time drivers; Loan process; Enterprise
Tags International impact, Reviewed
Changed by Changed by: doc. RNDr. Ing. Roman Šperka, Ph.D., učo 18157. Changed: 5/8/2021 11:13.
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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