D 2021

TDABC and Estimation of Time Drivers Using Process Mining

HALAŠKA, Michal a Roman ŠPERKA

Základní údaje

Originální název

TDABC and Estimation of Time Drivers Using Process Mining

Autoři

HALAŠKA, Michal (203 Česká republika, domácí) a Roman ŠPERKA (703 Slovensko, garant, domácí)

Vydání

Singapore, Smart Innovation, Systems and Technologies, od s. 489-499, 11 s. 2021

Nakladatel

Springer

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

50204 Business and management

Stát vydavatele

Singapur

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

tištěná verze "print"

Odkazy

Kód RIV

RIV/47813059:19520/21:A0000203

Organizační jednotka

Obchodně podnikatelská fakulta v Karviné

ISSN

Klíčová slova anglicky

Process mining; TDABC; Costing systems; Time drivers; Loan process; Enterprise

Příznaky

Mezinárodní význam, Recenzováno
Změněno: 5. 8. 2021 11:13, doc. RNDr. Ing. Roman Šperka, Ph.D.

Anotace

V originále

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.