HALAŠKA, Michal a Roman ŠPERKA. Performance of an Automated Process Model Discovery - the Logistics Process of a Manufacturing Company. Engineering Management in Production and Services. 2019, roč. 11, č. 2, s. 106-118. ISSN 2543-6597. Dostupné z: https://dx.doi.org/10.2478/emj-2019-0014.
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Základní údaje
Originální název Performance of an Automated Process Model Discovery - the Logistics Process of a Manufacturing Company
Autoři HALAŠKA, Michal (203 Česká republika, domácí) a Roman ŠPERKA (703 Slovensko, garant, domácí).
Vydání Engineering Management in Production and Services, 2019, 2543-6597.
Další údaje
Originální jazyk angličtina
Typ výsledku Článek v odborném periodiku
Obor 50204 Business and management
Stát vydavatele Polsko
Utajení není předmětem státního či obchodního tajemství
Kód RIV RIV/47813059:19520/19:A0000021
Organizační jednotka Obchodně podnikatelská fakulta v Karviné
Doi http://dx.doi.org/10.2478/emj-2019-0014
Klíčová slova anglicky process mining; automated process discovery; simulation; agent-based simulation; ABS
Změnil Změnila: Ing. Petra Skoumalová, učo 50554. Změněno: 21. 4. 2020 10:44.
The simulation and modelling paradigms have significantly shifted in recent years under the influence of the Industry 4.0 concept. There is a requirement for a much higher level of detail and a lower level of abstraction within the simulation of a modelled system that continuously develops. Consequently, higher demands are placed on the construction of automated process models. Such a possibility is provided by automated process discovery techniques. Thus, the paper aims to investigate the performance of automated process discovery techniques within the controlled environment. The presented paper aims to benchmark the automated discovery techniques regarding realistic simulation models within the controlled environment and, more specifically, the logistics process of a manufacturing company. The study is based on a hybrid simulation of logistics in a manufacturing company that implemented the AnyLogic framework. The hybrid simulation is modelled using the BPMN notation using BIMP, the business process modelling software, to acquire data in the form of event logs. Next, five chosen automated process discovery techniques are applied to the event logs, and the results are evaluated. Based on the evaluation of benchmark results received using the chosen discovery algorithms, it is evident that the discovery algorithms have a better overall performance using more extensive event logs both in terms of fitness and precision. Nevertheless, the discovery techniques perform better in the case of smaller data sets, with less complex process models. Typically, automated discovery techniques have to address scalability issues due to the high amount of data present in the logs. However, as demonstrated, the process discovery techniques can also encounter issues of opposite nature. While discovery techniques typically have to address scalability issues due to large datasets, in the case of companies with long delivery cycles, long processing times and parallel production, which is common for the industrial sector, they have to address issues with incompleteness and lack of information in datasets. The management of business companies is becoming essential for companies to stay competitive through efficiency. The issues encountered within the simulation model will be amplified through both vertical and horizontal integration of the supply chain within the Industry 4.0. The impact of vertical integration in the BPMN model and the chosen case identifier is demonstrated. Without the assumption of smart manufacturing, it would be impossible to use a single case identifier throughout the entire simulation. The entire process would have to be divided into several subprocesses.
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