<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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.

Performance of an automated process model discovery – the logistics process of a manufacturing company

Engineering Management in Production and Services

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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...

Algorithm	Fitness	Precision	F-score	Soundness	Struct.
sHM6	0.9488	0.0385	0.0739	Sound	1.0000
SM	-	-	-	-	-
IM	1.0000	0.0134	0.1775	Sound	1.0000
FO	0.7628	0.0130	0.0264	Sound	1.0000
A$	-	-	-	-	-

Algorithm	Fitness	Precision	F-score	Soundness	Struct.
sHM6	1.0000	0.1002	0.1821	Sound	1.0000
SM	1.0000	0.1002	0.1821	Sound	1.0000
IM	1.0000	0.1002	0.1821	Sound	1.0000
FO	1.0000	0.1002	0.1821	Sound	1.0000
A$	1.0000	0.1002	0.1821	Sound	1.0000

Automated process discovery technique	Related studies
Structure (sHM6) HeuristicsMiner	Augusto et al. (2018)
Split Miner (SM)	Augusto et al. (2017)
Inductive Miner (IM)	Leemans et al. (2014)
Fodina (FO)	Broucke and Weerdt (2017)
α$	Guo et al. (2015)

Algorithm	Fitness	Precision	F-score	Soundness	Struct.
sHM6	1.0000	0.0763	0.1418	Sound	1.0000
SM	1.0000	0.0763	0.1418	Sound	1.0000
IM	1.0000	0.0763	0.1418	Sound	1.0000
FO	0.5918	0.0810	0.1425	Sound	1.0000
A$	1.0000	0.2269	0.3698	Sound	1.0000

Algorithm	Fitness	Precision	F-score	Soundness	Struct.
sHM6	1.0000	0.1105	0.1990	Sound	1.0000
SM	-	-	-	-	-
IM	1.0000	0.1105	0.1990	Sound	1.0000
FO	1.0000	0.1105	0.1990	Sound	1.0000
A$	-	-	-	-	-

Performance of an automated process model discovery – the logistics process of a manufacturing company

Published Online: Jul 30, 2019

Page range: 106 - 118

Received: Jan 02, 2019

Accepted: Jun 15, 2019

DOI: https://doi.org/10.2478/emj-2019-0014

© 2019 Michal Halaška, Roman Šperka, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Fig. 1

Fig. 2

Fig. 3

Fig.4

Fitness and precision values for the process model depicted in Fig. 4 – 100 cases

Fitness and precision values for the process model depicted in Fig. 3 – 8000 cases

Selected automated process discovery techniques

Fitness and precision values for the process model depicted in Fig. 3 – 100 cases

Fitness and precision values for the process model depicted at Fig. 4 – 8000 cases