- Autor
- Halaška Michal (Silesian University in Opava, Czech Republic), Šperka Roman (Silesian University in Opava, Czech Republic)
- Tytuł
- Performance of an Automated Process Model Discovery - the Logistics Process of a Manufacturing Company
- Źródło
- Engineering Management in Production and Services, 2019, nr 2, s. 106-118, rys., tab., bibliogr. 73 poz.
- Słowa kluczowe
- Proces produkcji, Modelowanie symulacyjne, Efektywność produkcji, Przemysł 4.0, Procesy logistyczne
Production process, Simulation modelling, Production effectiveness, Industry 4.0, Logistics processes - Uwagi
- summ.
- Abstrakt
- 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. (original abstract)
- Dostępne w
- Biblioteka SGH im. Profesora Andrzeja Grodka
- Pełny tekst
- Pokaż
- Bibliografia
- van der Aalst, W. M. P. (2005). Business Alignment: Using Process Mining As a Tool for Delta Analysis and Conformance Testing. Requirements Engineering 10(3), 198-211. doi: 10.1007/s00766-005-0001-x
- van der Aalst, W.M.P. (2011). Process Mining: Discovery, Conformance and Enhancement of Business Processes. Berlin Heidelberg, Germany: Springer-Verlag.
- van der Aalst, W.M.P. (2015). Extracting Event Data from Databases to Unleash Process Mining. In J. vom Brocke & T. Schmiedel (Eds.), BPM - Driving Innovation in a Digital World (pp. 105-128). Switzerland: Springer Cham (Management for Professionals).
- van der Aalst, W.M.P. (2016). Process Mining: Data Science in Action 2nd edn. Berlin Heidelberg, Germany: Springer-Verlag.
- van der et al. Aalst, W.M.P. (2011). Process Mining Manifesto. In F. Daniel, K. Barkaoui, & S. Dustdar (Eds.), Business Process Management Workshops. International Conference on Business Process Management (pp. 169-194). Berlin, Heidelberg, Germany: Springer
- van der Aalst, W.M.P., Rubin, V., Verbeek, H.M.V., van Dongen, B.F., Kindler, E., & Günther, C.W. (2010). Process mining: a two-step approach to balance between underfitting and overfitting. Software & Systems Modeling 9(1), 87-111. doi: 10.1007/s10270-008-0106-z
- van der Aalst, W.M.P., Weijters, T., & Maruster, L. (2004). Workflow mining: discovering process models from event logs. IEEE Transactions on Knowledge and Data Engineering 16(9), 1128-1142. doi: 10.1109/TKDE.2004.47
- van der Aalst, W.M.P., Weijters, T., & Maruster, L. (2004). Workflow mining: discovering process models from event logs. IEEE Transactions on Knowledge and Data Engineering 16(9), 1128-1142. doi: 10.1109/TKDE.2004.47
- Abar, S., Theodoropoulos, G.K., Lemarinier P., & O'Hare, G.M.P. (2017). Agent Based Modelling and Simulation tools: A review of the state-of-art software. Computer Science Review 24, 13-33. doi: 10.1016/j. cosrev.2017.03.001
- Agrawal, R., Gunopulos, D., & Leymann, F. (1998). Mining Process Models from Workflow Logs. In H. Schek, F. Saltor, I. Ramos, & G. Alonso (Eds.), Proceedings of the 6th International Conference on Extending Database Technology (EDBT'98), Lecture Notes in Computer Science, vol. 1377 (pp. 469-483), Berlin, Germany: Springer.
- AnyLogic (2019). Simulation modelling software tool. Retrieved from https://www.anylogic.com
- Augusto, A. Conforti, R., Dumas, M., La Rosa, M., Maggi, F.M., Marrella, A., Mecella, M., & Soo, A. (2017). Automated Discovery of Process Models from Event Logs: Review and Benchmark Retrieved from http://arxiv.org/abs/1705.02288
- Augusto, A., Conforti, R., Dumas, M., & La Rosa, M. (2017). Split Miner: Discovering Accurate and Simple Business Process Models from Event Logs. In 2017 IEEE International Conference on Data Mining (ICDM) (pp. 1-10), New Orleans, United States: IEEE.
- Augusto, A., Conforti, R., Dumas, M., La Rosa, M., & Bruno, G. (2018). Automated discovery of structured process models from event logs: The discover-and-structure approach. Data & Knowledge Engineering 117, 373-392. doi: 10.1016/j.datak.2018.04.007
- Augusto, A., et al. (2019). Measuring Fitness and Precision of Automatically Discovered Process Models: A Principled and Scalable Approach Retrieved from https://minerva-access.unimelb.edu.au/bitstream/handle/11343/219723/main.pdf
- Bannat, A., et al. (2011). Artificial Cognition in Production Systems. IEEE Transactions on Automation Science and Engineering 8(1), 148-174. doi: 10.1109/TASE.2010.2053534
- BIMP (2019). Business Process Simulator. Retrieved from http://bimp.cs.ut.ee
- Boes, J., & Migeon, F. (2017). Self-organizing multi-agent systems for the control of complex systems. Journal of Systems and Software 134, 12-28. doi: 10.1016/j. jss.2017.08.038
- Borshchev, A., & Filippov, A. (2004). From System Dynamics and Discrete Event to Practical Agent Based Modeling: Reasons, Techniques, Tools Retrieved from https://www.researchgate.net/publication/233820565_From_System_Dynamics_and_Discrete_Event_to_Practical_Agent_Based_Modeling_Reasons_Techniques_Tools
- van den Broucke, S.K.L.M., & De Weerdt, J. (2017). Fodina: A robust and flexible heuristic process discovery technique. Decision Support Systems 100, 109-118. doi: 10.1016/j.dss.2017.04.005
- Buijs, J.C.A.M., van Dongen, B.F., & van der Aalst, W.M.P. (2012). On the Role of Fitness, Precision, Generalization and Simplicity in Process Discovery. In Meersman, et al. (Eds.), On the Move to Meaningful Internet Systems: OTM 2012. OTM Confederated International Conferences (pp. 305-322). Berlin, Heidelberg, Germany: Springer. doi: 10.1007/978-3-642-33606-5_19
- Buijs, J.C.A.M., Dongen, B.F. van, & Aalst, W.M.P. van der (2012). A genetic algorithm for discovering process trees. In 2012 IEEE Congress on Evolutionary Computation (pp. 1-8). Brisbane, Australia: IEEE. doi: 10.1109/CEC.2012.6256458
- Buijs, J.C. a. M., van Dongen, B.F., & van der Aalst, W.M.P. (2014) Quality Dimensions in Process Discovery: The Importance of Fitness, Precision, Generalization and Simplicity. International Journal of Cooperative Information Systems 23(01), 1440001. doi: 10.1142/S0218843014400012
- Buijs, J.C.A.M., van Dongen, B.F., & van der Aalst, W.M.P. (2014). Quality Dimensions in Process Discovery: The Importance of Fitness, Precision, Generalization and Simplicity. International Journal of Cooperative Information Systems 23(1), p. 1440001. doi: 10.1142/S0218843014400012
- Chan, W.K.V., Son, Y.J., & Macal, C.M. (2010). Agent-based simulation tutorial - simulation of emergent behavior and differences between agent-based simulation and discrete-event simulation. In Proceedings of the 2010 Winter Simulation Conference (pp. 135-150). Baltimore, United States: IEEE. doi: 10.1109/WSC.2010.5679168
- Claes, D., Oliehoek, F., Baier, H., & Tuyls, K. (2017). Decentralised Online Planning for Multi-Robot Warehouse Commissioning. In Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems. Richland, SC: International Foundation for Autonomous Agents and Multiagent Systems (AAMAS '17) (pp. 492-500). Richalnd, United States: SC.
- Cook, J.E., & Wolf, E.L. (1998). Discovering Models of Software Processes from Event-Based Data. ACM Transactions on Software Engineering and Methodology 7, 215-249.
- De Weerdt, J., De Backer, M., Vanthienen, J., & Baesens, B. (2012). A multi-dimensional quality assessment of state-of-the-art process discovery algorithms using real-life event logs. Information Systems 37(7), 654-676. doi: 10.1016/j.is.2012.02.004
- Dinardo, G., Fabbiano, L., & Vacca, G. (2018). A smart and intuitive machine condition monitoring in the Industry 4.0 scenario. Measurement 126, 1-12. doi: 10.1016/j.measurement.2018.05.041
- van Dongen, B.F., & van der Aalst, W.M.P. (2004). Multiphase Process Mining: Building Instance Graphs. In P. Atzeni, et al. (Eds.). Conceptual Modeling - ER 2004. International Conference on Conceptual Modeling (pp. 362-376). Berlin Heidelberg, Germany: Springer. doi: 10.1007/978-3-540-30464-7_29
- Doomun, R., & Vunka Jungum, N. (2008). Business process modelling, simulation and reengineering: call centres. Business Process Management Journal 14(6), 838-848.
- Goedertier, S., Martens, D., Vanthiene, J., Baesens, B. (2009). Robust Process Discovery with Artificial Negative Events. Journal of Machine Learning Research 10, 1305-1340.
- Gries, M., Kulkarni, C., Sauer, C., & Keutzer, K. (2003). Comparing analytical modeling with simulation for network processors: a case study. In Automation and Test in Europe Conference and Exhibition 2003 Design (pp. 256-261). Munich, Germany: IEEE. doi: 10.1109/DATE.2003.1253838
- Günther, C.W., & van der Aalst, W.M.P. (2007). Fuzzy Mining - Adaptive Process Simplification Based on Multi-perspective Metrics. In G. Alonso, P. Dadam, & M. Rosemann, (Eds.), Business Process Management (pp. 328-343). Berlin Heidelberg, Germany: Springer.
- Guo, Q., Wen, L., Wang, Z., & Yu, P.S. (2015). Mining Invisible Tasks in Non-free-choice Constructs. In H.R. Motahari-Nezhad, J. Recker, & M. Weidlich, (Eds.). Business Process Management (pp. 109-125). Cham, Germany: Springer International Publishing.
- Hlupić, V., & Vukšić, V.B. (2004). Business Process Modelling Using SIMUL8. Retrieved from https://www.researchgate.net/publication/254419366_BUSINESS_PROCESS_MODELLING_USING_SIMUL8
- Hsieh, F.-S. (2015). Scheduling Sustainable Supply Chains based on Multi-agent Systems and Workflow Models. In 2015 10th International Conference on Intelligent Systems and Knowledge Engineering (pp. 252-259). New York, United States: IEEE.
- Kelly, R.A., et al. (2013). Selecting among five common modelling approaches for integrated environmental assessment and management. Environmental Modelling & Software 47, 159-181. doi: 10.1016/j.envsoft.2013.05.005
- Kolberg, D., & Zühlke, D. (2015). Lean Automation enabled by Industry 4.0 Technologies. IFAC-PapersOnLine 48(3), 1870-1875. doi: 10.1016/j.ifacol.2015.06.359
- Kozma, T. (2017). Cooperation in the supply chain network. Forum Scientiae Oeconomia 5(3), 45-58.
- Leemans, S.J.J., Fahland, D., & van der Aalst, W.M.P. (2013a). Discovering Block-Structured Process Models from Event Logs - A Constructive Approach. In J.-M. Colom, & J. Desel, (Eds.), Application and Theory of Petri Nets and Concurrency. International Conference on Applications and Theory of Petri Nets and Concurrency (pp. 311-329). Berlin Heidelberg, Germany: Springer. doi: 10.1007/978-3-642-38697-8_17
- Leemans, S.J.J., Fahland, D., & van der Aalst, W.M.P. (2013b). Discovering Block-Structured Process Models from Event Logs Containing Infrequent Behaviour. In N. Lohmann, M. Song, & P., Wohed (Eds.), Business Process Management Workshops. International Conference on Business Process Management (pp. 66-78). Berlin, Germany: Springer International Publishing. doi: 10.1007/978-3-319-06257-0_6
- Leemans, S.J.J., Fahland, D., & van der Aalst, W.M.P. (2014). Discovering Block-Structured Process Models from Incomplete Event Logs. In G. Ciardo, & E. Kindler (Eds.), Application and Theory of Petri Nets and Concurrency. International Conference on Applications and Theory of Petri Nets and Concurrency (pp. 91-110). Berlin, Germany: Springer International Publishing. doi: 10.1007/978-3-319-07734-5_6
- Leemans, S.J.J., Fahland, D., & van der Aalst, W.M.P. (2015). Scalable Process Discovery with Guarantees. In K. Gaaloul, et al. (Eds.), Enterprise, Business-Process and Information Systems Modeling. International Conference on Enterprise (pp. 85-101). Berlin, Germany: Springer International Publishing. doi: 10.1007/978-3-319-19237-6_6
- Leemans, S.J.J., Fahland, D., & van der Aalst, W.M.P. (2018). Scalable process discovery and conformance checking. Software & Systems Modeling 17(2), 599-631. doi: 10.1007/s10270-016-0545-x
- Leitão, P., et al. (2016). Smart Agents in Industrial Cyber-Physical Systems. Proceedings of the IEEE 104(5), 1086-1101. doi: 10.1109/JPROC.2016.2521931
- Macal, C.M. (2010). To Agent-based Simulation from System Dynamics. In Proceedings of the Winter Simulation Conference (pp. 371-382). Baltimore, Maryland: WSC.
- Macal, C.M., & North, M.J. (2008). Agent-based Modeling and Simulation: ABMS Examples. In Proceedings of the 40th Conference on Winter Simulation (pp. 101-112). Miami, Florida, United States: WSC.
- de Medeiros, A.K.A., van Dongen, B.F., van der Aalst, W.M.P., & Weijters, A.J.M.M. (2005). Process Mining: Extending the α-algorithm to Mine Short Loops Retrieved from https://pdfs.semanticscholar.org/dd4b/bc6f1550fc6601b21bd83f5c5ff3b13a309d.pdf
- de Medeiros, A.K.A., Weijters, A.J.M.M., & van der Aalst, W.M.P. (2007). Genetic process mining: an experimental evaluation. Data Mining and Knowledge Discovery 14(2), 245-304. doi: 10.1007/s10618-006-0061-7
- Nguyen, H., et al. (2016). Business Process Deviance Mining: Review and Evaluation Retrieved from http://arxiv.org/abs/1608.08252
- Pan, M., et al. (2015). Applying Industry 4.0 to the Jurong Island Eco-industrial Park. Energy Procedia 75, 1536-1541. doi: 10.1016/j.egypro.2015.07.313
- Piccarozzi, M., Aquilani, B., & Gatti, C. (2018). Industry 4.0 in Management Studies: A Systematic Literature Review. Sustainability 10(10), 3821. doi: 10.3390/su10103821
- Pisching, M.A., et al. (2018). An architecture based on RAMI 4.0 to discover equipment to process operations required by products. Computers & Industrial Engineering 125, 574-591. doi: 10.1016/j.cie.2017.12.029
- Pomarlan, M., & Bateman, J. (2018). Robot Program Construction via Grounded Natural Language Semantics & Simulation. In Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems (pp. 857-864). Richland, United States: International Foundation for Autonomous Agents and Multiagent Systems (AAMAS '18).
- Qin, J., Liu, Y., & Grosvenor, R. (2016). A Categorical Framework of Manufacturing for Industry 4.0 and Beyond. Procedia CIRP 52, 173-178. doi: 10.1016/j.procir.2016.08.005
- Roblek, V., Meško, M., & Krapež, A. (2016). A Complex View of Industry 4.0. SAGE Open 6(2), 1-11. doi: 10.1177/2158244016653987
- Rodič, B. (2017). Industry 4.0 and the New Simulation Modelling Paradigm. Organizacija 50(3), 193-207. doi: 10.1515/orga-2017-0017
- Savaglio, C., et al. (2018). Agent-Based Computing in the Internet of Things: A Survey. In M. Ivanović, et al. (Eds.), Intelligent Distributed Computing XI (pp. 307-320), Cham, Germany: Springer International Publishing. doi: 10.1007/978-3-319-66379-1_27
- Siebers, P.O., et al. (2010). Discrete-event simulation is dead, long live agent-based simulation!. Journal of Simulation 4(3), 204-210. doi: 10.1057/jos.2010.14
- Ślusarczyk, B. (2018). Industry 4.0: are we ready?, Polish Journal of Management Studies 17(1), 232-248. doi: 10.17512/pjms.2018.17.1.19
- Sony, M. (2018). Industry 4.0 and lean management: a proposed integration model and research propositions. Production & Manufacturing Research 6(1), 416-432. doi: 10.1080/21693277.2018.1540949
- Tiwari, A., Turner, C.J., & Majeed, B. (2008). A review of business process mining: State-of-the-art and future trends. ResearchGate 14(1), 5-22. doi: 10.1108/14637150810849373
- Verbeek, H.M.W., & van der Aalst, W.M.P. (2015). Decomposed Process Mining: The ILP Case. In F. Fournier, & J. Mendling, (Eds.), Business Process Management Workshops (pp. 264-276). Berlin: Springer International Publishing.
- Verbeek, H.M.W., van der Aalst, W.M.P., & Munoz-Gama, J. (2017). Divide and Conquer: A Tool Framework for Supporting Decomposed Discovery in Process Mining. The Computer Journal 60(11), 1649-1674. doi: 10.1093/comjnl/bxx040
- Wan, J., et al. (2018). Toward Dynamic Resources Management for IoT-Based Manufacturing. IEEE Communications Magazine 56(2), 52-59. doi: 10.1109/MCOM.2018.1700629
- Wang, S., et al. (2016). Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination. Computer Networks 101, 158-168.
- Weijters, A.J.M.M., & Ribeiro, J.T.S. (2011). Flexible Heuristics Miner (FHM). In 2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) (pp. 310-317). Paris, France: IEEE. doi: 10.1109/CIDM.2011.5949453
- Weijters, A.J.M.M., van der Aalst, W.M.P., & Medeiros, A.K.A.D. (2006). Process Mining with the Heuristics-Miner Algorithm. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.118.8288&rep=rep1&type=pdf
- Wen, L., et al. (2007). Mining process models with non-free-choice constructs. Data Mining and Knowledge Discovery 15(2), 145-180. doi: 10.1007/s10618-007-0065-y
- Wen, L., Wang, J., & Sun, J. (2006). Detecting Implicit Dependencies Between Tasks from Event Logs. In Frontiers of WWW Research and Development - AP-Web 2006. Asia-Pacific Web Conference (pp. 591-603), Berlin, Heidelberg, Germany: Springer. doi: 10.1007/11610113_52
- van der Werf, J.M.E.M., van Dongen, B.F., Hurkens, C.J., & Serebrenik, A. (2009). Process Discovery using Integer Linear Programming. Fundamenta Informaticae, 94(3-4), 387-412. doi: 10.3233/FI-2009-136
- van Zelst, S.J., et al. (2018). Discovering workflow nets using integer linear programming. Computing 100(5), 529-556. doi: 10.1007/s00607-017-0582-5
- Cytowane przez
- ISSN
- 2543-6597
- Język
- eng
- URI / DOI
- https://doi.org/10.2478/emj-2019-0014