Thematic Section - Production Engineering leading the Digital Transformation

FaMoSim: a facilitated discrete event simulation framework to support online studies

Milena Silva de Oliveira; Carlos Henrique dos Santos; Gustavo Teodoro Gabriel; Fabiano Leal; José Arnaldo Barra Montevechi

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Paper aims: To propose a framework to support online simulation studies considering facilitated modeling and concepts of modern industry context, such as agility and flexibility.

Originality: Since the frameworks in the literature deal with simulation projects focused on healthcare and face-to-face meetings, the present work innovates by offering an agile and flexible guide for simulation projects in production systems, which also supports online interventions.

Research method: Action Research method was used to develop the framework. After its development, the FaMoSim (Facilitated Modeling Simulation) framework was applied in a real case to evaluate its applicability.

Main findings: In the application of FaMoSim, we achieved the framework's objectives: carrying out a faster (up to 3 months) and more flexible online modeling process; creating a simple computer model that does not require a complex data collection structure nor a specialist team; generating a better understanding of the process and assisting the stakeholders in identifying improvements.

Implications for theory and practice: Considering some challenges that prevent the expansion of DES studies, the framework assists in expanding DES studies in environments where it is not widely used. The framework supports online interventions, making it an interesting tool that can be used mainly in times of social distancing.


Facilitated modeling, Industry 4.0, Framework, Online intervention


Amaral, J. V. S., Montevechi, J. A. B., Miranda, R. C., & Sousa Junior, W. T. (2021). Metamodel-based simulation optimization: a systematic literature review. Simulation Modelling Practice and Theory.

Banks, J. (Ed.). (1998). Handbook of simulation: principles, methodology, advances, applications, and practice. New York: John Wiley & Sons.

Banks, J., Carson, J. S., Nelson, B., & Nicol, D. M. (2010). Discrete event system simulation (5th ed.). New Jersey: Pearson Prentice Hall.

Barlas, P., & Heavey, C. (2016). Automation of input data to discrete event simulation for manufacturing: a review. International Journal of Modeling, Simulation, and Scientific Computing, 07(1), 1630001.

Byrne, J., Byrne, P. J., Ferreira, D. C., & Ivers, A. M. (2013). Towards a cloud based SME data adapter for simulation modelling. In Proceedings of the Winter Simulations Conference. New York: IEEE.

Choi, S., & Kang, G. (2018). Towards development of cyber-physical systems based on integration of heterogeneous technologies. International Journal of Computer Applications in Technology, 58(2), 129-136.

Coghlan, D., Shani, A. R., Roth, J., & Sloyan, R. M. (2014). Executive development through insider action research: voices of insider action researchers. Journal of Management Development, 33(10), 991-1003.

Coughlan, P., & Coghlan, D. (2002). Action research for operations management. International Journal of Operations & Production Management, 22(2), 220-240.

Dani, V. S., Freitas, C. M. D. S., & Thom, L. H. (2019). Ten years of visualization of business process models: A systematic literature review. Computer Standards & Interfaces, 66, 103347.

Ferreira, W. P. A., Armellini, F., & Santa-Eulalia, L. A. (2020). Simulation in industry 4.0: A state-of-the-art review. Computers & Industrial Engineering, 149, 106868.

Franco, L. A., & Montibeller, G. (2010). Facilitated modelling in operational research. European Journal of Operational Research, 205(3), 489-500.

Gabriel, G. T., Campos, A. T., Leal, F., & Montevechi, J. A. B. (2022). Good practices and deficiencies in conceptual modelling: a systematic literature review. Journal of Simulation, 16(1), 84-100.

Goodall, P., Sharpe, R., & West, A. (2019). A data-driven simulation to support remanufacturing operations. Computers in Industry, 105, 48-60.

Hameed, B. Z., Tanidir, Y., Naik, N., Teoh, J. Y. C., Shah, M., Wroclawski, M. L., Kunjibettu, A. B., Castellani, D., Ibrahim, S., Silva, R. D., Rai, B., de la Rosette, J. J. M. C. H., Tp, R., Gauhar, V., & Somani, B. (2021). Will “hybrid” meetings replace face-to-face meetings post COVID-19 era? Perceptions and views from the urological community. Urology, 156, 52-57. PMid:33561472.

Harper, A., Mustafee, N., & Yearworth, M. (2021). Facets of trust in simulation studies. European Journal of Operational Research, 289(1), 197-213.

Itzchakov, G., & Grau, J. (2022). High-quality listening in the age of COVID-19: a key to better dyadic communication for more effective organizations. Organizational Dynamics, 51(2), 100820. PMid:35719174.

Ivers, A. M., Byrne, J., & Byrne, P. J. (2016). Analysis of SME data readiness: a simulation perspective. Journal of Small Business and Enterprise Development, 23(1), 163-188.

Kotiadis, K., & Tako, A. A. (2018). Facilitated post-model coding in discrete event simulation (DES): A case study in healthcare. European Journal of Operational Research, 266(3), 1120-1133.

Kotiadis, K., & Tako, A. A. (2021). A tutorial on involving stakeholders in facilitated simulation studies. In Proceedings of the Operational Research Society Simulation Workshop (SW21). Birmingham: The Operational Research Society.

Kotiadis, K., Tako, A. A., & Vasilakis, C. (2014). A participative and facilitative conceptual modelling framework for discrete event simulation studies in healthcare. The Journal of the Operational Research Society, 65(2), 197-213.

McKay, J., & Marshall, P. (2001). The dual imperatives of action research. Information Technology & People, 14(1), 46-59.

Mittal, S., Khan, M. A., Romero, D., & Wuest, T. (2018). A critical review of smart manufacturing & Industry 4.0 maturity models: implications for small and medium-sized enterprises (SMEs). Journal of Manufacturing Systems, 49, 194-214.

Moeuf, A., Pellerin, R., Lamouri, S., Tamayo-Giraldo, S., & Barbaray, R. (2018). The industrial management of SMEs in the era of Industry 4.0. International Journal of Production Research, 56(3), 1118-1136.

Montevechi, J. A. B., Leal, F., Pinho, A. F., Costa, R. F., Oliveira, M. L. M., & Silva, A. L. F. (2010, December). Conceptual modeling in simulation projects by mean adapted IDEF: an application in a Brazilian tech company. In Proceedings of the 2010 Winter Simulation Conference (pp. 1624-1635). New York: IEEE.

Mourtzis, D. (2020). Simulation in the design and operation of manufacturing systems: state of the art and new trends. International Journal of Production Research, 58(7), 1927-1949.

Oeppen, R. S., Shaw, G., & Brennan, P. A. (2020). Human factors recognition at virtual meetings and video conferencing: how to get the best performance from yourself and others. British Journal of Oral & Maxillofacial Surgery, 58(6), 643-646. PMid:32417017.

Oliveira, M. S., Leal, F., Pereira, T. F., & Montevechi, J. A. B. (2022). Facilitated discrete event simulation for industrial processes: a critical analysis. International Journal of Simulation Modelling, 21(3), 395-404.

Omri, N., Al Masry, Z., Mairot, N., Giampiccolo, S., & Zerhouni, N. (2020). Industrial data management strategy towards an SME-oriented PHM. Journal of Manufacturing Systems, 56, 23-36.

Pereira, T. F., Montevechi, J. A. B., Miranda, R. D. C., & Friend, J. D. (2015). Integrating soft systems methodology to aid simulation conceptual modeling. International Transactions in Operational Research, 22(2), 265-285.

Proudlove, N. C., Bisogno, S., Onggo, B. S., Calabrese, A., & Ghiron, N. L. (2017). Towards fully-facilitated discrete event simulation modelling: addressing the model coding stage. European Journal of Operational Research, 263(2), 583-595.

Richter, A. (2020). Locked-down digital work. International Journal of Information Management, 55, 102157. PMid:32836629.

Robinson, S. (2001). Soft with a hard centre: discrete-event simulation in facilitation. The Journal of the Operational Research Society, 52(8), 905-915.

Robinson, S. (2008). Conceptual modelling for simulation part I: definition and requirements. The Journal of the Operational Research Society, 59(3), 278-290.

Robinson, S., Radnor, Z. J., Burgess, N., & Worthington, C. (2012). SimLean: Utilising simulation in the implementation of lean in healthcare. European Journal of Operational Research, 219(1), 188-197.

Robinson, S., Worthington, C., Burgess, N., & Radnor, Z. J. (2014). Facilitated modelling with discrete-event simulation: reality or myth? European Journal of Operational Research, 234(1), 231-240.

Rodič, B. (2017). Industry 4.0 and the new simulation modelling paradigm. Organizacija, 50(3), 193-207.

Saez, M., Maturana, F. P., Barton, K., & Tilbury, D. M. (2018). Real-time manufacturing machine and system performance monitoring using internet of things. IEEE Transactions on Automation Science and Engineering, 15(4), 1735-1748.

Santos, C. H., Montevechi, J. A. B., Queiroz, J. A., Miranda, R. C., & Leal, F. (2021). Decision support in productive processes through DES and ABS in the Digital Twin era: a systematic literature review. International Journal of Production Research.

Santos, C. H., Queiroz, J. A., Leal, F., & Montevechi, J. A. B. (2022). Use of simulation in the Industry 4.0 context: creation of a digital twin to optimize decision making on non-automated process. Journal of Simulation, 16(3), 284-297.

Scheidegger, A. P. G., Pereira, T. F., de Oliveira, M. L. M., Banerjee, A., & Montevechi, J. A. B. (2018). An introductory guide for hybrid simulation modelers on the primary simulation methods in industrial engineering identified through a systematic review of the literature. Computers & Industrial Engineering, 124, 474-492.

Skoogh, A., Perera, T., & Johansson, B. (2012). Input Data management in simulation: industrial practices and future trends. Simulation Modelling Practice and Theory, 29, 181-192.

Standaert, W., Muylle, S., & Basu, A. (2022). Business meetings in a post-pandemic world: when and how to meet virtually? Business Horizons, 65(3), 267-275. PMid:36062237.

Tako, A. A., Robinson, S., Gogi, A., Radnor, Z., & Davenport, C. (2021, March). Using facilitated simulation to evaluate integrated community-based health and social care services. In Proceedings of the Operational Research Society Simulation Workshop (SW21). Birmingham: The Operational Research Society.

Tako, A. A., & Kotiadis, K. (2015). PartiSim: a multi-methodology framework to support facilitated simulation modelling in healthcare. European Journal of Operational Research, 244(2), 555-564.

Tako, A. A., & Kotiadis, K. (2018, December). Participative simulation (PartiSim): a facilitated simulation approach for stakeholder engagement. In Proceedings of the 2018 Winter Simulation Conference (WSC). New York: IEEE.

Tako, A. A., & Kotiadis, K. (2012). Facilitated conceptual modelling: practical issues and reflections. In Proceedings of the 2012 Winter Simulation Conference (WSC) (pp. 1-12). New York: IEEE.

Tako, A. A., Robinson, S., Gogi, A., Radnor, Z., & Davenport, C. (2019, December). Evaluating community-based integrated health and social care services: the Simtegr8 approach. In Proceedings of the 2019 Winter Simulation Conference (WSC). New York: IEEE.

Tako, A. A., Tsioptsias, N., & Robinson, S. (2020). Can we learn from simplified simulation models? An experimental study on user learning. Journal of Simulation, 14(2), 130-144.

Teerasoponpong, S., & Sopadang, A. (2021). A simulation-optimization approach for adaptive manufacturing capacity planning in small and medium-sized enterprises. Expert Systems with Applications, 168, 114451.

Vieira, A. A. C., Dias, L. M. S., Santos, M. Y., Pereira, G. A. B., & Oliveira, J. A. (2018). Setting an Industry 4.0 research and development agenda for simulation: a literature review. International Journal of Simulation Modelling, 17(3), 377-390.

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