Financial constraints (Teerasoponpong & Sopadang, 2021Teerasoponpong, 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. http://dx.doi.org/10.1016/j.eswa.2020.114451. http://dx.doi.org/10.1016/j.eswa.2020.11...
; Saez et al., 2018Saez, 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. http://dx.doi.org/10.1109/TASE.2017.2784826. http://dx.doi.org/10.1109/TASE.2017.2784...
) |
Financial constraints on investing in equipment for automatic data collection, experts with knowledge in DES, or consultancies. |
Focus on practical decisions, but not optimal ones (Franco & Montibeller, 2010Franco, L. A., & Montibeller, G. (2010). Facilitated modelling in operational research. European Journal of Operational Research, 205(3), 489-500. http://dx.doi.org/10.1016/j.ejor.2009.09.030. http://dx.doi.org/10.1016/j.ejor.2009.09...
; Robinson et al., 2014Robinson, 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. http://dx.doi.org/10.1016/j.ejor.2012.12.024. http://dx.doi.org/10.1016/j.ejor.2012.12...
). |
Periodic and continuous use of simulation to base decision making (Rodič, 2017Rodič, B. (2017). Industry 4.0 and the new simulation modelling paradigm. Organizacija, 50(3), 193-207. http://dx.doi.org/10.1515/orga-2017-0017. http://dx.doi.org/10.1515/orga-2017-0017...
; Santos et al., 2022Santos, 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. http://dx.doi.org/10.1080/17477778.2020.1811172. http://dx.doi.org/10.1080/17477778.2020....
). |
Data gap (Omri et al., 2020Omri, 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. http://dx.doi.org/10.1016/j.jmsy.2020.04.002. http://dx.doi.org/10.1016/j.jmsy.2020.04...
; Ivers et al., 2016Ivers, 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. http://dx.doi.org/10.1108/JSBED-03-2014-0046. http://dx.doi.org/10.1108/JSBED-03-2014-...
; Byrne et al., 2013Byrne, 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. http://dx.doi.org/10.1109/WSC.2013.6721415. http://dx.doi.org/10.1109/WSC.2013.67214...
) |
Little or no data is available on the processes. |
Simplified data collection (Robinson et al., 2012Robinson, 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. http://dx.doi.org/10.1016/j.ejor.2011.12.029. http://dx.doi.org/10.1016/j.ejor.2011.12...
; Robinson et al., 2014Robinson, 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. http://dx.doi.org/10.1016/j.ejor.2012.12.024. http://dx.doi.org/10.1016/j.ejor.2012.12...
; Proudlove et al., 2017Proudlove, 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. http://dx.doi.org/10.1016/j.ejor.2017.06.002. http://dx.doi.org/10.1016/j.ejor.2017.06...
). |
The modeling phase might not require extended mapping and data collection times (Santos et al., 2022Santos, 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. http://dx.doi.org/10.1080/17477778.2020.1811172. http://dx.doi.org/10.1080/17477778.2020....
). |
Lack of experts (Teerasoponpong & Sopadang, 2021Teerasoponpong, 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. http://dx.doi.org/10.1016/j.eswa.2020.114451. http://dx.doi.org/10.1016/j.eswa.2020.11...
; Mittal et al., 2018Mittal, 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. http://dx.doi.org/10.1016/j.jmsy.2018.10.005. http://dx.doi.org/10.1016/j.jmsy.2018.10...
) |
Lack of experts in DES and in developing computer models. |
Active participation of stakeholders and decision-makers in a simpler modeling process (Robinson, 2001Robinson, S. (2001). Soft with a hard centre: discrete-event simulation in facilitation. The Journal of the Operational Research Society, 52(8), 905-915. http://dx.doi.org/10.1057/palgrave.jors.2601158. http://dx.doi.org/10.1057/palgrave.jors....
; Franco & Montibeller, 2010Franco, L. A., & Montibeller, G. (2010). Facilitated modelling in operational research. European Journal of Operational Research, 205(3), 489-500. http://dx.doi.org/10.1016/j.ejor.2009.09.030. http://dx.doi.org/10.1016/j.ejor.2009.09...
; Kotiadis & Tako, 2018Kotiadis, 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. http://dx.doi.org/10.1016/j.ejor.2017.10.047. http://dx.doi.org/10.1016/j.ejor.2017.10...
; Harper et al., 2021Harper, A., Mustafee, N., & Yearworth, M. (2021). Facets of trust in simulation studies. European Journal of Operational Research, 289(1), 197-213. http://dx.doi.org/10.1016/j.ejor.2020.06.043. http://dx.doi.org/10.1016/j.ejor.2020.06...
). |
Modeling from user-friendly interfaces that do not require the help of specialists throughout the entire project but in specific phases (Rodič, 2017Rodič, B. (2017). Industry 4.0 and the new simulation modelling paradigm. Organizacija, 50(3), 193-207. http://dx.doi.org/10.1515/orga-2017-0017. http://dx.doi.org/10.1515/orga-2017-0017...
). |
Restricted time (Barlas & Heavey, 2016Barlas, 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. http://dx.doi.org/10.1142/S1793962316300016. http://dx.doi.org/10.1142/S1793962316300...
) |
Little time is available to engage in a simulation project. |
Fast and flexible modeling (Robinson et al., 2014Robinson, 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. http://dx.doi.org/10.1016/j.ejor.2012.12.024. http://dx.doi.org/10.1016/j.ejor.2012.12...
). |
Faster and more flexible modeling (Vieira et al., 2018Vieira, 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. http://dx.doi.org/10.2507/IJSIMM17(3)429. http://dx.doi.org/10.2507/IJSIMM17(3)429...
). |