cd3075c3-6d26-4244-b532-226af096c0c320220824052018284wseas:wseasmdt@crossref.orgMDT DepositWSEAS TRANSACTIONS ON SYSTEMS2224-26781109-277710.37394/23202http://wseas.org/wseas/cms.action?id=40671620221620222110.37394/23202.2022.21https://wseas.com/journals/systems/2022.phpAgouzzalKawtarDepartment of Electrical Engineering, Mohammed V University of Rabat Mohammadia School of Engineers, MOROCCOAbbouAhmedDepartment of Electrical Engineering, Mohammed V University of Rabat Mohammadia School of Engineers, MOROCCOIndustrial firms must face important environmental challenges (greenhouse gas emissions, energy efficiency) and business imperatives. For this sector to accomplish a lower-cost energy transition, digitalization is a key lever. Today, the fourth industrial revolution is constructing a forward-thinking first industry known as industry 4.0, which combines many developing technologies to produce digital and efficient solutions. In this paper, we examine the impact of Industry 4.0 on the evolution of a new simulation modeling paradigm embodied by the concept of Digital Twin. To begin, we will discuss the industry 4.0 paradigm, its history, current state of development, and its impact on the development of the simulation modeling paradigm. A needs-based approach can result in the faster, deeper, and more extensive implementation of efficient systems. Furthermore, we present the methodology's multiple case studies and discuss several research and development projects involving the modeling of automated industrial processes that have been presented in recent scientific publications. The lack of tools, however, is not a problem because the current generation of general-purpose simulation modeling tools provides adequate integration options. However, to build on several physical levels of the integrated model system, close collaboration between academia and industrial partners is required to demonstrate industry and scientific community acceptance of the new analog modeling paradigm. Adoption and development of relevant morality in a needs-based process can lead to more efficient industrial automation implementation that is faster, deeper, and more extensive.8242022824202215716717https://wseas.com/journals/systems/2022/a345102-932.pdf10.37394/23202.2022.21.17https://wseas.com/journals/systems/2022/a345102-932.pdf10.5772/48467Dejan Gradišar, Gašper Mušič, Automated Petri-net modeling for batch production scheduling, Petri Nets – Manufacturing and Computer Science, 2012, pp. 3-26 10.1109/wsc.1998.746053Harrell. C. R., Hicks. D. A. (1998), Simulation software component architecture for simulationbased enterprises applications, Proceedings of the 1998 Winter Simulation Conference, 1998, pp. 1717-1721 10.1109/wsc.2016.7822313Jain. S, Lechevalier. D, Standards based generation of a virtual factory model, Proceedings of the 2016 Winter Simulation Conference, 2016, pp. 2762-2773 10.1016/j.proeng.2013.09.191Kannan. R. M, Santhi. H. M, Automated construction layout and simulation of concrete formwork systems using building information modeling, Proceedings of the 4th International Conference of Euro Asia Civil Engineering Forum 2013 (EACEF 2013), 64, 2013, pp. C7- C12 Kirchhoff. P, automatically generating flow shop simulation models from SAP data, Proceedings of the 2016 Winter Simulation Conference, 2016, pp. 3588-3589 Kljajić. M, Škraba. A, Simulation Approach to Decision assessment in Enterprises, Systems Theory Simulation, (2002), pp.199-210 Lattner. A, D. Bogon, T. Lorion, Timm. I. J, A knowledge-based approach to automated simulation model adaptation Spring Simulation Multiconference,2010, pp. 200-207 Borshchev. A The Big Book of Simulation Modelling, AnyLogic North America, C5-C8, (2013) 10.1109/naecon.1990.112838Conner. W. R, Automated Petri net modelling of military operations, National Aerospace and Electronics Conference NAECON, 1990, pp. 624-627 Forrester. J. W, Industrial Dynamics, Cambridge MA MIT Press. 1961 10.1109/wsc.2010.5679129Goldsman. D, Nance. R. E, Wilson. J. R, A brief history of simulation, Proceedings of the 2009 Winter Simulation Conference, 2010, pp. 310- 313 Goossens. P, Industry 4.0 and the Power of the Digital Twin., 2017, from Design News Direct,2017, pp. 5-3 Marolt.M, Lenart. G, Maletič. D, Kljajić, Borštnar. M, Pucihar. A Business model innovation insights, from a multiple case study of Slovenian SMEs, 49(3),2016, pp. 161-171 Merriam. S. B, Case Study Research in Education, A Qualitative Approach. San Francisco: Jossey-Bass, 1998 Robson. C, Real World Research, A Resource for Social Scientists and Practitioner Researchers Oxford, 1993 Rodič. B, Kanduč. T, Optimisation of a complex manufacturing process using discrete event simulation and a novel heuristic algorithm, International Journal Of Mathematical Models and Methods in Applied Sciences, 2015 Rodič. B, Kljajić. M, Mobile agents and XML for distributed simulation support, Simulationbased decision support, 2005, pp. 490-498 10.1016/j.ifacol.2015.06.141Rosen. R, von Wichert. G, Lo. G, Bettenhausen. K. D, (2015). About The Importance of Autonomy and Digital Twins for the Future of Manufacturing, IFAC-Papers Online, 2015, pp.567-572 Schwab. K. The Fourth Industrial Revolution, Geneva: World Economic Forum, 2016 10.1016/j.cirp.2017.04.045Stark. R, Kind. S, Neumeyer. S, Innovations in digital modelling for next-generation manufacturing system design. CIRP Annals - Manufacturing Technology,2017 10.1109/wsc.2016.7822163Thiers. G, Graunke, Christian. M, Automated production system simulations using commercial off-the-shelf simulation tools, Proceedings of the 2016 Winter Simulation Conference, 2016, pp. 1036-1047 Yin. K, Case Study Research and Applications: Design and Methods, London: SAGE. (2017) Zainal. Z, Case study as a research method, Journal Kemanusiaan, 2007, pp.1-6