1. Proceedings
  2. I3M
  3. 2022
  4. MAS
  5. 10.46354/i3m.2022.mas.018

Towards a Smart Factory: An integrated approach based on Simulation and AHP

  • a Fabio De Felice,
  • b Cristina De Luca,
  • c Emanuele Guadalupi, 
  • d Antonella Petrillo 
  • a.b.d Università degli Studi di Napoli “Parthenope”, Isola C4 Centro Direzionale Napoli (NA), 80143, Italy
  • e I.M.I. Industria Monouso Italiana s.p.a. – s.s. Appia km 17,800 81043 Vitulazio (CE), Italy
Cite as
De Felice F., De Luca C., Guadalupi E., and Petrillo A. (2022).,Towards a Smart Factory: An integrated approach based on Simulation and AHP. Proceedings of the 21st International Conference on Modelling and Applied Simulation MAS 2022). , 018 . DOI: https://doi.org/10.46354/i3m.2022.mas.018

Abstract

The optimization of an industrial production process is a complex problem and it implies difficult decisions in terms of technological investments, operating costs, work organization, and economic investments. This is even more true in an economic context characterized by a digital transition that involves new business strategies. In this scenario, the application of digital technologies and multi-criteria decision-making techniques helps to identify the best strategy to improve operational performance. In particular, a discrete event simulation tool to optimize production performance is proposed in this study. More in detail, the present research presents a business model based on digitalization of an Italian SME. Firstly, a digital model (using a simulation software) was developed to carry out experiments and what-if scenarios of the existing production system. Secondly, after the analysis of the results and its discussion, the definition of the most appropriate business strategy was performed using the the well-known multicriteria method, Analytical Hierarchy Process (AHP). The study shows that simulation approach integrated with a multi criteria analysis can be a very powerful tool as a decision support system towards the smart factory paradigm. The studio is an academic pilot study scalable in different sectors not just the manufacturing sector. 

References

  1. Ahn, J.M.; Minshall, T.; Mortara, L. Open innovation: A new classification and its impact on firm performance in innovative smes. J. Innov. Manag. 2015, 3, 33–54.
  2. Al-Aomar, R., Williams, E. J., & Ulgen, O. M. (2015). Process simulation using witness. John Wiley & Sons.
  3. Anderl, R. (2015). Industrie 4.0–technological approaches, use cases, and implementation. at-Automatisierungstechnik, 63(10), 753-765.
  4. Bi, Z., Liu, Y., Krider, J., Buckland, J., Whiteman, A., Beachy, D., & Smith, J. (2018). Real-time force monitoring of smart grippers for Internet of Things (IoT) applications. Journal of Industrial Information Integration, 11, 19-28.
  5. Blaž, R. (2017). Industry 4.0 and the new simulation modelling paradigm. Organizacija, 50(3), 193-207.
  6. Camarinha-Matos, L. M., Afsarmanesh, H., Galeano, N., & Molina, A. (2009). Collaborative networked organizations–Concepts and practice in manufacturing enterprises. Computers & Industrial Engineering, 57(1), 46-60.
  7. Cheng Ying, Y., Ab-Samat, H., Kamaruddin, S. (2016). Practical production layout design for multi-product and small-lot-size production: A case study. Jurnal Teknologi, 78(7), pp. 161-175.
  8. Culot, G., Orzes, G., Sartor, M., & Nassimbeni, G. (2020). The future of manufacturing: A Delphi-based scenario analysis on Industry 4.0. Technological forecasting and social change, 157, 120092.
  9. De Felice, F., Petrillo, A., Zomparelli, F. (2018). Prospective design of smart manufacturing: An Italian pilot case study. Manufacturing Letters, 15, pp. 81-85.
  10. dos Santos, C. H., Montevechi, J. A. B., de Queiroz, J. A., de Carvalho Miranda, R., & Leal, F. (2022). Decision support in productive processes through DES and ABS in the Digital Twin era: a systematic literature review. International Journal of Production Research, 60(8), 2662-2681.
  11. Flores-Garcia, E., Bruch, J., Wiktorsson, M., & Jackson, M. (2020). Decision-making approaches in process innovations: an explorative case study. Journal of Manufacturing Technology Management.
  12. Gao, H.P., Wang, H.J., Zhang, Z.W., Yang, Q.D. (2016). Study of connecting rod cracking production line layout planning system based on VB6.0. Advanced Materials Research, 712-715, pp. 2625-2630.
  13. Guizzi, G., De Felice, F., Falcone, D. (2019). An integrated and parametric simulation model to improve production and maintenance processes: Towards a digital factory performance. Computers and Industrial EngineeringVolume 137.
  14. Han, Y., Jeong, J., Ko, M. H., Lee, S. U. C. H. U. L., & Kim, J. (2018). Analysis of global competitiveness of engineering modeling and simulation technology for next-manufacturing innovation: Using quantitative analysis of patents and papers. ICIC Express Letters, 9(4), 339-346.
  15. Hofmann, E., & Rüsch, M. (2017). Industry 4.0 and the current status as well as future prospects on logistics. Computers in industry, 89, 23-34.
  16. Hossain, M., & Kauranen, I. (2016). Open innovation in SMEs: a systematic literature review. Journal of Strategy and management.
  17. Kamble, S. S., & Gunasekaran, A. (2021). Analysing the role of Industry 4.0 technologies and circular economy practices in improving sustainable performance in Indian manufacturing organisations. Production Planning & Control, 1-15.
  18. Karnon, J., & Haji Ali Afzali, H. (2014). When to use discrete event simulation (DES) for the economic evaluation of health technologies? A review and critique of the costs and benefits of DES. Pharmacoeconomics, 32(6), 547-558.
  19. Lee, J., Bagheri, B., & Kao, H. A. (2015). A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manufacturing letters, 3, 18-23.
  20. Masood, T., & Sonntag, P. (2020). Industry 4.0: Adoption challenges and benefits for SMEs. Computers in Industry, 121, 103261.
  21. Meolic, R., & Kapus, T. (2022). Generating and Employing Witness Automata for ACTLW Formulae. IEEE Access.
  22. 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.
  23. 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.
  24. Moetakef-Imani, B., & Yussefian, N. Z. (2009). Dynamic simulation of boring process. International Journal of Machine Tools and Manufacture, 49(14), 1096-1103.
  25. Moeuf, A., Lamouri, S., Pellerin, R., Tamayo-Giraldo, S., Tobon-Valencia, E., & Eburdy, R. (2020). Identification of critical success factors, risks and opportunities of Industry 4.0 in SMEs. International Journal of Production Research, 58(5), 1384-1400.
  26. 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.
  27. 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.
  28. Müller, J. M., Kiel, D., & Voigt, K. I. (2018). What drives the implementation of Industry 4.0? The role of opportunities and challenges in the context of sustainability. Sustainability, 10(1), 247.
  29. Mustafee, N., Mittal, S., Diallo, S., & Zacharewicz, G. (2018). The advances in the state of the art of modeling and simulation: Discrete event system specification (DEVS). Simulation.
  30. Nam, S. M., & Cho, T. H. (2020). Discrete event simulation–based energy efficient path determination scheme for probabilistic voting–based filtering scheme in sensor networks. International Journal of Distributed Sensor Networks, 16(8), 1550147720949134.
  31. Negahban, A., & Smith, J. S. (2014). Simulation for manufacturing system design and operation: Literature review and analysis. Journal of manufacturing systems, 33(2), 241-261.
  32. Ortíz-Barrios, M., Petrillo, A., De Felice, F., (...), Jiménez-Delgado, G., Borrero-López, L. (2021). A dispatching-fuzzy ahp-topsis model for scheduling flexible job-shop systems in industry 4.0 context. Applied Sciences (Switzerland) 11(11),5107.
  33. Peillon, S., & Dubruc, N. (2019). Barriers to digital servitization in French manufacturing SMEs. Procedia CIRP, 83, 146-150
  34. Pereira, A. C., & Romero, F. (2017). A review of the meanings and the implications of the Industry 4.0 concept. Procedia Manufacturing, 13, 1206-1214.
  35. Ramírez-Durán, V. J., Berges, I., & Illarramendi, A. (2021). Towards the implementation of Industry 4.0: A methodology-based approach oriented to the customer life cycle. Computers in Industry, 126, 103403.
  36. Reischauer, G. (2018). Industry 4.0 as policy-driven discourse to institutionalize innovation systems in manufacturing. Technological Forecasting and Social Change, 132, 26-33.
  37. Robinson, S., Edwards, J. S., & Yongfa, W. (2003). Linking the Witness Simulation Software to an Expert System to Represent a Decision–Making Process. Journal of Computing and Information Technology, 11(2), 123-133.
  38. Rodič, B. (2017). Industry 4.0 and the new simulation modelling paradigm. Organizacija, 50(3), 193.
  39. Rüßmann, M., Lorenz, M., Gerbert, P., Waldner, M., Justus, J., Engel, P., & Harnisch, M. (2015). Industry 4.0: The future of productivity and growth in manufacturing industries. Boston consulting group, 9(1), 54-89.
  40. Saaty, T.L., (1982). How to structure and make choices in complex problems. Human Systems Management, 3(4), pp. 256-261.
  41. Saaty, T.L., (1979). Applications of analytical hierarchies. Mathematics and Computers in Simulation 21(1), pp. 1-20.
  42. Schneider, P. (2018). Managerial challenges of Industry 4.0: an empirically backed research agenda for a nascent field. Review of Managerial Science, 12(3), 803-848.
  43. Schumacher, A., Erol, S., & Sihn, W. (2016). A maturity model for assessing Industry 4.0 readiness and maturity of manufacturing enterprises. Procedia Cirp, 52, 161-166.
  44. Shi, Z., Xie, Y., Xue, W., Chen, Y., Fu, L., & Xu, X. (2020). Smart factory in Industry 4.0. Systems Research and Behavioral Science, 37(4), 607-617.
  45. Torn, I. A. R., & Vaneker, T. H. (2019). Mass Personalization with Industry 4.0 by SMEs: A concept for collaborative networks. Procedia manufacturing, 28, 135-141.
  46. Wieland, M., Hirmer, P., Steimle, F., Gröger, C., Mitschang, B., Rehder, E., ... & Bauernhansl, T. (2016). Towards a rule-based manufacturing integration assistant. Procedia CIRP, 57, 213-218.