Flexible Simulation for Manufacturing & Supply Chain Management

  • Agostino Bruzzone 
  • Marcello Braglia
  • c  Marco Frosolini, 
  • Marina Massei
  • Kirill Sinelshchikov, 
  • Roberto Ferrari, 
  • Luca Padellini, 
  • Marina Cardelli
  • a,d,h Simulation Team, SIM4Future, via Trento 43, 16145 Genova, Italy
  • b,c,g University of Pisa, Via Diotisalvi, 2, 56122 Pisa PI, Italy
  • Simulation Team, Genova, Italy
Cite as
Bruzzone A., Braglia M., Frosolini M., Massei M., Sinelshchikov K., Ferrari R., Padellini L., Cardelli M. (2021). Flexible Simulation for Manufacturing & Supply Chain Management. Proceedings of the 33rd European Modeling & Simulation Symposium (EMSS 2021), pp. 423-427. DOI: https://doi.org/10.46354/i3m.2021.emss.058

Abstract

Optimization of production processes is essential for competitiveness in industry and nowadays this activity can benefit from datasets combined with simulation based solutions. In this study the authors propose application of this approach to the field of shoe production chain, with particular attention to improvement of coordination among its participants.

References

  1. Ball, M. O., Ma, M., Raschid, L., & Zhao, Z. (2002). Supply chain infrastructures: system integration and information sharing. ACM Sigmod Record, 31(1), 61-66.
  2. Braglia, M., Marrazzini, L., Padellini, L., & Rinaldi, R. (2020). Managerial and Industry 4.0 solutions for fashion supply chains. Journal of Fashion Marketing and Management: An International Journal.
  3. Braglia, M., Frosolini, M., & Zammori, F. (2009). Overall equipment effectiveness of a manufacturing line (OEEML): an integrated approach to assess systems performance. Journal of Manufacturing Technology Management.
  4. Bruzzone, A., Massei, M., & Sinelshnkov, K. (2020a). Enabling strategic decisions for the industry of tomorrow. Procedia Manufacturing, 42, 548-553.
  5. Bruzzone, A., Sinelshchikov, K., Massei, M. & Schmidt, W. (2020b). Artificial intelligence to support retail sales optimization. In proceedings of 32 European Modeling & Simulation Symposium, pp. 430-434.
  6. Bruzzone, A., Massei M., Sinelshchikov K., Fadda, P., Fancello, G., Fabbrini G. & Gotelli M. (2019a). Extended reality, intelligent agents and simulation to improve efficiency, safety and security in harbors and port plants. In proceedings of 21st International Conference on Harbor, Maritime and Multimodal Logistics Modeling and Simulation, HMS 2019, pp. 88-91.
  7. Bruzzone, A., Massei., M., Sinelshchikov, K., Fabbrini, G., Gotelli, M. & Molinari, A. (2019b). Machine learning to support industrial digitalization and business transformation. In proceedings of 31st European Modeling and Simulation Symposium, EMSS 2019, pp. 390-393.
  8. Bruzzone, A., Massei, M., Agresta, M., Sinelshchikov, K. & Di Matteo, R. (2017). Agile solutions & data analytics for logistics providers based on simulation. In proceedings of Conf. on Harbor Maritime and Multimodal Logistics M&S, 2017, pp. 164-171.
  9. Bruzzone, A. & Longo, F. (2013a). An advanced modeling & simulation tool for investigating the behavior of a manufacturing system in the hazelnuts industry sector. International Journal of Food Engineering, 9(3), 241-257.
  10. Bruzzone, A., Longo, F., Nicoletti, L., Chiurco, A., & Bartolucci, C. (2013b, September). Multiple forecasting algorithms for demand forecasting in the fashion industry. In 2013 8th EUROSIM congress on modelling and simulation (pp. 421-426). IEEE.
  11. Bruzzone, A., Fadda, P., Fancello, G., Massei, M., Bocca, E., Tremori, A. & D'Errico, G. (2011). Logistics node simulator as an enabler for supply chain development: innovative portainer simulator as the assessment tool for human factors in port cranes. Simulation, 87(10), pp. 857-874.
  12. Bruzzone, A. G., Bocca, E., Pierfederici, L., & Massei, M. (2009, July). Multilevel forecasting models in manufacturing systems. In Proceedings of the 2009 Summer Computer Simulation Conference (pp. 239-245).
  13. Bruzzone, A. G., Bocca, E., Briano, E. & Poggi, S. (2007). Supply chain management and vulnerability. In Proceedings of the 2007 Summer Computer Simulation Conference, pp. 1080-1085.
  14. Caridi, M., Cigolini*, R., & De Marco, D. (2005). Improving supply-chain collaboration by linking intelligent agents to CPFR. International journal of production research, 43(20), 4191-4218.
  15. Field, F., Kirchain, R., & Roth, R. (2007). Process cost modeling: Strategic engineering and economic evaluation of materials technologies. Jom, 59(10), 21-32.
  16. Hassan, M.M., Kalamraju, S.P., Dangeti, S., Pudipeddi, S. & Williams, E.J. 2019. "Simulation Improves Efficiency and Quality in Shoe Manufacturing". In Proceedings of the 31st European Modelling and Simulation Symposium, pp. 231–236.
  17. Ivanov, D. (2018). Revealing interfaces of supply chain resilience and sustainability: a simulation study. International Journal of Production Research, 56(10), 3507-3523.
  18. Jiménez, E., Tejeda, A., Pérez, M., Blanco, J., & Martínez, E. (2012). Applicability of lean production with VSM to the Rioja wine sector. International Journal of Production Research, 50(7), 1890-1904.
  19. Koh, S. L., & Saad, S. M. (2006). Managing uncertainty in ERP-controlled manufacturing environments in SMEs. International Journal of Production Economics, 101(1), 109-127.
  20. Lamas-Rodríguez, A., Taracido-López, I. & Pernas-Álvarez, J. (2020a). Discrete Event Simulation for the Investment Analysis of an Offshore Wind Nodes Automatised Workshop.
  21. Lamas-Rodríguez, A., Pernas-Álvarez, J. & Taracido-López, I. (2020b). Discrete event simulation in the design of a jackets nodes robotized welding workshop. In Proceedings of the 2020 Summer Simulation Conference (pp. 1-12).
  22. Law, A. M., & McComas, M. G. (1987, December). Simulation of manufacturing systems. In Proceedings of the 19th conference on Winter simulation (pp. 631-643).
  23. Long, Q. (2016). A multi-methodological collaborative simulation for inter-organizational supply chain networks. Knowledge-Based Systems, 96, 84-95.
  24. Longo, F. (2011). Advances of modeling and simulation in supply chain and industry.
  25. Lorentz, H., Töyli, J., Solakivi, T., & Ojala, L. (2013). Priorities and determinants for supply chain management skills development in manufacturing firms. Supply Chain Management: An International Journal.
  26. Madenas, N., Tiwari, A., Turner, C. J., & Woodward, J. (2014). Information flow in supply chain management: A review across the product lifecycle. CIRP Journal of Manufacturing Science and Technology, 7(4), 335-346.
  27. Masood, S. (2006). Line balancing and simulation of an automated production transfer line. Assembly Automation.
  28. Paul, M., Leiber, D., Pleli, J., & Reinhart, G. (2019, September). Simulation of Reconfigurable Assembly Cells with Unity3D. In IFIP International Conference on Advances in Production Management Systems (pp. 223-230). Springer, Cham.
  29. Raychaudhuri, S. (2008, December). Introduction to monte carlo simulation. In 2008 Winter simulation conference (pp. 91-100). IEEE.
  30. Siemieniak, M. (2004, June). Working time losses in production lines with hybrid automation-case study. In Proceedings of the Fourth International Workshop on Robot Motion and Control (IEEE Cat. No. 04EX891) (pp. 293-297). IEEE.
  31. Zhou, L., Zhang, L., Ren, L., & Wang, J. (2019). Real-time scheduling of cloud manufacturing services based on dynamic data-driven simulation. IEEE Transactions on Industrial Informatics, 15(9), 5042-5051.