A Risk Management Framework via Multi-paradigm Simulation for Supply Chain and Business Process Management

  • Shayan Mohammadi,
  • Keivan Ghasemi Nodooshan,
  • Konstantinos Mykoniatis 
  • a,b,c  Auburn University, 345 W Magnolia Ave, Auburn, AL 36849, Auburn, AL, 36849, USA
Cite as
Mohammadi S., Ghasemi Nodooshan K., Mykoniatis K. (2021). A Risk Management Framework via Multi-paradigm Simulation for Supply Chain and Business Process Management. Proceedings of the 33rd European Modeling & Simulation Symposium (EMSS 2021), pp. 295-306. DOI: https://doi.org/10.46354/i3m.2021.emss.041

Abstract

With global crises and natural disasters becoming ever more prevalent, the importance of risk management is highlighted more than ever. Furthermore, risk management is often in contrast with more classic objectives of firms such as reaching higher levels of productivity. To add to the difficulty, there are fields that are already plagued with complexity beyond the limits of traditional problem-solving methods. Supply chain and business process management are two such complex fields that will be highly influenced by outside factors beyond a survival point if risk and operations management are treated disjointedly. In the current simulation literature of risk management, discrete event and agent-based simulation methods are mostly used for supply chain and business process management, respectively. In this article, we propose a risk management framework using multi-paradigm modeling and simulation to bring operations and risk under one umbrella. The framework adopts a continuous improvement cycle, quantifies risk as a deliverable, and provides the decision-makers with trade-offs between optimized risk and other management objectives. The framework is validated through the development of a multi-paradigm simulation model for a warehouse supply chain. The case study demonstrates how our framework could be utilized by the decision makers to systematically approach risk management. 

References

  1. Alauddin, M., Khan, M. A. I., Khan, F., Imtiaz, S., Ahmed, S., & Amyotte, P. (2020). How can process safety and a risk management approach guide pandemic risk management. Journal of Loss Prevention in the Process Industries, 68, 104310.
  2. Amantea, I. A., Di Leva, A., & Sulis, E. (2018). A Simulation-driven Approach in Risk-aware Business Process Management: A Case Study in Healthcare. In SIMULTECH (pp. 98-105).
  3. Betz, S., Hickl, S., & Oberweis, A. (2011, September). Risk-aware business process modeling and simulation using XML nets. In 2011 IEEE 13th Conference on Commerce and Enterprise Computing (pp. 349-356). IEEE.
  4. Carr, M. J., Konda, S. L., Monarch, I., Ulrich, F. C., & Walker, C. F. (1993). Taxonomy-based risk identification. carnegie-mellon univ pittsburgh pa software engineering inst.
  5. Chen, X., Ong, Y. S., Tan, P. S., Zhang, N., & Li, Z. (2013, October). Agent-based modeling and simulation for supply chain risk management-a survey of the state-of-the-art. In 2013 IEEE International Conference on Systems, Man, and Cybernetics (pp. 1294-1299). IEEE.
  6. Erol, I., & Ferrell Jr, W. G. (2003). A methodology for selection problems with multiple, conflicting objectives and both qualitative and quantitative criteria. International Journal of Production Economics, 86(3), 187-199.
  7. Froot, K. A., Scharfstein, D. S., & Stein, J. C. (1993). Risk management: Coordinating corporate investment and financing policies. The Journal of Finance, 48(5), 1629-1658.
  8. Gao, Q., Guo, S., Liu, X., Manogaran, G., Chilamkurti, N., & Kadry, S. (2020). Simulation analysis of supply chain risk management system based on IoT information platform. Enterprise Information Systems, 14(9-10), 1354-1378.
  9. Jacobi, C., Hayward, C., de Zwaan, M., Kraemer, H. C., & Agras, W. S. (2004). Coming to terms with risk factors for eating disorders: application of risk terminology and suggestions for a general taxonomy. Psychological bulletin, 130(1), 19.
  10. Jansen-Vullers, M., & Netjes, M. (2006, October). Business process simulation–a tool survey. In Workshop and Tutorial on Practical Use of Coloured Petri Nets and the CPN Tools, Aarhus, Denmark (Vol. 38).
  11. Jiang, C., & Sheng, Z. (2009). Case-based reinforcement learning for dynamic inventory control in a multi-agent supply-chain system. Expert Systems with Applications, 36(3), 6520-6526.
  12. Jirong, W., Jun, L., Yunhong, Z., & Zongwu, H. (2008, September). Simulation study on influences of information sharing to supply chain inventory system based on multi-agent system. In 2008 IEEE International Conference on Automation and Logistics (pp. 1001-1004). IEEE.
  13. Lamine, E., Thabet, R., Sienou, A., Bork, D., Fontanili, F., & Pingaud, H. (2020). BPRIM: An integrated framework for business process management and risk management. Computers in Industry, 117, 103199.
  14. Li, W., & Li, C. (2008, September). Transshipment policy research of multi-location inventory system based on multi-agent system. In 2008 IEEE International Conference on Automation and Logistics (pp. 1344-1351). IEEE.
  15. Longo, F. (2011a). Supply chain management based on modeling & simulation: state of the art and application examples in inventory and warehouse management. Supply Chain Management, 93.
  16. Longo, F. (2011b). Advances of modeling and simulation in supply chain and industry.
  17. Macdonald, J. R., Zobel, C. W., Melnyk, S. A., & Griffis, S. E. (2018). Supply chain risk and resilience: theory building through structured experiments and simulation. International Journal of Production Research, 56(12), 4337-4355.
  18. Mykoniatis, K. (2015). A Generic Framework For Multi-Method Modeling and Simulation of Complex Systems Using Discrete Event, System Dynamics and Agent Based Approaches.
  19. Mykoniatis, K., & Angelopoulou, A. (2020). A modeling framework for the application of multi-paradigm simulation methods. Simulation, 96(1), 55-73.
  20. Oliveira, J. B., Jin, M., Lima, R. S., Kobza, J. E., & Montevechi, J. A. B. (2019). The role of simulation and optimization methods in supply chain risk management: Performance and review standpoints. Simulation Modelling Practice and Theory, 92, 17-44.
  21. Pruckner, M., & German, R. (2013, December). A hybrid simulation model for large-scaled electricity generation systems. In 2013 Winter Simulations Conference (WSC) (pp. 1881-1892). IEEE.
  22. Reis, A. N., Pitombeira-Neto, A. R., & Rolim, G. A. (2017). Simulation of Tank Truck Loading Operations in a Fuel Distribution Terminal. Int. J. Simul. Model, 16, 435-447.
  23. Rosemann, M., & Zur Muehlen, M. (2005). Integrating risks in business process models.
  24. Rotaru, K., Wilkin, C., Churilov, L., Neiger, D., & Ceglowski, A. (2011). Formalizing process-based risk with value-focused process engineering. Information Systems and e-Business Management, 9(4), 447-474.
  25. Safari, A. (2016). An effective practical approach for business process modeling and simulation in service industries. Knowledge and Process Management, 23(1), 31-45.
  26. Satyal, S., Weber, I., Paik, H. Y., Di Ciccio, C., & Mendling, J. (2019). Business process improvement with the AB-BPM methodology. Information Systems, 84, 283-298.
  27. Schlüter, F. F., Hetterscheid, E., & Henke, M. (2019). A simulation-based evaluation approach for digitalization scenarios in smart supply chain risk management. Journal of Industrial Engineering and Management Science, 2019(1), 179-206.
  28. Schmitt, A. J., & Singh, M. (2009, December). Quantifying supply chain disruption risk using Monte Carlo and discrete-event simulation. In Proceedings of the 2009 winter simulation conference (WSC) (pp. 1237-1248). IEEE.
  29. Tjoa, S., Jakoubi, S., Goluch, G., Kitzler, G., Goluch, S., & Quirchmayr, G. (2010). A formal approach enabling risk-aware business process modeling and simulation. IEEE Transactions on Services Computing, 4(2), 153-166.
  30. Tjoa, S., Jakoubi, S., & Quirchmayr, G. (2008, March). Enhancing business impact analysis and risk assessment applying a risk-aware business process modeling and simulation methodology. In 2008 Third International Conference on Availability, Reliability and Security (pp. 179-186). IEEE.