MANAGING RISK WITH THE USE OF COMPUTER SIMULATION

Authors

DOI:

https://doi.org/10.12955/peb.v2.250

Keywords:

computer simulation, risks, industrial engineering, Tecnomatix Plant Simulation, Experiment Manager

Abstract

Internal and external risk management has become an important issue in today's global business environment, which is fraught with health, natural, political, economic and technical threats. This article deals with the design of a methodology for problem-solving and risk management in connection with computer simulation. The risk management methodology proposed by us consists of individual steps, which are summarized into three stages - risk assessment, risk analysis and risk management. The proposed computer simulation methodology consists of several steps, for example creating a parametric simulation model, designing experiments, analysis of the simulation model results or the evaluation of the simulation results. These steps are described in the article. After completing the previous steps, we describe the points of an action plan and what it must contain to avoid consequences and the impact of risks at the lowest possible level. An example of the use of computer simulation is the risk situation associated with the fluctuation of employees. In the end, the proposed methodology is supported by the results of our research and its further direction.

References

Bangsow, S. (2020). Tecnomatix Plant Simulation Modeling and Programming by Means of Examples (2 ed.). Springer International Publishing. doi:doi: 10.1007/978-3-030-41544-0

Bonfill, A., Bagajewicz, M., Espuña, A., & Puigjaner, L. (2004, January 8). Industrial & Engineering Chemistry Research. Risk Management in the Scheduling of Batch Plants under Uncertain Market Demand, 741-750. doi:doi.org/10.1021/ie030529f

Bubeník, P., Horák, F. (2014). Proactive approach to manufacturing planning. Quality Innovation Prosperity, 23-32.

Ďurica, L., Gregor, M., Vavrík, V., Marschall, M., Grznár, p., & M. Š. (2019, October). A Route Planner Using a Delegate Multi-Agent System for a. Applied Sciences. doi.org/10.3390/app9214515

Geiger, F., & Reinhart, G. (2016, April). Knowledge-based machine scheduling under consideration of uncertainties in master data. Production Engineering, 197–207. doi: 10.1007/s11740-015-0652-5

Ghadge, A., Dani, S., & Kalawsky, R. (2012, November 2). Supply chain risk management: present and future scope. The International Journal of Logistics Management, 313-339.

Giannakis, M., & Louis, M. (2011, March). A multi-agent based framework for supply chain risk management. Journal of Purchasing and Supply Management, 23-31. doi.org/10.1016/j.pursup.2010.05.001

Grznár, P., Gregor, M., Martin, K., Štefan, M., Marek, S., Vladimír, V., . . . Tomáš, B. (2020, June 29). Modeling and Simulation of Processes in a Factory of the Future. Applied Sciences. doi.org/10.3390/app10134503

Hahn, E., Doh, P. J., & Bunyaratavej, K. (2009, September). The Evolution of Risk in Information Systems Offshoring: The Impact of Home Country Risk, Firm Learning, and Competitive Dynamics. MIS Quarterly, 33(3), 597-616. doi: 10.2307/20650312

Hamzeh, R. F. (2009). Improving Construction Workflow-The Role of Production Planning and Control. Berkeley: University of California.

Hu, Z., & Hu, G. (2016, October). A two-stage stochastic programming model for lot-sizing and scheduling. International Journal of Production Economics, 198-207. doi: 10.1016/j.ijpe.2016.07.027

Chen, C. C., Law, C., & Yang, C. S. (2009, March). Managing ERP implementation failure: A project management perspective. IEEE Transactions on Engineering Management . doi: 10.1109/TEM.2008.2009802

Jans, M., Lybaert, N., & Vonhoof, K. (2010, March). Internal fraud risk reduction: Results of a data mining case study. International Journal of Accounting Information Systems, 17-41. doi: 10.1016/j.accinf.2009.12.004

Klöber-Koch, J., Braunreuther, S., & Reinhart, G. (2017). Predictive production planning considering the operative risk in a manufacturing system. Manufacturing Systems 4.0 – Proceedings of the 50th CIRP Conference on Manufacturing Systems (pp. 360-365). Taichung City: Curran Associates, Inc. doi:0.1016/j.procir.2017.03.118

Krajčovič, M., & Plinta, D. (2012, October). Comprehensive approach to the inventory control system improvement. Management and production engineering review, 3(3), 34-44. doi: 10.2478/v10270-012-0022-0

Kužma, D., Korba, P., Hovanec, M., & Dulina, Ľ. (2016). The Use of CAX Systems as a Tool for Modeling Construction Element in the . NAŠE MORE : znanstveni časopis za more i pomorstvo, 134-139. doi: 10.17818/nm/2016/si11

Müller, S. (2008). Methodik für die entwicklungsund planungsbegleitende Generierung. München: Herbert Utz Verlag GmbH.

Nateghi, R., Guikema, D. S., & Quiring, M. S. (2011, December). Comparison and validation of statistical methods for predicting power outage durations in the event of hurricanes. Risk Analysis: An International Journal, 1897–1906. doi: 10.1111/j.1539-6924.2011.01618.x

Pekarčíková, M., Trebuňa, P., Kliment, M., & Rosocha, L. (2020). Material flow optimization through e-kanban system simulation. International Journal of Simulation Modelling , 12.

Petrov́ić, D., & Duenas, A. (2006, August 16). A fuzzy logic based production scheduling/rescheduling in the presence of uncertain disruptions. Fuzzy Sets and Systems, 2273–2285.

Popp, A. (2015). Stochastische dynamische. Ingolstadt: Katholische Universität Eichstätt-Ingolstadt.

Raghavan, S. N., & Mishra, V. K. (2011). Short-term financing in a cash-constrained supply chain. International Journal of Production Economics, 407-412.

Shamsuzzoha, A., Rintala, S., Cunha, P., Ferreira, P., Kankaanpää, ‪., & Carneiro, M. L. (2012, December 17). Event Monitoring and Management Process in a Non‐Hierarchical Business Network. Intelligent Non‐hierarchical Manufacturing Networks, 349-374. doi.org/10.1002/9781118607077.ch16‬‬‬‬‬‬‬‬‬‬‬‬

Shiri, M. M., Amini, M. T., & Raftar, B. M. (2012, April). Data mining techniques and predicting corporate financial distress. Interdisciplinary journal of contemporary research in business, 61-68.

Smeureanu, I., Ruxanda, G., Diosteanu, A., Delcea, C., & Cotfas, A. L. (2012). Intelligent agents and risk based model for supply chain managment. Technological and economic development of economy, 452-469.

Steinmetz, M. (2007). Risikosituation und -handhabung in der Produktion: ein Konzept zur Verbesserung der Risikosituation. München: TCW Transfer-Centrum.

Vavrík, V., Gregor, M., Grznár, P., Mozol, Š., Schickerle, M., Ďurica, L., . . . Bielik, T. (n.d.). Design of Manufacturing Lines Using the Reconfigurability Principle. Mathematics. doi:10.3390/math8081227

Wang, X., Li, D., O'brien, C., & Li, Y. (2010). A production planning model to reduce risk and improve operations management. International Journal of Production Economics, 463-474.

Weig, S. (2008). Konzept eines integrierten Risikomanagements für die Ablaufund Strukturgestaltung in Fabrikplanungsprojekten. München: Herbert Utz Verlag GmbH.

Wu, D. D., & Olson, L. D. (2013, November). Computational simulation and risk analysis: An introduction of state of the art research Preface. Mathematical and Computer Modelling, 1581–1587. doi: 10.1016/j.mcm.2013.07.004

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Published

2021-10-24

How to Cite

Bučková, M. ., Fusko, M. ., Gabajová, G. ., Gašo, M. ., Mičieta, B. ., & Martinkovič , M. . (2021). MANAGING RISK WITH THE USE OF COMPUTER SIMULATION. Proceedings of CBU in Economics and Business, 2, 17-23. https://doi.org/10.12955/peb.v2.250
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