Abstract
Due to globalisation, supply chains face an increasing number of risks that impact the procurement process. Even though there are tools that help companies address these risks, most companies, even larger ones, still have problems for adequately quantifying the risks on their current process as well as on alternative process. The aim of our work is to provide companies with a software supported method for quantifying procurement risks and establishing adequate strategies for risk mitigation at an optimal cost. Based on the results of a survey on risk management practices and industrial needs, we developed a tool that enables them quantifying these risks. The tool makes it easier to express key risks via a process model that offers an adequate granularity for expressing them. A simulator incorporated in our tool can efficiently evaluate these risks through Monte-Carlo simulation technique. Our main technical contribution lies in the development of an efficient Discrete Event Simulation (DES) engine, together with a Query Language that can be used to measure business risks from the simulation results. We show the expressiveness and performance of our approach by benchmarking it on a set of cases that are taken from industry and cover a large set of risk categories.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Deleris, L., Erhun, F.: Risk management in supply networks using Monte-Carlo simulation. In: 2005 Winter Simulation Conference, Orlando, USA (2005)
Printz, S., von Cube, J.P., Ponsard, C.: Management of procurement risks on manufacturing processes - survey results (2015). http://simqri.com/uploads/media/Survey_Results.pdf
von Cube, J.P., Abbas, B., Schmitt, R., Jeschke, S.: A monetary approach of risk management in procurement. In: 7th International Conference on Production Research Americas’ 2014, Lima, Peru, pp. 35–40 (2014)
OscaR: OscaR: Scala in OR (2012). https://bitbucket.org/oscarlib/oscar
Romeike, F.: Der prozess der risikosteuerung und kontrolle. In: Romeike, F. (ed.) Erfolgsfaktor Risiko-Management, pp. 236–243. Gabler, Wiesbaden (2004)
Zsidisin, G.A., Ritchie, B.: Supply Chain Risk: A Handbook of Assessment, Management, and Performance. Springer, New York (2009)
Siepermann, M.: Risikokostenrechnung: Erfolgreiche Informationsversorgung und Risikoprävention. Erich Schmidt, Berlin (2008)
Sutton, I.: Process Risk and Reliability Management, 2nd edn. Elsevier (2015)
Printz, S., von Cube, J.P., Vossen, R., Schmitt, R., Jeschke, S.: Ein kybernetisches modell beschaffungsinduzierter störgößen. In: Exploring Cybernetics - Kybernetik im interdisziplinren Diskurs. Springer Spektrum (2015)
Artikis, C., Artikis, P.: Probability Distributions in Risk Management Operations. Springer, London (2015)
Zio, E.: The Monte Carlo Simulation Method for System Reliability and Risk Analysis. Springer, London (2013)
Gleißner, W.: Quantitative methods for risk management in the real estate development industry. J. Prop. Investment Financ. 30(6), 612–630 (2012)
Finke, G.R., Schmitt, A., Singh, M.: Modeling and simulating supply chain schedule risk. In: 2010 Winter Simulation Conference, Baltimore, USA (2010)
Brailsford, S., Churilov, L., Dangerfield, B.: Discrete-Event Simulation and Systems Dynamics for Management Decision Making. Wiley, Chichester (2014)
Byong-Kyu, C., Donghun, K.: Modeling and Simulation of Discrete-Event Systems. Wiley (2013)
AnyLogic: AnyLogic Multimethod Simulation Software (2015). http://www.anylogic.com
Automation, R.: Arena Simulation Software (2015). https://www.arenasimulation.com
Siemens: Plant Simulator (2015). http://goo.gl/gH63jw
Wampler, D., Payne, A.: Programming Scala. 2nd edn. O’Reilly media (2015)
Boostrap: Bootstrap website (2016). http://getbootstrap.com
The jQuery Foundation: jQuery website (2016). https://jquery.com
ClientIO: JointJS website (2016). http://jointjs.com
Scalatra: Scalatra website (2016). http://scalatra.org
Klimov, R.A., Merkuyev, Y.A.: Simulation-based risk measurement in supply chains. In: 20th European Conference on Modelling and Simulation (ECMS 2006), Bonn, Germany (2006)
Schmitt, A., Singh, M.: Quantifying supply chain disruption risk using Monte Carlo and discrete-event simulation. In: 2009 Winter Simulation Conference, Austin, USA (2009)
Almeder, C., Preusser, M., Hartl, R.F.: Simulation and optimization of supply chains: alternative or complementary approaches? In Günther, H.O., Meyr, H. (eds.) Supply Chain Planning, pp. 1–25. Springer, Heidelberg (2009)
Arenas, A.E., Massonet, P., Ponsard, C., Aziz, B.: Goal-oriented requirement engineering support for business continuity planning. In: Jeusfeld, M.A., Karlapalem, K. (eds.) ER 2015. LNCS, vol. 9382, pp. 259–269. Springer, Cham (2015). doi:10.1007/978-3-319-25747-1_26
SimQRi: Online SimQRi tool (2015). https://simqri.cetic.be
Acknowledgement
This research was conducted under the SimQRi research project (ERA-NET CORNET, Grant No. 1318172). The CORNET promotion plan of the Research Community for Management Cybernetics e.V. (IfU) has been funded by the German Federation of Industrial Research Associations (AiF), based on an enactment of the German Bundestag.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
De Landtsheer, R. et al. (2017). Assessment of Risks in Manufacturing Using Discrete-Event Simulation. In: Vitoriano, B., Parlier, G. (eds) Operations Research and Enterprise Systems. ICORES 2016. Communications in Computer and Information Science, vol 695. Springer, Cham. https://doi.org/10.1007/978-3-319-53982-9_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-53982-9_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-53981-2
Online ISBN: 978-3-319-53982-9
eBook Packages: Computer ScienceComputer Science (R0)