Assessment of Risks in Manufacturing Using Discrete-Event Simulation

Renaud De Landtsheer (), Gustavo Ospina, Philippe Massonet, Christophe Ponsard, Stephan Printz, Sabina Jeschke, Lasse Härtel, Johann Philipp von Cube and Robert Schmitt
Additional contact information
Renaud De Landtsheer: CETIC Research Centre
Gustavo Ospina: CETIC Research Centre
Philippe Massonet: CETIC Research Centre
Christophe Ponsard: CETIC Research Centre
Stephan Printz: IMA/ZLW & IfU, RWTH Aachen University
Sabina Jeschke: IMA/ZLW & IfU, RWTH Aachen University
Lasse Härtel: Fraunhofer Institute for Production Technology (IPT)
Johann Philipp von Cube: Fraunhofer Institute for Production Technology (IPT)
Robert Schmitt: Fraunhofer Institute for Production Technology (IPT)

A chapter in Automation, Communication and Cybernetics in Science and Engineering 2015/2016, 2016, pp 869-891 from Springer

Abstract: 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 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 techniques. 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.

Keywords: Discrete Event Simulation; Manufacturing; Supply Chain; Procurement Risks; Risk Management (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-42620-4_66

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DOI: 10.1007/978-3-319-42620-4_66

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