Monetary Quantification of Supply Risks of Manufacturing Enterprises - Discrete Event Simulation Based Approach

被引:1
|
作者
von Cube, Philipp [1 ]
Haertel, Lasse [1 ]
Schmitt, Robert [1 ,2 ]
Ponsard, Christophe [3 ]
Massonet, Philippe [3 ]
De landtsheer, Renaud [3 ]
Ospina, Gustavo
Printz, Stephan [4 ]
Jeschke, Sabina [4 ]
机构
[1] Fraunhofer IPT, Steinbachstr 17, D-52074 Aachen, Germany
[2] Rhein Westfal TH Aachen, Lab Machine Tools & Prod Engn WZL, Steinbachstr 19, D-52074 Aachen, Germany
[3] CETIC Res Ctr, Rue Jean Mermoz 28, B-6041 Gosselies, Belgium
[4] Inst Management Cybernet IfU, Dennewartstr 25, D-52068 Aachen, Germany
来源
FACTORIES OF THE FUTURE IN THE DIGITAL ENVIRONMENT | 2016年 / 57卷
关键词
monetary risk quantification; discrete event simulation; monte carlo simulation; use-cases; LEVEL; MODEL;
D O I
10.1016/j.procir.2016.11.029
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Various approaches exist to quantify risks in supply chains. However, two aspects in risk assessments are not usually considered: monetarized risk quantification and use-case dependent model complexity. Monetarily quantifying risks means quantifying root-cause and severity of each single risk and aggregating these risks into an aggregated risk value. Thereby information uncertainty, complex interrelations and dynamic influences need to be considered. Depending on a use-case's goal information or process models need to be created at different levels of detail. This paper presents a Discrete Event Simulation (DES) approach providing all necessary features to monetarily quantify risks independent of the depth of information and thus allow adjusting the model dependent on the use-case. It provides graphical modeling language equipped with risk assessment probes enabling to capture all risk-relevant aspects. Based on this instrumented model, the framework is then able to compute and report about monetary risk quantification using an efficient DES engine driven by a Monte-Carlo procedure. Within this paper applicability of such an approach shall be assessed in use-case specific processes characterized by determined risks and parameter settings. (C) 2016 The Authors. Published by Elsevier B.V.
引用
收藏
页码:164 / 170
页数:7
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