Ein kybernetisches Modell beschaffungsinduzierter Störgrößen

Printz, Stephan; Cube, Johann Philipp von; Vossen, René; Schmitt, Robert; Jeschke, Sabina

doi:10.1007/978-3-319-42620-4_12

Cited by 1 publication

(1 citation statement)

References 32 publications

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“…A "natural" quantification of the hazard associated to a risk is the product of all the quantified consequences by the likelihood. A cybernetic model of procurement based hazards and their management is presented in (Printz et al, 2015a).…”

Section: Riskmentioning

confidence: 99%

A Discrete Event Simulation Approach for Quantifying Risks in Manufacturing Processes

Landtsheer¹,

Ospina²,

Massonet³

et al. 2016

Proceedings of 5th the International Conference on Operations Research and Enterprise Systems

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Nowadays supply chains have to face an increasing number of risks related to the globalisation, especially impacting the procurement processes. Even though tools are available to help companies in addressing those risks, most companies, even larger ones, still have problems to adequately quantify the risks and assess to what extend an alternative could address them. The aim of our work is to provide companies with a software supported methodology to quantify such risks and elaborate adequate risk mitigation strategies at an optimal cost. Based on a survey conducted about the risk management practices and needs within companies, we developed a tool that enables a constant focus on risks by enabling the easy expression of key risks together with the process model and hence help to focus the granularity of the model at the right level. A model-based simulator can then efficiently evaluate these risks thanks to well-known 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 which can be used to measure business risks based on simulation results. We demonstrate the expressiveness and performance of our approach by benchmarking it on a set of cases originating from the industry and covering a large set of risk categories.

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Section: Riskmentioning

confidence: 99%