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Authors: Renaud De Landtsheer 1 ; Gustavo Ospina 1 ; Philippe Massonet 1 ; Christophe Ponsard 1 ; Stephan Printz 2 ; Lasse Härtel 3 and Johann Philipp von Cube 3

Affiliations: 1 CETIC Research Centre, Belgium ; 2 RWTH Aachen University, Germany ; 3 Fraunhofer Institute for Production Technology (IPT), Germany

Keyword(s): Discrete Event Simulation, Manufacturing, Supply Chain, Procurement Risks, Risk Management.

Related Ontology Subjects/Areas/Topics: Agents ; Applications ; Artificial Intelligence ; Bioinformatics ; Biomedical Engineering ; Business Analytics ; Cardiovascular Technologies ; Computing and Telecommunications in Cardiology ; Data Engineering ; Decision Support Systems ; Decision Support Systems, Remote Data Analysis ; Enterprise Information Systems ; Health Engineering and Technology Applications ; Information Systems Analysis and Specification ; Knowledge-Based Systems ; Methodologies and Technologies ; Operational Research ; Risk Management ; Simulation ; Software Engineering ; Software Project Management ; Supply Chain Management ; Symbolic Systems

Abstract: 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. (More)

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Paper citation in several formats:
Landtsheer, R.; Ospina, G.; Massonet, P.; Ponsard, C.; Printz, S.; Härtel, L. and Cube, J. (2016). A Discrete Event Simulation Approach for Quantifying Risks in Manufacturing Processes. In Proceedings of 5th the International Conference on Operations Research and Enterprise Systems - ICORES; ISBN 978-989-758-171-7; ISSN 2184-4372, SciTePress, pages 313-322. DOI: 10.5220/0005752403130322

@conference{icores16,
author={Renaud De Landtsheer. and Gustavo Ospina. and Philippe Massonet. and Christophe Ponsard. and Stephan Printz. and Lasse Härtel. and Johann Philipp von Cube.},
title={A Discrete Event Simulation Approach for Quantifying Risks in Manufacturing Processes},
booktitle={Proceedings of 5th the International Conference on Operations Research and Enterprise Systems - ICORES},
year={2016},
pages={313-322},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005752403130322},
isbn={978-989-758-171-7},
issn={2184-4372},
}

TY - CONF

JO - Proceedings of 5th the International Conference on Operations Research and Enterprise Systems - ICORES
TI - A Discrete Event Simulation Approach for Quantifying Risks in Manufacturing Processes
SN - 978-989-758-171-7
IS - 2184-4372
AU - Landtsheer, R.
AU - Ospina, G.
AU - Massonet, P.
AU - Ponsard, C.
AU - Printz, S.
AU - Härtel, L.
AU - Cube, J.
PY - 2016
SP - 313
EP - 322
DO - 10.5220/0005752403130322
PB - SciTePress