ANYLOGIC CONFERENCE 2016 SOFTWARE
ANYLOGIC CONFERENCE 2016 SIMULATOR

on Production Research Americas' 2014, pages 35-40, Lima, Peru. A monetary approach of risk management in procurement. P., Abbas, B., Schmitt, R., and Jeschke, S. Risikokostenrechnung: Erfolgreiche Informationsversorgung und Risikoprävention. In 2009 Winter Simulation Conference, Austin, USA. Quantifying supply chain disruption risk using Monte Carlo and discrete-event simulation. In Romeike, F., editor, Erfolgsfaktor RisikoManagement, pages 236-243.

Der prozess der risikosteuerung und - kontrolle. Management of procurement risks on manufacturing processes - survey results.

Printz, S., von Cube, P., and Ponsard, C.

In Exploring Cybernetics - Kybernetik im interdisziplinren Diskurs. Ein kybernetisches modell beschaffungsinduzierter störgößen. P., Vossen, R., Schmitt, R., and Jeschke, S.

In 20th European Conference on Modelling and Simulation (ECMS 2006), Bonn, Germany. Simulationbased risk measurement in supply chains. In Journal of Property Investment & Finance, volume 30(6), pages 612-630. Quantitative methods for risk management in the real estate development industry. In 2010 Winter Simulation Conference, Baltimore, USA. Modeling and simulating supply chain schedule risk. In 2005 Winter Simulation Conference, Orlando, USA. Risk management in supply networks using Monte-Carlo simulation. Modeling and Simulation of Discrete-Event Systems. Discrete-Event Simulation and Systems Dynamics for Management Decision Making.

Brailsford, S., Churilov, L., and Dangerfield, B.

Probability Distributions in Risk Management Operations. In Proceedings of MReBA'15, Stockholm, Sweden. Goaloriented requirement engineering support for business continuity planning. AnyLogic Multimethod Simulation Software. and Meyr, H., editors, Supply Chain Planning. Simulation and optimization of supply chains: alternative or complementary approaches? In Günther, H.

Almeder, C., Preusser, M., and Hartl, R.

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. 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.

ANYLOGIC CONFERENCE 2016 SIMULATOR

A model-based simulator can then efficiently evaluate these risks thanks to well-known Monte-Carlo simulation techniques. 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.

ANYLOGIC CONFERENCE 2016 SOFTWARE

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. 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. Nowadays supply chains have to face an increasing number of risks related to the globalisation, especially impacting the procurement processes. Renaud De Landtsheer, Gustavo Ospina, Philippe Massonet, Christophe Ponsard, Stephan Printz, Lasse Härtel, Johann Philipp von Cube 2016 Abstract SCITEPRESS - SCIENCE AND TECHNOLOGY PUBLICATIONS A Discrete Event Simulation Approach for Quantifying Risks in Manufacturing Processes

Anylogic conference 2016