Evaluation of risk supply chain models using stochastic simulation models
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Enterprise’ main goal is profit maximisation and shareholders benefit. During the past years, the evolution of technologies has led to the introduction of information systems. Consolidation of geographical limits drives the change in the internal, but also in the wider operational environment of the firm. The dynamic environment defines the culture and risk appetite of every organization in it. More specifically firm are turning their preference to dynamics methods that can constantly monitor the market and predict uncertainty. The adoption of the appropriate conditions drives to equilibrium, balancing performance and risk exposure. A substantial contributor to this balance constitutes a portfolio of information technologies such as simulation platforms that in contrast with analytical techniques, face risks in total without assumptions. This Thesis initially will present the theory of risk and simulation and furthermore to present actual data produced by simulation models. While Deterministic theory is based on data consistency, the stochastic behavior of demand is reflecting uncertainty and randomness that are hidden to data. Uncertainty in calculations is associated with variation in predictions. Modern simulation models, using heavy mathematical procedures, are adding the dimension of time in calculation. The importance of simulation is reflected to the ability, that firms gain to predict future conditions without exposing themselves to actual risks. The paper is divided into two thematic sections. The first section (Chapter 1-3), present the basic theory concerning risk and supply chain, while the second section (Chapter 4-7), presents the efficiency of stochastic simulation models, in risk estimation. Key contributors to this effort are the custom models, designed by Arena Simulator Platform.