Training in simulation and data analysis
Many engineering and management problems have a “hazard” component, for example fluctuations in interest rates, variability in the time required to complete activities, potential consumer demand, the size of a part being machined according to specifications, etc. In these situations, the typical approach is to make assumptions such as using an average and then evaluating worst case scenarios. The main shortcoming of this approach is that the results are conditional on the quality and the correctness of the assumptions.
Monte-Carlo simulation adds robustness regarding assumptions to decision-making. In addition to properly evaluating the average and worst case scenarios, this technique quantifies the impact of the variability of the assumed values, as if thousands of possible scenarios were evaluated and analyzed simultaneously. Monte-Carlo simulation is essentially based on the intensive use of random numbers from statistical distributions. This training aims to provide participants with a solid working knowledge of the Monte-Carlo simulation approach: what it consists of, what are the key concepts and what are the possible applications.