In this quick primer, advanced quantitative risk- based concepts will be introduced, namely, the hands-on applications of Monte Carlo simulation, real options analysis, stochastic forecasting, and portfolio optimization. These methodologies rely on existing techniques (e.g., return on investment, discounted cash flow, cost-based analysis, and so forth), and complements these traditional techniques by pushing the envelope of analytics, and not to replace them outright. It is not a complete chance of paradigm, and we are not asking the reader to throw out what has been tried and true, but to shift one’s paradigm, to move with the times, and to improve upon what has been tried and true. These new methodologies are used in helping make the best possible decisions, allocating budgets, and so forth, where the conditions surrounding these decisions are risky or uncertain. These new techniques can be used to identify, analyze, quantify, value, predict, hedge, mitigate, optimize, allocate, diversify, and manage risk. Find out how multinational like 3M, Airbus, Boeing, BP, Chevron, GE, Motorola, Pfizer, Johnson & Johnson, and many others are relying on these advanced analytical techniques.
This model illustrates how to use Risk Simulator for running a Monte Carlo simulation on a multi-phased project to forecast costs and cost overruns, simulating discrete events (success/failure and go/no-go outcomes), linking these events, and linking the input parameters in a simulation assumption to calculated cells.
Requirements: Modeling Toolkit, Risk Simulator.