@RISK Modeling Tips
Shanghai FDA Keeps the Food
@RISK has helped the Shanghai FDA carry out exposure assessments of chemical and biological contaminates, as well as analyzing surveys of data on residents’ expenditure on various foods. Overall, the software “has played an indispensable role in the efficient development of food safety monitoring at the Administration, helping guarantee the safety of the food Shanghai residents consume,” writes Mighsheng.
Mingsheng says, “with @RISK, the Shanghai FDA has been able to eliminate its old-fashioned, backward risk assessment and evaluation methods, and find patterns even at the micro level, finding, for example, patterns and risks in chemical and biological contamination, with important implications for maintaining food safety for Shanghai residents.”
Rafael Hartke’s recent article in Oil & Gas Monitor focuses on the production curve risk of a single oil well by exploring how risk factors affect the production curve, and some common mistakes one should avoid when modeling production curves of oil projects. The article demonstrates how the use of deterministic models to forecast oil production rates can be very misleading in face of uncertainty. The correct approach to follow when modeling oil production curves in the face of uncertainty is to use a probabilistic method such as Monte Carlo simulation, which allows determining all possible production curves based on the uncertain production parameters. The curves obtained via simulation, which include expected production curve, percentiles and the cumulative production, are in fact calculated from all possible production curves obtained from the combination of the uncertain inputs modeled as probability distributions.
Civil engineering consultancy Solvĕre has developed a methodology to enable the partners involved in PPP infrastructure projects to minimize their financial risks by accounting for each element of the project that can affect its financial status and therefore profitability. To do this Solvĕre uses @RISK to estimate the performance and to forecast the potential deductions in the payment mechanism for each project. (How much and at what intervals the government pays the contractor is determined by the ‘payment mechanism’. This relates to the quality of the service provided by the private partner whose revenue is therefore dependent on its performance score and the incentive or penalty rules of the contract).
The key objective is to quantify, for various levels of probability, the economic impact of the performance criteria not being met. Solvĕre’s @RISK model takes into account the contract specifications and the resources committed by the operator to complete the project and undertake maintenance of the infrastructure, combining them in order to evaluate the expected level of performance on this base scenario.
Solvĕre believes that the highly-complex nature of PPP contracts, coupled with payment mechanisms being subject to a significant degree of uncertainty, requires in-depth analysis in terms of probability and risk. Using @RISK, it has developed a way to do this, thereby enabling informed decisions regarding the feasibility of the project and a proper risk allocation between the partners.
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Using @RISK in Evaluating Full (late stage) Compound Development in the Pharmaceutical Industry
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