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Decision-Making and Quantitative Risk Analysis
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Decision-Making and Quantitative Risk Analysis
@RISK used to Determine Viability
More specifically, @RISK is used to estimate uncertain costs, such as repair and maintenance costs of the turbines. In addition, @RISK is used to model failure rates of the turbines, based on reliability of their components.
ECN is an independent research organization based in the Netherlands that provides in-depth knowledge and technology to enable the transition to sustainable energy management.
Palisade Discusses Supply Chain
Risk in SupplyChainBrain
The expansion of global supply chains has meant an exponential growth of the risk of disruptions to those networks. Catastrophic events, such as the Japanese earthquake and Hurricane Katrina, have highlighted the need for analysis of disruption risk and development of mitigation plans to cope with them. In addition, trends such as globalization, heavy reliance on transportation and communication infrastructures, and lean manufacturing have led to an increase in the vulnerability of supply networks. Furthermore, nodes on supply chains are very often interrelated, causing these vulnerabilities to propagate rapidly.
Palisade consultant Fernando Hernandez discusses how quantitative risk analysis like the Monte Carlo simulation performed in @RISK can address these growing risks to global supply chains.
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Abu Dhabi relies almost completely on desalinated seawater for its potable water requirements. The desalination process is challenging in terms of operation, costs, and environmental impact. ADWEC must therefore forecast as accurately as possible the demand for water and electricity across the Emirate in order to plan for the optimum expansion as well as the most efficient and effective use of water production plants.
ADWEC selected @RISK from Palisade because to forecast demand for water because it offered the features required and was cost-effective. ADWEC now uses @RISK to help it forecast Abu Dhabi's water demand up until the year 2030. (This timescale is in line with the government's vision, 'Abu Dhabi Plan 2030', a framework to optimize the city's development, based on environmental, social and economic criteria).
As a result of using @RISK to assist with its forecasting, planning and management strategies, ADWEC has been able to consistently meet with almost complete accuracy and a high degree of confidence the Abu Dhabi Emirate water demand forecasts.
The output in the model is the NPV of the reserves for the first 10 years of production. Other factors considered include the decline rate, the gas-oil ratio (GOR), the prices of oil and gas, as well as the rate of increase of the prices of oil and gas. The only input factor that contains an @RISK probability distribution function is reserves, but you could make the model more realistic by using distribution functions to describe the decline rate, GOR, price of oil, etc.
Production and Economic Forecast using Exponential Decline
q = qi*exp(-at)
where qi is the annual production for the first year and a is the (fixed) annual percentage decline rate.
Uncertain input factors include:
For each year, a new sample is drawn from a new Normal distribution, modeling variation from year to year.
Operating expenses are fixed throughout the forecast. The first year expense may be thought of as capital investment. Outputs are defined as:
Like the Band.xls model, you can generate a summary graph to see the change in OilGros over time.
Finally, a SimTable function has been used in the Discount Rate input that is used to calculate Total NPV. This contains two possible values for Discount Rate – 12% and 14% - enabling you to run two back-to-back simulations to compare the effect of different discount rates on your Total NPV.
This example was taken from Decisions Involving Uncertainty: An @RISK Tutorial for the Petroleum Industry by James Murtha, published by Palisade, where a detailed, step-by-step explanation can be found.
Introduction to Risk & Decision Analysis
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