Cranfield University Uses @RISK from Palisade
Cranfield University's School of Applied Sciences used @RISK software from Palisade during a research project to encourage business managers in the oil and gas industry to invest in the long-term reliability of production equipment. The assignment illustrated that time and resources spent early on in the product design lifecycle can significantly increase return on investment (ROI).
Subsea oil and gas production equipment can be required to function in excess of 2000m below sea level. The failure of any product or product component, has huge ramifications on cost and oil and gas production. @RISK is used to calculate the likelihood of this equipment failing and the overall cost throughout the project life cycle, should it do so.
For example, the cost of a support vessel can exceed $200,000 per day. Depending on the severity of the malfunction and the length of time it takes to restore a system to working order, the total cost of intervention can often be more than $10,000,000. In addition, for the duration of time that the equipment is out of service, oil or gas isn't being produced, which is also a substantial opportunity cost.
@RISK combines the two core risk analysis engineering techniques – RAM (Reliability, Availability, Maintainability) and LCC (Lifecycle Cost Analysis) inputs – as defined by Cranfield project managers into a single model, and then runs thousands of simulations to show a distribution of all possible installation performance outcomes, the probability of each outcome occurring, and the lifecycle cost implications for each performance outcome. It does this by using Monte Carlo simulation, which shows all potential scenarios, as well as the likelihood that each will occur, thereby providing decision-makers with the most complete picture.
In addition, the risk calculations are enhanced by @RISK's sensitivity analysis feature. This enables the project managers to take into account a particular component in the overall system that is most sensitive in terms of reliability, but may not be the most sensitive when it comes to cost.
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