Resources
This model is of a manufacturer that needs to reduce the number of defective units produced. It uses @RISK to pinpoint the manufacturing stage that is the worst offender. It also obtains key process capability metrics for each stage, as well as for the entire process, that will help to improve quality and reduce waste. Given historical data, it also uses @RISK's distribution fitting feature to define distribution functions describing the number of defective parts at each stage of the process.
This model is used to calculate the percentage of defective products. Each product component is nondefective if some property of its finished state lies within defined tolerance limits. The project component cells are designated as @RISK outputs with RiskSixSigma property functions defining LSL, USL, and Target value. In this way, you can see graphs of the components' quality and calculate Six Sigma statistics on each component.
This is an extension of the DMAIC (Define Measure, Analyze, Improve, Control) failure model for use in quality control and planning. It includes the use of RiskTheo functions (in this case RiskTheoXtoP) for determining the failure rate without actually running a simulation. The model also illustrates @RISK outputs with RiskSixSigma property functions defining LSL, USL, and Target values for each component.
This model explores an incentive to increase customer loyalty in a market such as the cell phone market. Each year, each of our customers remains with us with a given probability and each of the competitors' customers switches to us with another given probability. The question is whether it is worth our while to incentivize their customers to switch to us. The model assumes a one-time monetary incentive when they make such a switch.
This model is for a company that mails its catalog to a customer every quarter. For each customer, the company keeps track of the recency (the number of catalogs since the customer last purchased) and frequency (the total number of purchases so far). It costs $1 to mail a catalog. If the customer makes a purchase, the company's profit (not counting the cost of mailing the catalog) is Pert distributed with given parameters. The company keeps mailing catalogs to a customer until 24 catalogs produce no purchases, that is, until recency reaches 24.
This model illustrates one possible way oil prices might change through time, as influenced by the market. @RISK's distribution fitting tool is used to simulate future absolute price changes based on historical daily oil prices.
This model is of an oil operator who faces random oil prices and uncertainty in oil volumes over the next five years. To forecast future oil prices, @RISK's Time Series Fit tool is used to fit actual historical oil prices. Then to evaluate a hedge against decreases in oil prices, a "base" model is compared to a model with oil swaps.
This model simulates risks in a network of oil pipelines. There are nine types of risks, and there are nine routes in the pipeline network. Each route has three characteristics: diameter, mean pressure, and distance. For each type of risk and each route, two quantities are simulated: the number of events where that risk type occurs and the typical magnitude of such a risk. These are then accumulated to find the severities of the risk types, by route and total over all routes.
This model is based on an actual Palisade consulting experience. It shows the logic implemented for modeling oil transportation considering severe weather interrupts shipments. For example, if a shipment would normally take 5 days, but severe weather occurs on 2 of these days, the actual shipment time is increased to 7 days. Also, ship loading is possible only on days when there is no severe weather.
This model examines the familiar production forecasting model for oil and gas wells using the exponential decline curve.
This model forecasts production, revenues, and present value based on exponential decline.
This model analyzes a waterflood project, where recoverable oil must be estimated and one of four production schedules is used to generate a revenue stream. The model combines volumetric estimates, prices, costs, and production scheduling. The overall objective is to estimate the internal rate of return (IRR) for the project over an 18 year horizon, given information about initial costs, operating costs, reservoir description, production schedules, prices, working interest, and taxes.
