Resources
A set of basic @RISK models for modeling events:
1. Any (or all) events could occur.
2. A version illustrating the RiskMakeInput function for use in sensitivity analysis.
3. A version where dependent events can occur (or not occur) sequentially in time.
A model that illustrates one possible generic approach for generating demands for substitute products.
An inventory ordering model that illustrates how the Time Series Define feature can be used to generate demands.
A model which illustrates how the bullwhip effect can occur with a single retailer and supplier and a second model that illustrates the bullwhip effect when there are multiple tiers of suppliers.
This model of a company's multiple projects over a 12-month horizon. Each project is planned to start in a given month, and from that month on, it has anticipated costs, some of which are known (or 0) and some of which are uncertain. There are possible random delays (or, for a few projects, possible earlier starting months), which shift the cost schedule to the right (or the left). In addition, each project has a 5% chance of failing in any month after its actual starting month. If it fails in a given month, that month's costs plus any remaining months' costs are not incurred._x000D_
A model that illustrates how @RISK can be used to correlate task times in a project.
A model for determining whether the total of uncertain costs meets a budget
A model of an electrical system that illustrates @RISK's Six Sigma functions.
These examples files can be used as a template to understand the capability of a system. You will find three of them:
1. A basic @RISK model where the system functions if at least k of its n parts function.
2. A version where the values of the parts are correlated.
3. A model that illustrates @RISK's Six Sigma functions in a process control setting.
A model for building a catapult that illustrates @RISK's Six Sigma functions in the context of DOE.
A model for welding a disk onto a ring that illustrates @RISK's Six Sigma functions in the context of DOE.
A model that illustrates the use of the Taguchi quadratic loss function to measure uncertain quality.
