Abstract: In one implementation, a volatility of expected service usage is calculated along with an expected revenue. User input provides estimates of service usage such as an estimated high usage, an estimated low usage, and a swing range of percentiles for the high and low usage estimates, for each time period in a plurality of future time periods. A user also provides a schedule of prices that are based on usage rates. A model volatility is calculated based on the swing range of percentiles, the estimated high and low usage, and a cumulative density function, such as a cumulative lognormal density function, for each time period in a plurality of time periods. The expected usage and the schedule of prices, along with any discount plans, are also used to calculate an estimated revenue.

Abstract: A computer-implemented method is disclosed. The method includes producing optimized prices for products for sale.

Abstract: A method for estimation of component gating risk in manufacturing operations is disclosed. The method includes generating an altered component plan by altering a component plan for a component, computing a mean production value using the altered component plan, and computing the component gating risk using the mean production value.

Abstract: A method and apparatus for component plan analysis under uncertainty is disclosed. A representative implementation of this method begins when a planner captures assumptions about products and components in an entity called a “scenario.” A scenario is the parameterization of all the demand, financial, and operational information for a portfolio of products and components across a set of time buckets (planning periods). The planner then specifies a component plan to be analyzed. The component plan identifies the quantities of each resource that will be used or procured during each planning period. The planner then generates a request for analysis including one or more analysis parameters that will be used in order to evaluate the performance and the risks associated with the component plan and scenario. The request for analysis is submitted to an analysis engine for calculation of risk indicators. The analytical engine calculates all the performance indicators and returns the results.

Abstract: A method for forecasting the component surpluses for a target planning period is provided. To begin this method, a planner first identifies each component required to produce a product. For each component, the planner defines a planned level and an uncancelable level. The planned level for a component is the quantity at which the component is expected to be available. The uncancelable level for a component is the quantity of the component that cannot be liquidated without charge. The planner also defines a vector of connect rates for the components. After the required data has been entered, an expected surplus is computed for each component. To compute a component's expected surplus, the component is assumed to be available at its uncancelable level. The remainder of the components are assumed to be available at their respective planned levels.

Abstract: A method for forecasting the mean production (the expected production) for a target planning period is provided. To begin this method, a user, or planner chooses one or more products for which the expected production is desired. The user then enters data describing each selected product. The data entered for each product includes data describing the demand for that product as well as data describing the components required for each product. Inter-product dependencies are also entered. The expected production for each selected product for the target planning-period is expressed as a sum of multidimensional integrals involving the data entered in the previous two steps. Once formulated, the integrals are evaluated. The result of this computation is then presented to the user.