Microsoft Technology Licensing, LLC

Rapt, Inc.

Balazs Kralik

Paul Dagum

Michael Goldbach

Vivek Vaidya

Pascal M Bensoussan

Adam Galper

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.

Method and apparatus for estimation of component gating and shortage risk in manufacturing operations

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. The mean production for the component is computed and used, along with the uncancelable level the selected component and the vector of connect rates to compute the component's expected surplus. This computation is repeated for all components in turn.

Method and business process for the estimation of erosion costs in assemble-to-order manufacturing operations

A representative implementation of a 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, which identifies the quantities of each resource that will be used or procured during each planning period. A request for analysis includes 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. The results are then typically stored in a database or other persistent storage system.

Method and apparatus for component plan analysis under uncertainty

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. The results are then typically stored in a database or other persistent storage system.

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.

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Berkeley, CA, United States of America

Balazs Kralik