Microsoft Technology Licensing, LLC

Rapt, Inc.

Microsoft Corporation

Business.com, Inc.

Rapt Technologies Corporation

Paul Dagum

Michael Goldbach

Balazs Kralik

Vivek Vaidya

Pascal M Bensoussan

Thomas A Chavez

Adam Galper

Leonardo Dagum

Tarandeep Singh

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 computer implemented method for executing an ANSI SQL expression belonging to the SELECT-WHERE-equi-JOIN class on data residing in a distributed file system, said method comprising the steps of entering the ANSI SQL expression into a user interface; converting the ANSI SQL expression into a map-reduce program; running the map-reduce program on the distributed file system; storing the result set of the program in the distributed file system; and presenting the result set through a user interface.

System and method for data warehousing and analytics on a distributed file system

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 provides for an efficient unconstrained and nonlinear constrained optimization solution to a multivariate allocation of resources to meet manufacturing needs for uncertain multiproduct demand, where uncertainty is captured through a multivariate normal distribution over product demand (or more generally, through a member of the elliptical family of distributions).

Method and apparatus for optimizing a multivariate allocation of resources

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.

Method and apparatus for utility pricing analysis

A method for the allocation of resources. A series of products (refinements) is arranged in a product space plan (PSP) according to connectivities between the products and associated components (resources). An expected value function is derived for the plan, and converted into a closed form expression. The PSP is transformed into a working space plan (WSP) having corresponding elements for each product. A loading step is applied to the WSP wherein each element is sequentially analyzed to determine if an associated component gates production of that product. Each element is loaded, with components being unloaded from prior elements if a subsequent element is gated. A reloading step redetermines if any elements are gated by a component that might have been unloaded from the element. If the element remains gated, the elements sharing the component are merged into a sub-plan, which can be maximized as a function of a single variable.

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.

Method and business process for the estimation of mean production for assemble-to-order manufacturing operations

A method and apparatus providing for an efficient solution to the multivariate allocation of resources are described. A model is formulated that derives the relationship between a set of resources and a set of refinements, wherein any of a number of resources are used to build or comprise a refinement. The model provides for at least: the resource consumption as based upon the relationship between each refinement and its set of supporting resources, a demand distribution of the refinements, and a value function. Each resource, and the refinements that it supports, generates a resource hyperplane in a demand space, and the complete set of refinements generates an intersecting set of hyperplanes forming a polytope on which resource allocation fulfills refinement demand. An expected value function is thereafter formulated and transformed into a closed form solution.

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Paul Dagum