Signaldemand, Inc.

Sap Ag

Sap Se

Robert D Pierce

Kenneth J Ouimet

Hau Leung Lee

Michael A Davis

Charles R Troyer

Sushil Kumar Verma

Hartwig C Huemer

A computer system performs data processing using a data organizer which parses a primary set of data into predefined sets of data components according to one or more enterprise models. The sets of data components from the data organizer are respectively stored in multiple data sources. The data components may be stored as a binary file or hierarchical file. A plurality of data processing systems execute the enterprise models. A task manager controls which enterprise model executes on each data processing system. The data processing systems access the data components from the data sources which correspond the executing enterprise model. The data components are sent over dedicated communication channels to the data processing systems. Each data processing system may have a data interface, which is configured to receive data from different sources and convert the data into a standard format.

Data management and processing system for large enterprise model and method therefor

A computer-based profit optimization model is provided that takes account of supply-side and demand-side factors in optimizing profit for an organization. The model takes into account which parts the organization uses to assemble various products. Demand curves are used to characterize the quantity of each product that will be demanded for various classes of product and classes of customer. Supply model data is used to determine which mix of products can be sold in view of parts availability. Using the demand model and supply model data, the optimization model can recommend a set of prices to use for selling the organization's products to various customers and at various shipment times. The model ensures that the organization has sufficient resources available to produce the products and enforces user-supplied business rules and other constraints.

Profit optimization system for supply chains with limited parts availability

A profit optimization system takes account of supply-side and demand-side factors in optimizing profit for an organization. The profit optimization system uses an optimization model to optimize profit in a spot market. The model takes into account which parts the organization uses to assemble various products. Demand curves are used to characterize the quantity of each product that will be demanded as a function of price on the spot market. Supply model data is used to determine which mix of products can be sold in view of parts availability. Using the demand model and supply model data, the optimization model can recommend a set of prices to use for selling the organization's products. The model ensures that the organization has sufficient resources available to produce the products and enforces user-supplied business rules and other constraints.

Spot market profit optimization system

A computer system models customer response using observable data. The observable data includes transaction, product, price, and promotion. The computer system receives data observable from customer responses. A set of factors including customer traffic within a store, selecting a product, and quantity of selected product is defined as expected values, each in terms of a set of parameters related to customer buying decision. A likelihood function is defined for each of the set of factors. The parameters are solved using the observable data and associated likelihood function. The customer response model is time series of unit sales defined by a product combination of the expected value of customer traffic and the expected value of selecting a product and the expected value of quantity of selected product. A linear relationship is given between different products which includes a constant of proportionality that determines affinity and cannibalization relationships between the products.

System and method for modeling customer response using data observable from customer buying decisions

A bid evaluation tool is provided for evaluating proposed bids for commodity sales contracts. The tool displays historical price data and forecasts in a graph. A user of the tool defines a date range for a proposed contract by entering starting and ending dates for the contract. The tool analyzes price data for the date range and generates a corresponding proposed bid for the date range. A bid window may be used to gather user inputs for the tool. The user can select between multiple bid pricing models for the bid evaluation tool to use when processing the price data to produce the proposed bid. Bid models for the tool include models such as a flat average model, a linear fit model, and an offset model. The user can adjust which historical data is included in the graph and can select which data set is used to generate bids.

Commodity contract bid evaluation system

System and method for modeling affinity and cannibalization in customer buying decisions

A model creation tool is provided that automatically evaluates potential econometric models and, given a set of historical data on which the model is intended to operate and other constraints, automatically identifies an optimum model. The optimum model may be an autoregressive model. The optimum model is defined by a set of optimum model parameters and optimum model coefficients. The optimum model parameters may include a model order such as an autoregressive model order, a seasonality function parameter, a trend function parameter, and a parameter that sets a tolerance that is used when performing principal components dimension reduction operations on the historical data.

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Albany, CA, United States of America

Robert D Pierce