Abstract: A computer-implemented method prepares data for modeling. The method comprises storing data from customer sales transactions in a database and retrieving a dataset of the data from the database. The dataset may include promotion and merchandizing entries. The method includes cleansing the dataset to remove erroneous and anomalous entries. Cleansing the dataset may include determining a threshold value from the dataset and determining whether a value of the dataset exceeds the threshold value, and determining an out-of-stock status for a product from the dataset. The method includes aggregating the dataset over a plurality of dimensions of the transactional space including store, product, and time dimensions, and analyzing the dataset following the cleansing and aggregating steps within a model to predict attributes of subsequent sales transactions.

Abstract: A computer-implemented method prepares data for modeling. The method comprises storing data from customer sales transactions in a database and retrieving a dataset of the data from the database. The dataset may include promotion and merchandizing entries. The method includes cleansing the dataset to remove erroneous and anomalous entries. Cleansing the dataset may include determining a threshold value from the dataset and determining whether a value of the dataset exceeds the threshold value, and determining an out-of-stock status for a product from the dataset. The method includes aggregating the dataset over a plurality of dimensions of the transactional space including store, product, and time dimensions, and analyzing the dataset following the cleansing and aggregating steps within a model to predict attributes of subsequent sales transactions.

Abstract: A method and apparatus for iterative price zone analysis includes steps of generating auxiliary data for a geographic region that includes a plurality of retail locations, the regions being dividable into a plurality of geographic zones. The method and apparatus further includes modeling of sales information for the retail locations and thereby generate demand model information based on the set zones. The method and apparatus further includes providing the demand model information and the retail locations having the demand model information associated therewith to a price zoning display of the geographic region. Additionally the auxiliary data is provided for display relative to the model information and retail locations, where the display allows for the iterative selection of various factors and recalculations of the modeling information on the visual display.

Abstract: A computer-implemented method involves modeling of product decisions in a retail store. The product decision variables are profit, assortment, placement, promotion, and inventory. Various rules and constraints such as facing elasticity, shelf replenishment costs, shelf space, carrying costs, facing capacity, slotting fees, and cannibalization are defined for multiple product decision variables. An objective function utilizes the rules and constraints for the multiple product decision variables. Other product parameters are organized into a hierarchal structure. A function is defined for each product parameter and a control variable is selected to control each product parameter function. The hierarchical structure can use brand hierarchy, enterprise hierarchy, or customer buying decisions. The objective function simultaneously models each of the multiple product decision variables by iteratively resolving the objective function into values which optimize sales, revenue, and profit for the retail store.

Abstract: A computer-implemented method involves modeling of product decisions in a retail store. The product decision variables are profit, assortment, placement, promotion, and inventory. Various rules and constraints such as facing elasticity, shelf replenishment costs, shelf space, carrying costs, facing capacity, slotting fees, and cannibalization are defined for multiple product decision variables. An objective function utilizes the rules and constraints for the multiple product decision variables. The objective function model is resolved by uses nested loops to solve for a first variable, and then using the first variable to solve for a second variable. Each decision variable in the objective function is controllable by externally determined multipliers. The objective function simultaneously models each of the multiple product decision variables by iteratively resolving the objective function into values which optimize sales, revenue, and profit for the retail store. The model is output in graphic format.

Abstract: In a planning model, a decision variable optimization process (200) generates a planning function (122) describing the planning model, the planning function (122) depending upon a set of decision variables (125). The planning function (122) is separated into independent planning functions, SPi, each of which depend upon different decision variables (125). Each of the independent planning functions, SPi, is independently optimized to obtain decisions for the different decision variables (125), and an outcome is presented that indicates the decisions. The planning function (122) further includes an embedded constraint function that introduces an embedded constraint to weaken the coupling between decision variables (125) in the planning model, thereby reducing an N-dimensional optimization problem into a lower order optimization problem.

Abstract: A system and process for modeling attributes of a set of observed data. The system and process may include an initialization process providing for a likelihood function, a first order prior function; a calibration data set; a flexible interface module in which the likelihood function, the first order first order prior function, and the second order prior function are written in a programming language; a parameter bounding process in which parameters determined to be too large are interpolated or sequentially locked down; an outlier flagging process which identifies outliers using the gradient of the likelihood function; and an output process which reports information that may include outlier forces, confidence intervals, and other factors that are unique to this modeler and useful in refining the model.

Abstract: 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.

Abstract: 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.

Abstract: A computer program product is described for solving the traveling salesman problem in polynomial time. The probability distribution of the space of all paths is modeled in a configurational density distribution. A Hamiltonian is constructed specifying the costs, distance, or penalty associated with different legs of paths encompassed in the configurational density distribution. Starting at a maximum temperature where free energy dominates and the penalty function plays little role, the system is iteratively adapted to reduce the temperature in steps incrementally chosen to preserve the linear characteristic of the approximation, until a lower temperature state of reduced energy is reached in which a preferred set of paths can be identified from the configurational density distribution.

Abstract: A non-stationary time series model using a likelihood function as a function of input data, base demand parameters, and time dependent parameter. The likelihood function may represent any statistical distribution. The likelihood function uses a prior probability distribution to provide information external to the input data and is used to control the model. In one embodiment the prior is a function of adjacent time periods of the demand profile. The base demand parameters and time dependent parameter are solved using a multi-diagonal band matrix. The solution of base demand parameters and time dependent parameter involves making estimates thereof in an iterative manner until the base demand parameters and time dependent parameter each converge. A non-stationary time series model is provided from an expression using the solution of the base demand parameters and time dependent parameter. The non-stationary time series model provides a demand forecast as a function of time.