Abstract: A business enterprise is assessed by a consultant using a computer implemented tool for practicing component business modeling techniques. The tool builds a map of components, filters the map to form a heat map and identifies collaborations. Key performance indicators are evaluated.

Abstract: Equipment failures are diagnosed using an integrated approach of case-based reasoning (CBR) and statistical reliability analysis. The method outputs a single list of suggested failed components, ranked by an overall probability of failure, and their associated past solutions from the case base. The overall probability of failure is calculated using the combined logic of case based reasoning and statistical reliability analysis. The method is typically used in a real-time decision support system to aid equipment diagnosis by a maintenance technician working in the field.

Abstract: Equipment failures are diagnosed using an integrated approach of case-based reasoning (CBR) and statistical reliability analysis. The method outputs a single list of suggested failed components, ranked by an overall probability of failure, and their associated past solutions from the case base. The overall probability of failure is calculated using the combined logic of case based reasoning and statistical reliability analysis. The method is typically used in a real-time decision support system to aid equipment diagnosis by a maintenance technician working in the field.

Abstract: A computer-implemented method determines current beliefs and/or behaviors of a population, and forecasts the behaviors and/or beliefs which this population is likely to have in the future. The method involves selecting a subset of members from a target population, obtaining survey responses from the members in the subset, generating point estimates of at least one population parameter, generating confidence bounds for the point estimates, and conducting a trend analysis on the survey responses and the point estimates of the at least one population parameter. Future behavior, beliefs, or other attributes of the population is then determined based on the trend analysis.

Abstract: Method and apparatus suitable for demand forecasting. The invention can enable sales forecasting “by item, by size, by location”. The invention features combining a demand profile and a demand model into a single encompassing model which is capable of projecting demand for an identified set of merchandise.

Abstract: A computer system provides one or more lists of product combinations to one or more shoppers over one or more networks. The computer system comprises one or more central processing units (CPUs), one or more memories, and one or more network interfaces to one or more networks. An electronic shopping assistant process implemented on the computer system receives one or more inputs of constraints from shoppers, sends out the inputs to one or more shopping list generating processes in one or more stores, receives one or more lists of recommended product combinations that satisfy the given constraints, and allows shoppers to view and select one or more product combinations from received lists.

Abstract: A computer method for providing optimization for design processes for situations wherein there is defined a functional form y=f(x,b), where x comprises a set of independent controllable variables x={x1, . . . xn}, b comprises a set of functional parameters b={b1, . . . bm}, and y comprises a dependent uncontrollable design variable, f(x,b) subject to constraints on the dependent uncontrollable design variable y. The method comprises the steps of converting the constraints on y to constraints on b by using a functional estimate of y and its design variables (parameters) b; optimizing the function f(x,b) subject to the converted constraints on its design variables (parameters) b; and generating from step (ii) a set of optimized values of b which can optimize the dependent design variable y.

Abstract: A computer method for providing optimization for business processes for situations wherein there is defined a functional form y=f(x,b), where x comprises a set of independent controllable variables x={x1, . . . xn}, b comprises a set of functional parameters b={b1, . . . bm}, and y comprises a dependent uncontrollable business variable, f(x,b) subject to constraints on the dependent uncontrollable business variable y. The method comprises the steps of converting the constraints on y to constraints on b by using a functional estimate of y and its business variables (parameters) b; optimizing the function f(x,b) subject to the converted constraints on its business variables (parameters) b; and generating from step (ii) a set of optimized values of b which can optimize the dependent business variable y.

Abstract: A computer method for providing optimization for manufacturing processes for situations wherein there is defined a functional form y=f(x,b), where x comprises a set of independent controllable variables x={x1, . . . xn}, b comprises a set of functional parameters b={b1, . . . bm}, and y comprises a dependent uncontrollable manufacturing variable, f(x,b) subject to constraints on the dependent uncontrollable manufacturing variable y. The method comprises the steps of converting the constraints on y to constraints on b by using a functional estimate of y and its manufacturing variables (parameters) b; optimizing the function f(x,b) subject to the converted constraints on its manufacturing variables (parameters) b; and generating from step (ii) a set of optimized values of b which can optimize the dependent manufacturing variable y.

Abstract: A computer method improves the robustness of population parameters estimated using sample responses. Two different factors that may impact the accuracy of estimates are considered. The first factor is referred to as statistical outliers. By statistical outliers, what is meant is observations that fall “statistically outside” of the other remaining observations in the sample. The second factor considered is the impact of the weight assigned to each observation on the overall parameter estimate. More specifically, the fact that weights assigned to each respondent are typically estimated and thus not exact is addressed. Consequently, the weights do not unduly influence the value of the parameter estimates.

Abstract: A computer method for providing optimization for manufacturing processes for situations wherein there is defined a functional form y=f(x,b), where x comprises a set of independent controllable variables x={x1, . . . xn}, b comprises a set of functional parameters b={b1,. . . bm}, and y comprises a dependent uncontrollable manufacturing variable, f(x,b) subject to constraints on the dependent uncontrollable manufacturing variable y. The method comprises the steps of converting the constraints on y to constraints on b by using a functional estimate of y and its manufacturing variables (parameters) b; optimizing the function f(x,b) subject to the converted constraints on its manufacturing variables (parameters) b; and generating from step (ii) a set of optimized values of b which can optimize the dependent manufacturing variable y.

Abstract: A computer method improves the robustness of population parameters estimated using sample responses. Two different factors that may impact the accuracy of estimates are considered. The first factor is referred to as statistical outliers. By statistical outliers, what is meant is observations that fall “statistically outside” of the other remaining observations in the sample. The second factor considered is the impact of the weight assigned to each observation on the overall parameter estimate. More specifically, the fact that weights assigned to each respondent are typically estimated and thus not exact is addressed. Consequently, the weights do not unduly influence the value of the parameter estimates.

Abstract: A computer-implemented method determines current beliefs and/or behaviors of a population, and forecasts the behaviors and/or beliefs which this population is likely to have in the future. The method involves selecting a subset of members from a target population, obtaining survey responses from the members in the subset, generating point estimates of at least one population parameter, generating confidence bounds for the point estimates, and conducting a trend analysis on the survey responses and the point estimates of the at least one population parameter. Future behavior, beliefs, or other attributes of the population is then determined based on the trend analysis.

Abstract: A computer method for providing optimization for design processes for situations wherein there is defined a functional form y=f(x,b), where x comprises a set of independent controllable variables x={x1, . . . xn}, b comprises a set of functional parameters b={b1, . . . bm}, and y comprises a dependent uncontrollable design variable, f(x,b) subject to constraints on the dependent uncontrollable design variable y. The method comprises the steps of converting the constraints on y to constraints on b by using a functional estimate of y and its design variables (parameters) b; optimizing the function f(x,b) subject to the converted constraints on its design variables (parameters) b; and generating from step (ii) a set of optimized values of b which can optimize the dependent design variable y.

Abstract: A method and system for grouping multiple data points, each data point being a set (e.g., a vector, a tuple, etc.) including a measured dependent value and at least one related independent variable value, include fitting the data into a model relating the independent and dependent variables of the data, and calculating similarity and distance between the data points and groups of the data points, thereby to group the multiple data points.

Abstract: Sensitivity information about the average back-ordered sales and inventory levels of a back-order (R,s,S) inventory system is generated with respect to the two decision variables s and S. This information is used for what-if analysis and optimization of the decision variables s and S. The computer implemented algorithm provides sensitivity of both average inventory levels and average back orders with respect to changes in s and q (where q=S-s), for a back-order inventory simulation for any specified demand distribution. Further, the sensitivity information provided by the algorithm is exact when compared to a finite-difference simulation for the case of both discrete and continuous, stationary and non-stationary demands.

Abstract: Sensitivity information about average lost sales and inventory levels of a lost-sale (s,S) inventory system with respect to the two decision variables s and S is used for "what-if" analysis and optimization of the decision variables. Computer software implements an algorithm that provides sensitivity of average inventory levels as well as average lost sales with respect to changes in s and q(q=S-s) for a lost-sale inventory simulation for any specified demand distribution. The sensitivity information provided is exact when compared to a finite-difference simulation for the case of both discrete and continuous, stationary and non-stationary demands. A finite difference estimate of the sensitivity is obtained by running two simulations where the parameter in question (s or S) differ by a chosen small number, and then calculating the resulting difference in the performance measure of interest, in this case average inventory and lost sales.