Abstract: A method and associated systems for automatically identifying a risk that a staffing resource identified by a proposed project-staffing plan will be unavailable. A computerized project-management tool, in response to receiving a proposed project plan, requests, receives, and aggregates information about other past, present, and expected projects and about expected supplies of, and competing demands for, staffing resources required by the proposed plan. The tool infers patterns, business rules, and trends from the received information, with which it adjusts the proposed project's planned start date, expected availabilities of required staffing resources, and expected competition for staffing resources. The tool then determines a likelihood that a sufficient supply of each of the plan's required staffing resources, as adjusted, will be available at the required times, as adjusted. If the risk is too high, the user may fine-tune the proposed plan by repeating the preceding steps with varying project parameters.

Abstract: Techniques for automated pricing of an item are provided. The techniques include obtaining historical data of one or more previous purchases for the item, performing a regression on the historical data, and using the regression to obtain a buying price and a selling price for the item.

Abstract: A method, an apparatus and an article of manufacture for detecting a fraudulent mobile money transaction. The method includes determining a reputation score for at least one customer based on historical data, determining multiple patterns related to fraudulent mobile money transaction activities by using the reputation score and the historical data, automatically detecting at least one of the patterns in mobile money transactions incoming from at least one other customer, and classifying the at least one detected pattern as a fraudulent mobile money transaction or a genuine mobile money transaction.

Abstract: Techniques for scheduling one or more services are provided. The techniques include obtaining customer information, obtaining one or more service characteristics for each of the one or more services, and using the customer information and the one or more service characteristics to schedule the one or more services.

Abstract: A processor identifies a first demand type, which includes a first set of demand requirements, and also identifies a second demand type, which includes a second set of demand requirements. Next, the processor retrieves a demand compatibility rule, which specifies a compatibility between the first set of demand requirements and the second set of demand requirements. The processor uses the demand compatibility rule to compute a demand type overlap value between the first demand type and the second demand type. In turn, the processor computes a minimum facility requirement based upon the demand type overlap value and generates a facility report that includes the minimum facility requirement.

Abstract: A method (and system) of providing optimization repeatability in an on-demand computing environment removes variability in an optimization model instance and can be exemplarily implemented in a service architecture. The method and system receives a plurality of physical data instances, which are different representations of the same logical data model, and transforms the plurality of physical data instances into a normalized physical data instance, which can be combined with an optimization model to form a unique optimization model instance, thereby providing repeatability in solving optimization problems.

Abstract: A method for allocating resources in a business model includes determining a plurality of goals for a specified business problem. A plurality of data parameters are extracted from the goals into a computer program and read by a computer. The data parameters represent each of the goals. The data parameters are analyzed to develop a problem set representing the data parameters. Target criteria are input into the computer program, and the target criteria relates to each of the goals and may include prioritizing and/or attributing a weight to each goal. A solution instruction set is developed using the target criteria and the problem set. A solution model is developed from the solution instruction set. The steps of inputting target criteria, developing the solution instruction set, and developing the solution model are repeated to develop additional solution models and an optimal solution model can ultimately be selected.

Abstract: A decision support tool which takes a high-level view of supply and demand in the realm of labor resources and produces globally-optimal recommendations for assignment of practitioners to open seats. These assignments preferably optimize a utility function composed of different business metrics and can be customizable for different organizations and priorities.

Abstract: Techniques for automated pricing of an item are provided. The techniques include obtaining historical data of one or more previous purchases for the item, performing a regression on the historical data, and using the regression to obtain a buying price and a selling price for the item.

Abstract: Techniques for scheduling one or more services are provided. The techniques include obtaining customer information, obtaining one or more service characteristics for each of the one or more services, and using the customer information and the one or more service characteristics to schedule the one or more services.

Abstract: A method and system are disclosed for planning a workforce headcount for a given business process. The method comprises the steps of providing as inputs, i) productivity ramp-ups to model the level of experience and to measure the performance of both new hires and current employees, and ii) industry/market attrition rates for employees; and performing an evaluation, using said inputs, of at least one given management objective. On the basis of this evaluation, a future hiring and transition plan is provided for the given business process for a defined period of time. In the preferred embodiment of the invention, uncertainty is associated with one or more of the inputs, and the future hiring and transition plan is provided by using stochastic programming to model the uncertainty associated with at least one of said one or more of the inputs.

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 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 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 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.