Abstract: Systems and methods are provided for predicting and optimizing the probability of an outcome event. In a specific embodiment, the disclosure is directed to a multi-phase communication system configured to perform predictive analyses during stages based on input received from a user. In a particular implementation, there may be a first communication phase configured to accept limited input from a user to establish linear dependency between input and an outcome event for the purpose of an agent assignment, followed by a second communication phase to provide sequential predictive analyses based on natural conversation data between a user and agent. In a specific embodiment, the second communication phase may implement a second predictive model trained to identify non-linear dependencies between communication data and an outcome event. Herein is also described a graphical user interface for representing scores corresponding to the probability of outcome events, among other features.

Abstract: A report indicating a user-reported probability of a successful outcome is received. A behavior and information model is estimated based on the report. The behavior and information model includes a behavior model component having a bias parameter and a consistency parameter. The behavior and information model includes an information model component having a first user-believed probability of a successful outcome and a second user-believed probability of a successful outcome. The behavior and information model is used to yield a model-determined probability of a successful outcome that more accurately reflects a probability of a successful outcome than the user-reported probability of a successful outcome does.

Abstract: A technique includes clustering a plurality of electronic patient records (PRs) based on related diagnostic codes into a plurality of clusters, and analyzing one of the plurality of clusters to determine variations in resource usage within the cluster.

Abstract: Methods, systems, and computer-readable and executable instructions are provided for resource planning. Resource planning can include identifying a resource capacity for a given resource planning horizon, identifying a resource pool guideline that defines a priority of resources from which to satisfy a full-time equivalent (FTE) resource planning requirement, and performing resource planning by satisfying the FTE requirement based on the resource capacity and the resource pool guideline.

Abstract: Information may be extracted from a document. A new pattern may be identified in the document. Classification may be performed based on the extracted information.

Abstract: A system includes an intersection engine to determine intersection points between multiple first functions, each first function corresponding to a product from a plurality of products and each first function based on the corresponding product's utility and profit at a given price point. The system further includes a first ranking engine to rank the intersection points and a second ranking engine to rank the first functions based on the intersection points. The system further includes a solving engine to solve a second function based on a subset of the ranked functions to determine a subset of the products to include in a product portfolio and a price for each product in the portfolio to maximize profit for the plurality of products.

Abstract: Methods, systems, and computer-readable and executable instructions are provided for estimating semi-parametric product demand models. Estimating a semi-parametric product demand model can include identifying a set of products from input market sales data, analyzing the market sales data to determine a relationship between a plurality of factors of the set of products, and estimating the semi-parametric product demand model based on the determined relationship using iterative estimation of a plurality of incremental data trees.

Abstract: There is provided a computer-implemented method of generating a data forecasts for different levels of an entity. The method includes generating an aggregate forecast for an upper level entity comprised of two or more components. The method also includes determining mean values and a coefficient of variation for a probability distribution corresponding to future expected decomposition rates for each of the two or more components. A probability distribution parameter vector is computed based on the mean values and the coefficient of variation. The expected future decomposition rates for each of the two or more components may be computed based on the probability distribution parameter vector and a sample observation corresponding to previously observed decomposition values of each of the two or more components. Component forecasts corresponding to each of the two or more components may be computed based on the aggregate forecast and the expected future decomposition rates.

Abstract: Techniques for generating optimized surgery schedules are described in various implementations. In one example implementation, a method that implements the techniques includes receiving a plurality of surgery scheduling requests for surgical procedures to be performed at a surgical facility having a plurality of operating rooms. The method also includes identifying resource constraints associated with the surgery scheduling requests, and identifying an optimization goal for the surgical facility, the optimization goal being defined using weighted optimization parameters. The method also includes generating a proposed surgery schedule for the surgical facility that includes sequencing and operating room assignments for each of the surgical procedures to be performed, the proposed surgery schedule satisfying the resource constraints and being optimized based on the optimization goal for the surgical facility.

Abstract: Methods, systems, and computer-readable and executable instructions are provided for determining a product price. Determining a product price can include determining an initial market attraction value, a market price sensitivity, and cost information for a product. Determining a product price can also include receiving a market constraint with respect to the product and pricing the product based on the initial market attraction value, the market price sensitivity, the cost information, and the market constraint.

Abstract: Parent node data is split into first and second child nodes based on a first partition variable to create a tree-based model. A first regression model for the first child node data relates the response variable and the predictor variable.

Abstract: A maximum expected value of a residual value warranty for a product to a customer is determined. An expected cost to a provider to support the residual value warranty for the customer is determined, based on the maximum expected value of the candidate residual value warranty to the customer. The expected profitability of the candidate residual value warranty is determined based on the expected cost.

Abstract: A method for determining call center resource allocation can include modeling call center performance over an operations time period using a computer. A number of replicas of the modeled call center performance are simulated, using the computer, over a planning time period, each replica having random contact arrivals and contact service times following a stochastic arrival and service process according to a probability distributions of inter-arrival time and service time. Multiple iterations of each simulation are run on the computer to optimize call center resource allocation. A particular simulation iteration is tested against a criterion of convergence, and call center resource is allocated based on the particular simulation iteration with a successful criterion of convergence.

Abstract: Each product of a number of products is assigned to one of a number of popularity tiers. The popularity tiers are ordered from a most popular tier to a least popular tier. The popularity tiers indicate how popular the products are expected to be among consumers. Each product is assigned to one of a number of margin tiers. The margin tiers are ordered from a highest margin tier to a lowest margin tier. The margin tiers indicate how much money a retailer makes in selling the products to the consumers. Which of the products to offer for sale by the retailer to the consumers are selected by applying decision rules to the products as have been assigned to the popularity tiers and to the margin tiers.

Abstract: Example methods, apparatus and articles of manufacture to determine work placement preferences are disclosed. A disclosed example method includes receiving, via a processor, a request for a resource to fill an open position within an organization, the request including a primary resource group for the open position, determining with the processor resource group preferences of resource groups using a structure of the organization that includes functional relationships between the resource groups, calculating weights for the resource group preferences based on respective organizational distances in the structure of the respective resource groups from the primary resource group, determining a target resource group with a greatest calculated weight for a resource group preference from among the resource groups that have at least one available resource, and selecting a resource within the target resource group to fill the open position.

Abstract: A workforce capacity and capability at an entity is optimized. Values for parameters related to the workforce capacity and capability at the entity are received. A workforce capacity and capability model is used to generate values for decision variables related to the workforce capacity and capability at the entity, based on the values for the parameters that have been received. The workforce capacity and capability model models uncertainty associated with a workforce.

Abstract: A report indicating a user-reported probability of a successful outcome is received. A behavior and information model is estimated based on the report. The behavior and information model includes a behavior model component having a bias parameter and a consistency parameter. The behavior and information model includes an information model component having a first user-believed probability of a successful outcome and a second user-believed probability of a successful outcome. The behavior and information model is used to yield a model-determined probability of a successful outcome that more accurately reflects a probability of a successful outcome than the user-reported probability of a successful outcome does.

Abstract: Illustrated is a system and method for modeling and predicting call center service calls to plan staffing needs based upon historical call center volume data and product life cycle data for the product being serviced by the call center. It includes identifying adjusted historical monthly call volume data. Additionally, the system and method includes transforming the adjusted historical monthly call volume data, using a forecasting algorithm, into a long-term trend and season effect forecast. Further, the system and method includes transforming the adjusted historical monthly call volume data, using a regression algorithm, into a long-term product life cycle based forecast. Additionally, the system and method includes adjusting a point prediction, based on the long-term product life cycle based forecast to create a long-term adjusted monthly forecast, the point prediction adjusted where it exceeds a data point in a bound forecast generated from the long-term trend and season effect forecast.

Abstract: A resource is scored based on a matching of the resource to a job. Job attributes for the job and resource attributes for the resource are identified. A hierarchy of the job attributes that includes dependencies between the job attributes is determined. A score for the resource is determined using the hierarchy of the job attributes and the resource attributes. The score represents a matching between the resource and the job.

Abstract: One embodiment is a method that receives historical data of suppliers and applies, to the historical data, a mathematical optimization system that includes a set of mathematical equations and inequalities that express capabilities and capacities of the suppliers. The mathematical optimization system includes an objective function that minimizes a number of the suppliers to perform third-party labor services for an enterprise. The method selects a sub-set of the suppliers to perform the third-party labor services for the enterprise.