Abstract: Example implementations described herein involve extracting keywords and dependency information from a text; and generating a co-occurrence dictionary for the text, the generating the co-occurrence dictionary involving selecting ones of the keywords for inclusion in the co-occurrence dictionary based on a number of times the ones of the keywords satisfy the dependency rules; determining for the selected ones of the keywords included in the co-occurrence dictionary, surrounding words to be associated with the selected ones of the keywords in the co-occurrence dictionary based on a number of instances of co-occurrence of the surrounding words with the selected ones of the keywords; and generating weights for each of the selected ones of the keywords in the co-occurrence dictionary based on a number of the surrounding words associated with the selected ones of the keywords.

Abstract: Example implementations involve a mechanism of distribution of products and services, which may have early signals of demand when such products are consumed by project based activities. By offering flexible demand and supply matching, an analytics engine platform predicts such demand, and constructs micro-contracts to match suppliers with project operators, anticipating their demand needs. The mechanisms can be applied to distribution of any kind of products or services in a supply chain which demand arises by planned projects.

Abstract: Example implementations involve a mechanism of distribution of products and services, which may have early signals of demand when such products are consumed by project based activities. By offering flexible demand and supply matching, an analytics engine platform predicts such demand, and constructs micro-contracts to match suppliers with project operators, anticipating their demand needs. The mechanisms can be applied to distribution of any kind of products or services in a supply chain which demand arises by planned projects.

Abstract: Example implementations described herein involve extracting keywords and dependency information from a text; and generating a co-occurrence dictionary for the text, the generating the co-occurrence dictionary involving selecting ones of the keywords for inclusion in the co-occurrence dictionary based on a number of times the ones of the keywords satisfy the dependency rules; determining for the selected ones of the keywords included in the co-occurrence dictionary, surrounding words to be associated with the selected ones of the keywords in the co-occurrence dictionary based on a number of instances of co-occurrence of the surrounding words with the selected ones of the keywords; and generating weights for each of the selected ones of the keywords in the co-occurrence dictionary based on a number of the surrounding words associated with the selected ones of the keywords.

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: 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: 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.

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: A system and method for determining the optimum price that a service provider should charge to customers of at least a partially refundable extended-product warranty to optimize profits generated from providing such warranties. In one aspect of the present invention the customer may elect to purchase warranty coverage when the product is new and cancel warranty coverage at any time thereafter, which election is based in part on the customer's expected discounted net utility from his coverage decisions. In another aspect of the present invention, the customer is allowed to make dynamic repair or replacement decisions in each period based on the product's failure status or on other criteria. In one embodiment, the customer can be afforded warranty coverage flexibility in terms of his ability to turn coverage off whenever desired and to obtain a partial refund of the warranty premium.

Abstract: A system and method for determining the optimum price that a service provider should charge to customers of a periodic extended-product warranty to optimize profits generated from providing such warranties. In one aspect of the present invention the customer is allowed to elect or to cancel warranty coverage on a monthly basis which election is based in part on the customer's expected net utility from his coverage decisions. In one embodiment, the customer can be afforded complete warranty coverage flexibility in terms of his ability to turn coverage on and off whenever desired. In another aspect of the present invention the customer can be allowed to make dynamic repair or replacement decisions in each period based on the product's failure status or on other criteria.

Abstract: Systems, methods, and other embodiments associated with workforce planning. One example method includes receiving a project data associated with a project opportunity. The project data includes a probability data. The probability data describes the likelihood of a project opportunity being selected. The project data includes a workforce demand. The example method may also include establishing an objective function that is based, at least in part, on the project data, and a set of constraints. The objective function concerns a workforce allocation plan. The example method may also include minimizing a workforce cost by solving the objective function in light of the set of constraints. Solving the objective function includes optimizing a tradeoff between labor utilization and project demand fulfillment. The example method may also include providing the workforce allocation plan.

Abstract: One embodiment is a method that determines, for a sale of a product and a post-sale service for the product from a supplier to a retailer, an expected supply chain profit for the sale as a function of an effort cost function of the retailer, a quantity of the product ordered, and an expected revenue of the retailer. A wholesale price schedule is determined that is a function of the quantity of the product ordered by the retailer. The wholesale price schedule reduces effects of double marginalization on the sale of the product and the post-sale service.

Abstract: The present disclosure is directed to methods for pricing limited use product warranties to increase profit. Such a method can include selecting a plurality of potential warranty options, where each warranty option has an associated product usage limit and an associated warranty purchase cost; calculating a customer's expected support cost; determining a customer demand for each of the plurality of warranty options; calculating a provider's expected option profit for each of the plurality of warranty options; and calculating a provider's expected total profit for the plurality of warranty options.

Abstract: To perform forecasting, a first data collection having data values at first intervals is received, and a first forecasting model is built based on the first data collection. A second forecasting model is built based on a second data collection having intervals aggregated from intervals of the first data collection, wherein the second forecasting model is at a different aggregation level than the first forecasting model. At least one metric is computed by performing at least one test based on at least one of the first and second data collections to indicate which of the first and second forecasting models has a better forecast quality.

Abstract: The present disclosure is directed to product warranties having a residual value and a potential rebate based on the residual value. The residual value can thus be inversely related to the number of claims filed against the warranty.

Abstract: To perform forecasting, a first data collection having data values at first intervals is received, and a first forecasting model is built based on the first data collection. A second forecasting model is built based on a second data collection having intervals aggregated from intervals of the first data collection, wherein the second forecasting model is at a different aggregation level than the first forecasting model. At least one metric is computed by performing at least one test based on at least one of the first and second data collections to indicate which of the first and second forecasting models has a better forecast quality.