Abstract: Methods, systems, and computer program products for a joint prediction and improvement framework for machine learning models are provided herein. A method includes obtaining a machine learning model initialized with a set of parameters; identifying one or more actions based on test inputs corresponding to the machine learning model and historical actions related to a task, where the historical actions are dependent on respective historical outputs of the machine learning model; using the identified one or more actions to jointly compute: one or more first values corresponding to inference loss for the machine learning model; and one or more second values based at least in part on a computing cost function associated with the task; and updating the set of parameters of the machine learning model based on the one or more first values and the one or more second values.

Abstract: Methods and systems of evaluating a performance of an entity are described. A processor may obtain first data indicating tier attributes of resources of the entity, second data indicating function attributes of the resources, and third data indicating productivity attributes of the resources. The processor may train a model based on the first data, the second data, and the third data, the model may represent transitions of the resources over time. The processor may receive a set of controls including at least an objective to optimize a performance of the entity. The processor may generate a controlled model by integrating the set of controls into the model. The processor may determine a set of outcomes from the controlled model that includes at least a set of transitions relating to the resources that may optimize the performance of the entity.

Abstract: Methods and systems of evaluating a performance of an entity are described. A processor may obtain first data indicating tier attributes of resources of the entity, second data indicating function attributes of the resources, and third data indicating productivity attributes of the resources. The processor may train a model based on the first data, the second data, and the third data, the model may represent transitions of the resources over time. The processor may receive a set of controls including at least an objective to optimize a performance of the entity. The processor may generate a controlled model by integrating the set of controls into the model. The processor may determine a set of outcomes from the controlled model that includes at least a set of transitions relating to the resources that may optimize the performance of the entity.

Abstract: A model is built of benefit of each of a plurality of computing tasks under uncertainty as a function of computing resources invested in each of the computing tasks, and a model of risk is built of each of the computing tasks under uncertainty as a function of the computing resources invested in each of the computing tasks. Risk of a task allocation is calculated with the risk model, and benefit of a task allocation is calculated with the benefit model. An allocation of the computing resources is found to increase the benefit and manage the risk. The allocation of computing resources is applied to the computing tasks.

Abstract: One or more processors determine a financial target for a plurality of business accounts across a plurality of product brands that are included in a business account level of an organizational hierarchy of a business organization. The organizational hierarchy includes a plurality of levels. One or more processors determine respective financial targets and quotas for a plurality of nodes included in a level of the organizational hierarchy. One or more processors determine respective financial targets for combinations of business accounts and product brands. The determination of the financial targets is based on a statistical model that is fitted at a middle level of the organizational hierarchy, and a risk-based stochastic optimization that is used to set financial targets and quotas at one or more levels of the organizational hierarchy.

Abstract: A method for commodity sourcing management or commodity management includes identifying a plurality of divisions of commoditization. A plurality of sourcing options for at least one of the divisions of commoditization are identified, as well as a plurality of financial instruments. The method includes correlating the plurality of divisions of commoditization, the sourcing options and the financial instruments so that they are commonly commensurable for analysis.

Abstract: Tasks of varying sizes may be allocated to multiple knapsacks (resources) both with multiple dimensions (domain principles) of a given size according to one or more desired criteria. For instance, sales territory management involves determining the allocation of items (accounts) of varying sizes to multiple knapsacks (territories or sales representatives) both with multiple dimensions (business principles) of a given size according to one or more desired criteria.

Abstract: A method for commodity sourcing management or commodity management includes identifying a plurality of divisions of commoditization. A plurality of sourcing options for at least one of the divisions of commoditization are identified, as well as a plurality of financial instruments. The method includes correlating the plurality of divisions of commoditization, the sourcing options and the financial instruments so that they are commonly commensurable for analysis.

Abstract: A risk management method and system determine distribution of skills, composition of skills and resources to achieve said distribution, a set of actions to achieve said composition, a portfolio of service and/or product offerings, a composition of staffing plans to achieve said portfolio, and a set of demand conditioning actions to achieve said composition, in order to hedge against uncertainty based on demand information, risk information, product and/or service revenue and information, and skill cost and information, while meeting business objectives.

Abstract: The present invention provides a method, a system, and a computer-readable medium with instructions for a computer to optimize one or more tradeoffs between or among serviceability, liability, and/or inventory in a multi-tier network of suppliers. The probabilistic optimization of tradeoffs enables assets stored at one or a plurality of tiers in the network to be optimally transferred downstream with certain probabilities. The multi-tier network of suppliers may consist of at least one original equipment manufacturer tier and at least one supplier tier.

Abstract: A method (and structure) for end-to-end workforce management, includes identifying sources of data that together reflect data of substantially the entirety of a workforce of an organization, identifying service components related to the workforce, and combining the data sources and service components into an integrated framework to support an end-to-end workforce management cycle.

Abstract: A workforce management tool includes an input section that receives data from one or more data sources that together reflect data of substantially an entirety of a workforce of an organization. A plurality of service modules receive and process the data in accordance with requirements of different segments of the organization. The plurality of service modules are interconnected so that a user in a segment of the organization can selectively view any data of the workforce tool that is related to that segment.

Abstract: A methodology for business process analysis and optimization. This enables firms to analyze business processes using stochastic processing network models to estimate process key performance indicators. Based on these indicators, alternate process models can be further developed and analyzed, in order to optimize business objectives. The analysis methodology can be used for business process design (at design time) and for business process management (at run time).

Abstract: A structure (and method) for a computerized organization optimization tool includes an input port to receive one or more of: characteristics of at least one offering of the organization; characteristics of resources of the organization; and characteristics of constraints of at least one of the resources and the at least one offering. A calculator receives the characteristics to calculate one or more optimal targets for the organization.

Abstract: A method of managing resources, includes identifying a project or service opportunity that has disparate resource attribute requirements. At least one of internal and external flexible resources suitable for the project or service opportunities are also identified. The internal and external flexible resources are correlated with the disparate resource attribute requirements for managing a risk associated with the project or service opportunity.

Abstract: The present invention provides a method, a system, and a computer-readable medium with instructions for a computer to optimize one or more tradeoffs between or among serviceability, liability, and/or inventory in a multi-tier network of suppliers. The probabilistic optimization of tradeoffs enables assets stored at one or a plurality of tiers in the network to be optimally transferred downstream with certain probabilities. The multi-tier network of suppliers may consist of at least one original equipment manufacturer tier and at least one supplier tier.

Abstract: A method (and structure) for developing a distribution function for the probability of winning a bid by a seller for a product or service, using the seller's own historical data for winning bids and lost bids, includes normalizing the data for winning bids and the data for lost bids and merging the normalized data into a single set of data.

Abstract: A methodology for business process analysis and optimization. This enables firms to analyze business processes using stochastic processing network models to estimate process key performance indicators. Based on these indicators, alternate process models can be further developed and analyzed, in order to optimize business objectives. The analysis methodology can be used for business process design (at design time) and for business process management (at run time).

Abstract: A method (and structure) for developing a distribution function for the probability of winning a bid by a seller for a product or service, using the seller's own historical data for winning bids and lost bids, includes normalizing the data for winning bids and the data for lost bids and merging the normalized data into a single set of data.