Abstract: Risk in business management is analyzed based on a probabilistic network approach which quantifies the impact of operational risk on financial metrics such as Value-at-Risk (VAR) and/or Potential Losses (PL). This approach provides further capability to determine the optimal placement of one or more countermeasures within a system to minimize the impact of operational risks.

Abstract: Mathematical means and methods are used within the context of mathematical models of a workforce evolution to address key issues in workforce design and planning. Examples of such mathematical means and methods are (but not limited to) fluid-flow models and diffusion-process models. In each case, these mathematical models characterize the workforce evolution over time as a function of dynamic workforce events, such as new hires, terminations, resignations, retirements, promotions and transfers, and dynamic workforce topology, such as the viable paths from one workforce resource state to another workforce resource state.

Abstract: Mathematical means and methods are used within the context of mathematical models of a workforce evolution to address key issues in workforce design and planning. Examples of such mathematical means and methods are (but not limited to) fluid-flow models and diffusion-process models. In each case, these mathematical models characterize the workforce evolution over time as a function of dynamic workforce events, such as new hires, terminations, resignations, retirements, promotions and transfers, and dynamic workforce topology, policy, or scenario, such as the viable paths from one workforce resource state to another workforce resource state.

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: Risk in business management is analyzed based on a probabilistic network approach which quantifies the impact of operational risk on financial metrics such as Value-at-Risk (VAR) and/or Potential Losses (PL). This approach provides further capability to determine the optimal placement of one or more countermeasures within a system to minimize the impact of operational risks.

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: Mathematical means and methods are used within the context of mathematical models of a workforce evolution to address key issues in workforce design and planning. Examples of such mathematical means and methods are (but not limited to) fluid-flow models and diffusion-process models. In each case, these mathematical models characterize the workforce evolution over time as a function of dynamic workforce events, such as new hires, terminations, resignations, retirements, promotions and transfers, and dynamic workforce topology, such as the viable paths from one workforce resource state to another workforce resource state.

Abstract: Mathematical means and methods are used within the context of mathematical models of a workforce evolution to address key issues in workforce design and planning. Examples of such mathematical means and methods are (but not limited to) fluid-flow models and diffusion-process models. In each case, these mathematical models characterize the workforce evolution over time as a function of dynamic workforce events, such as new hires, terminations, resignations, retirements, promotions and transfers, and dynamic workforce topology, policy, or scenario, such as the viable paths from one workforce resource state to another workforce resource state.

Abstract: A method and structure for calculating a risk exposure for a disaster recovery process, including loading a user interface into a memory, the user interface allowing control of an execution of one or more risk models. Each risk model is based on a specific disaster type, and each risk model addresses a recovery utilization of one or more specific assets identified as necessary for a recovery process of the disaster type. One of the risk models is executed at least one time.