Abstract: System and method for calibration of WFM system modeling parameters. A first mode M[D,S] of a modeler computes demand-shrinkage controlled service levels and an error metric e(M[D,S]) between the controlled and actual service levels. A user device iteratively adjusts each core parameter. When the user is satisfied that e(M[D,S]) is sufficiently small, calibration of the core parameters is complete. The same is done for calibrating the modeling factor, and then a final e(M[D,S])f is computed. A second mode M[D] computes, using the parameters just calibrated, demand-controlled service levels and an error metric e(M[D]) between the controlled service levels and actual levels. The user iteratively adjusts the shrinkage. When the user is satisfied that e(M[D]) is sufficiently small, calibration of the core parameters is complete.

Abstract: A method for providing benchmark-plans to a customer based on benchmark information, comprising receiving a customer-defined service goal and a demand forecast for the customer; generating, with a planner, a plan for achieving the customer-defined service goal based on the demand forecast; determining a benchmark category from a plurality of benchmark categories that the customer belongs to based on at least an industry of the customer, wherein the benchmark category defines a plurality of other customer-defined service goals for other customers participating in at least the industry as the customer; determining benchmark service goals based on the determined benchmark category; executing the planner for each of the benchmark service goals thereby generating benchmark-plans for the demand forecast for the customer; and outputting, to the customer, the plan and the benchmark-plans, wherein the benchmark-plans are different from the plan.

Abstract: The present disclosure describes methods and systems for selecting the forecasting algorithm to use for a prediction based on motifs. A motif is a pattern of interval values that is found to repeat in time series data. Time series data that includes historical demand data (e.g., average communication volume) for an entity at various time intervals in the past is received. The time series data is processed to identify motifs. For each identified motif, the forecasting algorithm that best predicts the historical demand data for time intervals associated with the motif is determined. Later, when the entity desires to receive a forecast for a future time interval, the motif associated with the future time interval is determined. The forecasting algorithm determined to best predict demand for the determined motif is then used to predict the demand for the future time interval.

Abstract: Methods and systems for selecting a forecasting algorithm to use for a forecast for a time interval are provided. A class is a series of time intervals that is selected by an entity from time series data that relates to external data or is a series of time intervals from the time series data that corresponds to a motif. The time series data is processed by a computer to identify motifs, and classes are generated based on each identified motif. A user may further identify one or more classes in the time series data. For each class, the forecasting algorithm that best predicts the historical demand data for time intervals associated with the class is determined. Later, when the entity desires to receive a forecast for a future time interval, the class associated with the future time interval is determined. The forecasting algorithm determined to best predict demand for the determined class is then used.

Abstract: In an entity such as a call center, back office, or retail operation, external event data is recorded along with call volume information for a plurality of time intervals. Based on the recorded event data and call volume for the plurality of intervals, a model is trained to predict call (or other communication) volume for a specified time interval using the external event data. The external event data may include data about one or more events that may affect the demand received by the entity. When the predicted call volume is significantly above or below what would be predicted for the entity using historical data alone, an indicator may be displayed to a user or administrator that identifies the external event that is responsible for the lower or higher prediction. The call volume prediction may be used to schedule one or more agents (or other employees) to work during the specified time interval.

Abstract: System and method for calibration of WFM system modeling parameters. A first mode M[D,S] of a modeler computes demand-shrinkage controlled service levels and an error metric e(M[D,S]) between the controlled and actual service levels. A user device iteratively adjusts each core parameter. When the user is satisfied that e(M[D,S]) is sufficiently small, calibration of the core parameters is complete. The same is done for calibrating the modeling factor, and then a final e(M[D,S])f is computed. A second mode M[D] computes, using the parameters just calibrated, demand-controlled service levels and an error metric e(M[D]) between the controlled service levels and actual levels. The user iteratively adjusts the shrinkage. When the user is satisfied that e(M[D]) is sufficiently small, calibration of the core parameters is complete.

Abstract: System and method for calibration of WFM system modeling parameters. A first mode M[D,S] of a modeler computes demand-shrinkage controlled service levels and an error metric e(M[D,S]) between the controlled and actual service levels. A user device iteratively adjusts each core parameter. When the user is satisfied that e(M[D,S]) is sufficiently small, calibration of the core parameters is complete. The same is done for calibrating the modeling factor, and then a final e(M[D,S])f is computed. A second mode M[D] computes, using the parameters just calibrated, demand-controlled service levels and an error metric e(M[D]) between the controlled service levels and actual levels. The user iteratively adjusts the shrinkage. When the user is satisfied that e(M[D]) is sufficiently small, calibration of the core parameters is complete.

Abstract: In an entity such as a call center, back office, or retail operation, external event data is recorded along with call volume information for a plurality of time intervals. Based on the recorded event data and call volume for the plurality of intervals, a model is trained to predict call (or other communication) volume for a specified time interval using the external event data. The external event data may include data about one or more events that may affect the demand received by the entity. When the predicted call volume is significantly above or below what would be predicted for the entity using historical data alone, an indicator may be displayed to a user or administrator that identifies the external event that is responsible for the lower or higher prediction. The call volume prediction may be used to schedule one or more agents (or other employees) to work during the specified time interval.

Abstract: A rechargeable lithium-ion cell has a cell capacity and includes a positive electrode having a recharged potential and a negative electrode. The rechargeable lithium-ion cell also includes a charge-carrying electrolyte. The charge-carrying electrolyte includes a charge-carrying medium and a lithium salt. The rechargeable lithium-ion cell also includes a redox shuttle having the following structure Formula (I).

Abstract: A rechargeable lithium-ion cell has a cell capacity and includes a positive electrode having a recharged potential and a negative electrode. the rechargeable lithium-ion cell also includes a charge-carrying electrolyte. The charge-carrying electrolyte includes a charge-carrying medium and a lithium salt.

Abstract: A rechargeable lithium-ion cell has a cell capacity and includes a positive electrode having a recharged potential and a negative electrode. The rechargeable lithium-ion cell also includes a charge-carrying electrolyte. The charge-carrying electrolyte includes a charge-carrying medium and a lithium salt. The rechargeable lithium-ion cell also includes a redox shuttle having the following structure.

Abstract: A rechargeable lithium-ion cell has a cell capacity and includes a positive electrode having a recharged potential and a negative electrode. The rechargeable lithium-ion cell also includes a charge-carrying electrolyte. The charge-carrying electrolyte includes a charge-carrying medium and a lithium salt. The rechargeable lithium-ion cell also includes a redox shuttle having the following structure.

Abstract: A rechargeable lithium-ion cell has a cell capacity and includes a positive electrode having a recharged potential and a negative electrode. The rechargeable lithium-ion cell also includes a charge-carrying electrolyte. The charge-carrying electrolyte includes a charge-carrying medium and a lithium salt. The rechargeable lithium-ion cell also includes a redox shuttle having the following structure.