Abstract: A method and associated systems for benchmarking performance of a service organization. A processor collects recorded performance data that identifies how much time members of randomly selected service teams of the service organization required to perform tasks associated with sub-activities of an activity of interest. The data is sorted by sub-activity and data associated with certain biasing types of tasks may be discarded. For each sub-activity for which enough valid data exists, a sub-activity benchmark is identified as a function of a median of task-performance times associated with that sub-activity. This sub-activity benchmark may be used to derive statistical functions that characterize performance of another service team when performing tasks related to the same sub-activity. Such characterizations may be aggregated and exported to a scorecard report that identifies overperforming and underperforming service teams.

Abstract: In one embodiment, a computer-implemented method includes obtaining incident data related to a plurality of servers, including a first server. Configuration data is obtained for each of the plurality of servers. The configuration data includes information about a set of one or more configuration items of the first server. A predictive modeler is trained to predict incident characteristics, based at least in part on the incident data and the configuration data. A modification is selected to the set of configuration items of the first server. Predicted incident characteristics of the first server are simulated, by a computer processor, based on the selected modifications. It is recommended that the selected modifications be made to the first server if predetermined criteria are met by the simulation.

Abstract: An approach for modeling a service delivery system is presented. Data from the service delivery system is collected. Discrete event simulation, queueing, and system heuristics models are constructed from the collected data. Based on the constructed models, a first utilization error indicating first variations among measures of utilization of staffing by the service delivery system is determined. Based on the first utilization error, a problem that causes the first variations is determined and in response, adjustments to the models to correct the problem are determined. A second utilization error is determined. The second utilization error indicates second variations among other measures of the utilization of staffing by the service delivery system which are based on the adjustments. Based on the second utilization error, a consistency among the adjusted models is determined, and in response, an initial recommended model of the service delivery system is derived.

Abstract: An approach for validating a model is presented. Data from a system being modeled is collected. First and second models of the system are constructed from the collected data. Based on the first model, a first determination of an aspect of the system is determined. Based on the second model, a second determination of the aspect of the system is determined. A variation between the first and second determinations is determined. An input for resolving the variation is received and in response, a model of the system that reduces the variation is derived.

Abstract: An approach for prioritizing work requests to resolve incidents in an information technology (IT) infrastructure is presented. Historical data of work requests to resolve incidents in the IT infrastructure is divided into first and second data sets. A first set of data fields of work requests in the first data set is used to generate incident concept(s). The incident concept(s) are combined with a second set of data fields of the work requests in the first data set to form a set of predictive variables. Utilizing the predictive variables, a statistical model is generated for predicting whether or not work requests will be resolved in accordance with a service level target. The statistical model is validated using the second data set. The statistical model is deployed to the IT infrastructure.

Abstract: A computer implemented method allocates resources within a workload management program. A computer identifies a first ratio for a first skill team of plurality of skill teams. The computer determines whether the first ratio for the first skill team exceeds a skill team threshold of the first skill team. Responsive to the computer determining that the first ratio for the first skill team exceeds the skill team threshold of the first skill team, the computer allocates the resources from at least one of the plurality of skill teams other than the first skill team to the first skill team. A computer program product and a computer system for allocating resources within a workload management program are also provided.