Abstract: A computer-implemented method, according to one embodiment, includes: receiving an offer request including one or more desired services, and selecting available offerings, each of which include at least one of the desired services. A determination is made whether available benchmarks exist for each of the at least one desired service included in each of the selected available offerings. For each desired service determined as not having available benchmarks, a draft benchmark is computed for each of a plurality of criteria. A confidence weight is also computed for each of the draft benchmarks. The available benchmarks, the draft benchmarks, and the confidence weights are further used to construct an offer which is submitted in response to the received offer request. Moreover, the draft benchmarks and the corresponding confidence weights are re-computed for each of the respective desired services in response to determining that the submitted offer was not accepted.

Abstract: A system for cognitive prioritization for report generation may include a processor and a memory cooperating therewith. The processor may be configured to accept a request for a new report from a user, the request having a user profile importance associated therewith and generate a predicted completion time for the new report based upon a historical completion time prediction model based upon historical data for prior reports. The processor may be configured to generate a predicted importance of the new report based upon a historical importance prediction model based upon the historical data for prior reports and determine a combined predicted importance based upon the user profile importance and the predicted importance. The processor may also be configured to generate a prioritization of the new report among other reports based upon the predicted completion time and the combined predicted importance and generate the new report based upon the prioritization.

Abstract: One embodiment provides a method for estimating unit price reduction of services in a new in-flight deal using data of historical deals and market reference deals cost structures. The method includes receiving a detailed cost structure for historical information, market deals information, services quantity information and deals metadata for a first year. For each service: peer deals to the in-flight deal are selected based on the detailed cost structure; missing cost data values in the peer deals are augmented; unit cost reduction values for the peer deals estimated; the unit cost reduction for the in-flight deal from each year in total contract years to a next year without a last contract year are estimated; and a total cost for the in-flight deal for all years in the total contract years beyond the first year are estimated.

Abstract: A method for selecting a set of information technology (IT) services peer deals to an in-flight deal for each first level service in the in-flight deal includes receiving a detailed cost structure for historical information, in-flight deals information, market deals information, services baselines and deals metadata, and multiple peer deals for selection. For historical information and market deals information, all missing baselines for all services in the in-flight deal and all missing unit cost for services are augmented at the first level service. The multiple peer deals are classified into different clusters at the first level service. A closest cluster to the in-flight deal at the first level service is selected. For each second level service of the in-flight deal the method: classifies the selected peer deals into different clusters. A predetermined number of peer deals that appear in a largest number of the selected clusters is selected.

Abstract: A computer-implemented method, according to one embodiment, includes: receiving an offer request including one or more desired services, and selecting available offerings, each of which include at least one of the desired services. A determination is made whether available benchmarks exist for each of the at least one desired service included in each of the selected available offerings. For each desired service determined as not having available benchmarks, a draft benchmark is computed for each of a plurality of criteria. A confidence weight is also computed for each of the draft benchmarks. The available benchmarks, the draft benchmarks, and the confidence weights are further used to construct an offer which is submitted in response to the received offer request. Moreover, the draft benchmarks and the corresponding confidence weights are re-computed for each of the respective desired services in response to determining that the submitted offer was not accepted.

Abstract: A system for cognitive prioritization for report generation may include a processor and a memory cooperating therewith. The processor may be configured to accept a request for a new report from a user, the request having a user profile importance associated therewith and generate a predicted completion time for the new report based upon a historical completion time prediction model based upon historical data for prior reports. The processor may be configured to generate a predicted importance of the new report based upon a historical importance prediction model based upon the historical data for prior reports and determine a combined predicted importance based upon the user profile importance and the predicted importance. The processor may also be configured to generate a prioritization of the new report among other reports based upon the predicted completion time and the combined predicted importance and generate the new report based upon the prioritization.

Abstract: A method for selecting a set of information technology (IT) services peer deals to an in-flight deal for each first level service in the in-flight deal includes receiving a detailed cost structure for historical information, in-flight deals information, market deals information, services baselines and deals metadata, and multiple peer deals for selection. For historical information and market deals information, all missing baselines for all services in the in-flight deal and all missing unit cost for services are augmented at the first level service. The multiple peer deals are classified into different clusters at the first level service. A closest cluster to the in-flight deal at the first level service is selected. For each second level service of the in-flight deal the method: classifies the selected peer deals into different clusters. A predetermined number of peer deals that appear in a largest number of the selected clusters is selected.

Abstract: One embodiment provides a method for estimating unit price reduction of services in a new in-flight deal using data of historical deals and market reference deals cost structures. The method includes receiving a detailed cost structure for historical information, market deals information, services quantity information and deals metadata for a first year. For each service: peer deals to the in-flight deal are selected based on the detailed cost structure; missing cost data values in the peer deals are augmented; unit cost reduction values for the peer deals estimated; the unit cost reduction for the in-flight deal from each year in total contract years to a next year without a last contract year are estimated; and a total cost for the in-flight deal for all years in the total contract years beyond the first year are estimated.

Abstract: A computer-implemented method for operating a business including operations to receive an enterprise model, define at least one new association between a first and a second business element in the enterprise model, federate a business analysis tool, and determine a business-related impact of the new association on other business elements in the enterprise model. The enterprise model is received at a model server and includes two or more business elements and a map of business components. The collection of business elements indicates associations between one or more business elements. The business analysis tools can filter the enterprise model and are federated by incorporating a business element generated from the business analysis tool into the enterprise model. The business-related impact is determined by using the business analysis tool.