CONSOLIDATED RESOURCE MANAGEMENT ACROSS MULTIPLE SERVICES
Various embodiments include a computer-implemented method performed by an optimization engine. The method can include receiving a request to deploy a resource for one of multiple service lines, where each service line is operable independent of the other, the resource belongs to a pool of resources, and each resource has learned features regarding suitability for any of the service lines. The method can further include mediating the request to identify suitable resource(s) that satisfy the request, where suitability is determined based on the learned features output by a machine learning model based on inputs indicative of interactions between the plurality of service lines and the pool of resources. The method can further include deploying an identified resource that satisfies the request for the service line, wherein the identified resource is deployable among at least two or more of the service lines.
1 . A method comprising:
receiving, at an optimization engine, a request to deploy a resource to perform a service for a service line of a plurality of service lines, wherein each service line is operable independent of any other service line, and wherein the resource belongs to a pool of resources and each resource is associated with a plurality of features that reflect suitability for any of the plurality of service lines;
mediating, at the optimization engine, the request to identify one or more suitable resources that satisfy the request, wherein suitability of any resource is determined based on the plurality of features; and
deploying, at the optimization engine, an identified resource that satisfies the request for the service line, wherein the identified resource is deployable among at least two or more of the service lines.
2 . The method of claim 1 , wherein the plurality of features comprises a plurality of learned features.
3 . The method of claim 2 , wherein identifying the one or more suitable resources comprises.
determining the suitability of a resource based on the plurality of learned features, the plurality of learned features being output of a predictive data model based on inputs indicative of data representing interactions between the plurality of service lines and the pool of resources.
4 . The method of claim 2 , wherein identifying the one or more suitable resources comprises.
analyzing statistically one or more components of interaction message data and the plurality of features.
5 . The method of claim 4 , further comprising:
predicting data representing a value indicative of a capacity of another resource; and
modifying a subset of learned feature data to implement the another resource,
wherein the resource and the another resource are assigned to different service lines.
6 . The method of claim 1 , further comprising:
monitoring utilization and performance of the identified resource relative to the service line.
7 . The method of claim 6 , further comprising:
updating the plurality of features based on the monitored utilization and performance of the identified resource relative to the service line.
8 . The method of claim 1 , wherein the plurality of features includes data representing one or more of profile data value, a skill and certification data value, a schedule of resource availability data value, a talent and performance management data value, a subcontractor management data value, algorithmic data, and a supply and demand management data value.
9 . The method of claim 1 , wherein the request is based on one or more of a resource utilization data value, data representing an assignment to a project, data representing an assignment to a case, data representing an assignment to break/fix-modeled task, data representing an assignment to a managed service request, data representing a schedule of utilization based on time, and data representing an update of progress of an activity in which a the resource is engaged.
10 . A system comprising:
a resource management optimization system including a data store configured to store executable instructions and data, and a processor configured to execute instructions, the resource management optimization system configured to interface with a plurality of disparate computing platforms configured to provide one or more portions of different service lines, at least one platform being a cloud platform, the processor being configured to:
receive a request to deploy a resource to perform a service for a service line of a plurality of service lines, wherein each service line is operable independent of any other service line, and wherein the resource belongs to a pool of resources and each resource is associated with a plurality of features that reflect suitability for any of the plurality of service lines;
mediate the request to identify one or more suitable resources that satisfy the request, wherein suitability of any resource is determined based on the plurality of features; and
deploy an identified resource that satisfies the request for the service line, wherein the identified resource is deployable among at least two or more of the service lines.
11 . The system of claim 10 , wherein the plurality of features comprises a plurality of learned features.
12 . The system of claim 11 , wherein the processor is further configured to:
determine the suitability of a resource based on the plurality of learned features, the plurality of learned features being output of a predictive data model based on inputs indicative of data representing interactions between the plurality of service lines and the pool of resources.
13 . The system of claim 11 , wherein the processor is further configured to:
analyze statistically one or more components of interaction message data and the plurality of features.
14 . The system of claim 13 , wherein the processor is further configured to:
predict data representing a value indicative of a capacity of another resource; and
modify a subset of learned feature data to implement the another resource,
wherein the resource and the another resource are assigned to different service lines.
15 . The system of claim 10 , wherein the processor is further configured to:
monitor utilization and performance of the identified resource relative to the service line.
16 . The system of claim 15 , wherein the processor is further configured to:
update the plurality of features based on the monitored utilization and performance of the identified resource relative to the service line.
17 . The system of claim 10 , wherein the plurality of features includes data representing one or more of profile data value, a skill and certification data value, a schedule of resource availability data value, a talent and performance management data value, a subcontractor management data value, algorithmic data, and a supply and demand management data value.
18 . The system of claim 10 , wherein the request is based on one or more of a resource utilization data value, data representing an assignment to a project, data representing an assignment to a case, data representing an assignment to break/fix-modeled task, data representing an assignment to a managed service request, data representing a schedule of utilization based on time, and data representing an update of progress of an activity in which a the resource is engaged.