A Learning Method for Improving Quality of Service Infrastructure Management in New Technical Support Groups
Service infrastructure management requires the matching of tasks to technicians with a variety of expert knowledge in different areas. Most Service Delivery organizations do not have a consistent view of the evolution of the technician skills because in a dynamic environment the creation and maintenance of a skill model is a difficult task, especially in light of privacy regulations, changing service catalogs and worker turnover. In addition, as services expand, new technical support groups for the same type of services are created and also new technicians may be added, either into a new group or into existing groups. To tackle this problem we evolve a method for ranking technicians on their expected performance according to their suitability for receiving the assignment of a service request. This method makes use of similarities between the technicians and previous tasks performed by them. We propose a strategy for incorporating new technicians and delivery team reorganizations into the method and we present experimental results demonstrating the efficacy of the strategy. Applying this strategy to new teams yields on average acceptable accuracy within 4 hours, though with a wide variation across teams for the first 12 hours. Accuracy and its variability approach the quality of accuracy on older teams over 24 hours.
Keywordsservice management service quality machine learning ticket dispatching request fulfillment
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