Analysis of Operational Data for Expertise Aware Staffing

Abstract

Knowledge intensive business services such as IT Services, rely on the expertise of the knowledge workers for performing the activities involved in the delivery of services. The activities performed could range from performing simple, repetitive tasks to resolving more complex situations. The expertise of the task force can also vary from novices who cost less to advanced skill workers and experts who are more expensive. Staffing of service systems relies largely on the assumptions underlying the operational productivity of the workers. Research independently points to the impact of factors such as complexity of work and expertise of the worker on worker productivity. In this paper, we examine the impact of complexity of work, priority or importance of work and expertise of the worker together, on the operational productivity of the worker. For our empirical analysis, we use the data from real-life engagement in the IT service management domain. Our finding, on the basis of the data indicates, not surprisingly, that experts are more suitable for complex or high priority work with strict service levels. In the same setting, when experts are given simpler tasks of lower priority, they tend to not perform better than their less experienced counterparts. The operational productivity measure of experts and novices is further used as an input to a discrete event simulation based optimization framework that model real-life service system to arrive at an optimal staffing. Our work demonstrates that data driven techniques, similar to the one presented here is useful for making more accurate staffing decisions by understanding worker efficiency derived from the analysis of operational data.