Predicting Labor Cost through IT Management Complexity Metrics

Abstract

We propose a model for relating IT management complexity metrics to key business-level performance metrics like time and labor cost. In particular, we address the problem of quantifying and predicting the value that automation and IT service management process transformation will yield before their actual deployment. Our approach looks at this problem from a different, new perspective by regarding complexity as a surrogate for potential labor cost and human-error-induced problems: It consists in (1) assessing and evaluating the complexity of IT management processes and procedures, (2) separately measuring business-level performance metrics (3) relating the collected complexity metrics to business-level performance metrics by means of a quantitative model, and (4) validating the model through a field study. Algorithms are presented for selecting a subset of the complexity metrics to use as explanatory variables in a quantitative model and for constructing the quantitative model itself. Besides improving decision making for deploying automation technologies and transforming IT service management processes, our quantitative IT management complexity model can help service providers and outsourcers predict the amount of human effort and skills that will be needed to provide a given service, thus allowing them to more effectively evaluate costs and benefits of automation technologies and IT management process transformations.