We develop techniques for mining labor records from a large number of historical IT consulting projects in order to discover clusters of projects exhibiting similar resource usage over the project life-cycle. The clustering results, together with domain expertise, are used to build a meaningful project taxonomy that can be linked to project resource requirements. Such a linkage is essential for project-based workforce demand forecasting, a key input for more advanced workforce management decision support. We formulate the problem as a sequence clustering problem where each sequence represents a project and each observation in the sequence represents the weekly distribution of project labor hours across job role categories. To solve the problem, we use a model-based clustering algorithm based on explicit state duration left-right hidden semi-Markov models (HsMM) capable of handling high-dimensional, sparse, and noisy Dirichlet-distributed observations and sequences of widely varying lengths. We then present an approach for using the underlying cluster models to estimate future staffing needs. The approach is applied to a set of 250 IT consulting projects and the results discussed.
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