Professional service firms (PSFs) such as management consulting, law, accounting, investment banking, architecture, advertising and home-repair companies provide services for
complicated turnkey projects. The firm bids for a project and, if successful in the bid, assigns
employees to work on the project. We formulate this as a revenue management problem under two assumptions: a quality-revelation setup where the employees that would be assigned
to the project are committed ex ante, as part of the bid, and a quality-reputation setup where
the bid’s win probability depends on past performance, say an average of the quality of past
jobs. We first model a stylized Markov-Chain model of the problem amenable to analysis and
show that upfront revelation of the assigned employees has subtle advantages. Subsequent
to this analysis, we develop an operational stochastic dynamic programming framework under the revelation model to aid the firm in this bidding and assignment process. We show
that the problem is computationally challenging and provide a series of bounds and solution methods to approximate the stochastic dynamic program. Based on our model and
computational methods we are able to address a number of interesting business questions
for a PSF, such as the optimal utilization levels and the value of each employee type. Our
methodology provides management a toolkit for bidding on projects as well as to perform
workforce analytics and to make staffing decisions.