Professional service firms (PSFs) such as management consulting, law, accounting, investment banking, architecture, advertising, and home-repair companies provide services for complicated turnkey projects. A 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 up-front 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 with a tool kit for bidding on projects as well as to perform workforce analytics and to make staffing decisions.
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