Revenue Management of a Professional Services Firm Under a Quality-Revelation Model

Professional service firms (PSFs) such as management consulting, law, accounting, investment banking, architecture, advertising and home-repair companies provide services for turnkey complicated projects. The operational tasks of such firms consists of first bidding for the project and, if successful in the bid, assigning employee resources to the project. In this paper we model this as a revenue management problem and develop a stochastic dynamic programming framework to aid the firm in their bidding and assignment process. We study this primarily under a quality-revelation model where the employees that would be assigned to the project are committed ex ante, as part of the bid. The win probability depends on the quoted price as well as the quality of the employees who would work on the project. We also consider a quality-reputation model where the bid’s win probability depends on past performance, say an average of the quality of past jobs. The problem is computationally challenging and we provide a series of bounds and solution methods to approximate the stochastic dynamic program. Based on our computational techniques we are able to address a number of interesting questions on utilization and the value of each employee type for a PSF. Our methodology provides management a toolkit for bidding on projects as well as to perform workforce analytics to determine optimal utilization levels and staffing decisions.