Revenue Management of Professional services firms (joined work with Kalyan Talluri)



Professional service firms (PSFs) 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. We study this as a revenue management problem and we consider two models: a quality-revelation model where the employees that would be assigned to the project are committed ex ante, as part of the bid and a quality-reputation model where the bid’s win probability depends on past performance, say an average of the quality of past jobs. Using a stylized Markov chain model we argue that the transparency of the quality-revelation model is advantageous, as its performance can only be replicated in the reputation model via a counter-intuitive bidding strategy which may be hard to implement in practice. Subsequently, we develop a stochastic dynamic programming framework for the revelation model, to aid the firm in their bidding and assignment process. The problem is computationally challenging and we provide a series of bounds and solution methods to approximate the stochastic dynamic program.

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Meeting ID: 926 8030 1315