Imperial College London
Alternate

Professor Kalyan Talluri

Business School

Professor of Analytics and Operations
 
 
 
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Contact

 

kalyan.talluri Website CV

 
 
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Location

 

387ABusiness School BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Talluri:2023:10.1287/opre.2022.2351,
author = {Talluri, K and Angelos, T},
doi = {10.1287/opre.2022.2351},
journal = {Operations Research},
pages = {1021--1439},
title = {Revenue management of a professional services  firm with quality-revelation},
url = {http://dx.doi.org/10.1287/opre.2022.2351},
volume = {71},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Professional service firms (PSFs) such as management consulting, law, accounting, investment banking, architecture, advertising and home-repair companies provide services forcomplicated turnkey projects. The firm bids for a project and, if successful in the bid, assignsemployees 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 assignedto the project are committed ex ante, as part of the bid, and a quality-reputation setup wherethe bid’s win probability depends on past performance, say an average of the quality of pastjobs. We first model a stylized Markov-Chain model of the problem amenable to analysis andshow that upfront revelation of the assigned employees has subtle advantages. Subsequentto 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 showthat the problem is computationally challenging and provide a series of bounds and solution methods to approximate the stochastic dynamic program. Based on our model andcomputational methods we are able to address a number of interesting business questionsfor a PSF, such as the optimal utilization levels and the value of each employee type. Ourmethodology provides management a toolkit for bidding on projects as well as to performworkforce analytics and to make staffing decisions.
AU  - Talluri,K
AU  - Angelos,T
DO  - 10.1287/opre.2022.2351
EP  - 1439
PY  - 2023///
SN  - 0030-364X
SP  - 1021
TI  - Revenue management of a professional services  firm with quality-revelation
T2  - Operations Research
UR  - http://dx.doi.org/10.1287/opre.2022.2351
UR  - http://hdl.handle.net/10044/1/97864
VL  - 71
ER  -
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