Description:
One of the key challenges facing the professional
services delivery business is the issue of optimally balancing
competing demands from multiple, concurrent engagements
on a limited supply of skill resources. In this paper, we
present a framework for combining causal Bayesian analysis
and optimization to address this challenge. Our framework
integrates the identification and modeling of the impact of
various staffing factors on the delivery quality of individual
engagements, and the optimization of the collective adjustments
of these staffing factors, to maximize overall delivery quality
for a pool of engagements. We describe a prototype system
built using this framework and actual services delivery data
from IBM?s IT consulting business. System evaluation under
realistic scenarios constructed using historical delivery records
provides encouraging evidence that this framework can lead to
significant delivery quality improvements. These initial results
further open up exciting opportunities of additional future work
in this area, including the integration of temporal relationships
for causal learning and multi-period optimization to address
more complex business scenarios.
Speaker(s):
Afsaneh Shirazi, PhD student, Department of Computer Science, University of Illinois at Urbana-Champaign
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