SPEAKER:   Mark Squillante

ABSTRACT:

In this talk we present an integrated suite of operations research models and methods that supports the effective and efficient management and planning of human capital supply chains by addressing distinct features and characteristics of human talent and skills. This consists of solutions for: (1) the statistical forecasting of future demand and resource requirements; (2) a new form of risk-based stochastic resource capacity planning; (3) the stochastic modeling and optimization/control of supply evolutionary dynamics over time; (4) a new form of optimal multi-skill supply-demand matching; and (5) the stochastic optimization of business decisions to manage resource shortages and overages. These solutions include contributions in the areas of stochastic models and stochastic optimization/control.  The suite of models and methods constitutes an end-to-end solution that is deployed as an important part of the human capital management and planning process within IBM.

Mark S. Squillante is a Research Staff Member in the Mathematical Sciences Department at the IBM Thomas J. Watson Research Center, where he leads the Applied Probability and Stochastic Optimization team.  His research interests concern mathematical foundations of the analysis, modeling and optimization of the design and control of stochastic systems, including stochastic processes, applied probability, stochastic optimization and control, and their applications.  He is the author of many research articles across these areas, and has received several internal (IBM) and external research awards.  He is a Fellow of ACM and IEEE, and currently serves on the editorial boards of Operations Research, Stochastic Models and Performance Evaluation.