Mark Squillante, IBM Research

Event Details
  • Date/Time:
    • Tuesday November 16, 2010
      10:00 am - 11:00 am
  • Location: Executive classroom
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  • Fee(s):
    N/A
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Contact

Ton Dieker, ISyE
Contact Ton Dieker
404-385-3140

Summaries

Summary Sentence: Integrated Stochastic Resource Planning of Human Capital Supply Chains

Full Summary: 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.

Speaker

Mark S. Squillante,
IBM Research

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. 

Bio

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.

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H. Milton Stewart School of Industrial and Systems Engineering (ISYE)

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Status
  • Created By: Mike Alberghini
  • Workflow Status: Published
  • Created On: Dec 20, 2012 - 10:26am
  • Last Updated: Oct 7, 2016 - 10:01pm