IDEAS home Printed from https://ideas.repec.org/a/inm/orinte/v41y2011i5p414-435.html
   My bibliography  Save this article

OnTheMark: Integrated Stochastic Resource Planning of Human Capital Supply Chains

Author

Listed:
  • Heng Cao

    (Business Analytics and Mathematical Sciences Department, IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598)

  • Jianying Hu

    (Business Analytics and Mathematical Sciences Department, IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598)

  • Chen Jiang

    (Business Analytics and Mathematical Sciences Department, IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598)

  • Tarun Kumar

    (Business Analytics and Mathematical Sciences Department, IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598)

  • Ta-Hsin Li

    (Business Analytics and Mathematical Sciences Department, IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598)

  • Yang Liu

    (Business Analytics and Mathematical Sciences Department, IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598)

  • Yingdong Lu

    (Business Analytics and Mathematical Sciences Department, IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598)

  • Shilpa Mahatma

    (Business Analytics and Mathematical Sciences Department, IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598)

  • Aleksandra Mojsilović

    (Business Analytics and Mathematical Sciences Department, IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598)

  • Mayank Sharma

    (Business Analytics and Mathematical Sciences Department, IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598)

  • Mark S. Squillante

    (Business Analytics and Mathematical Sciences Department, IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598)

  • Yichong Yu

    (Business Analytics and Mathematical Sciences Department, IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598)

Abstract

In this paper, we present a suite of innovative operations research models and methods called OnTheMark (OTM). This suite supports the effective management of human capital supply chains by addressing distinct features of human talent that cannot be handled via traditional supply chain management. OTM consists of novel solutions for (1) statistical forecasting of demand and human capital requirements, (2) risk-based stochastic human-talent capacity planning, (3) stochastic modeling and optimization (control) of human capital supply evolutionary dynamics over time, (4) optimal multiskill supply-demand matching, and (5) stochastic optimization of business decisions and investments to manage human capital shortages and overages. The OTM suite was developed and deployed as an important part of the human capital management and planning process within IBM, providing support for decision making to drive better business performance. This is achieved through important contributions in the areas of stochastic models and optimization (control), and the innovative application and integration of these models and methods in human capital management applications.

Suggested Citation

  • Heng Cao & Jianying Hu & Chen Jiang & Tarun Kumar & Ta-Hsin Li & Yang Liu & Yingdong Lu & Shilpa Mahatma & Aleksandra Mojsilović & Mayank Sharma & Mark S. Squillante & Yichong Yu, 2011. "OnTheMark: Integrated Stochastic Resource Planning of Human Capital Supply Chains," Interfaces, INFORMS, vol. 41(5), pages 414-435, October.
    • Handle: RePEc:inm:orinte:v:41:y:2011:i:5:p:414-435
    DOI: 10.1287/inte.1110.0596
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/inte.1110.0596
    Download Restriction: no
    File URL: https://libkey.io/10.1287/inte.1110.0596?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kyomin Jung & Yingdong Lu & Devavrat Shah & Mayank Sharma & Mark S. Squillante, 2019. "Revisiting Stochastic Loss Networks: Structures and Approximations," Mathematics of Operations Research, INFORMS, vol. 44(3), pages 890-918, August.
    2. Michael J. Davis & Yingdong Lu & Mayank Sharma & Mark S. Squillante & Bo Zhang, 2018. "Stochastic Optimization Models for Workforce Planning, Operations, and Risk Management," Service Science, INFORMS, vol. 10(1), pages 40-57, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:orinte:v:41:y:2011:i:5:p:414-435. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.