IDEAS home Printed from https://ideas.repec.org/a/inm/orinte/v43y2013i2p152-169.html
   My bibliography  Save this article

HP Enterprise Services Uses Optimization for Resource Planning

Author

Listed:
  • Cipriano Santos

    (HP Labs, Palo Alto, California 94304)

  • Tere Gonzalez

    (HP Labs, Palo Alto, California 94304)

  • Haitao Li

    (College of Business Administration, University of Missouri, St. Louis, St. Louis, Missouri 63121)

  • Kay-Yut Chen

    (HP Labs, Palo Alto, California 94304)

  • Dirk Beyer

    (MarketShare L.L.P., Los Angeles, California 90025)

  • Sundaresh Biligi

    (HP Enterprise Business, Bangalore 560 100, India)

  • Qi Feng

    (McCombs School of Business, University of Texas at Austin, Austin, Texas 78712)

  • Ravindra Kumar

    (HP Enterprise Business, Bangalore 560 100, India)

  • Shelen Jain

    (HP Labs, Palo Alto, California 94304)

  • Ranga Ramanujam

    (HP Enterprise Business, Bangalore 560 100, India)

  • Alex Zhang

    (HP Labs, Palo Alto, California 94304)

Abstract

The main responsibility of resource and delivery managers at Hewlett-Packard (HP) Enterprise Services (HPES) involves matching resources (skilled professionals) with jobs that project opportunities require. The previous Solution Opportunity Approval and Review (SOAR) process at HPES addressed uncertainty by producing decentralized project staffing decisions. This often led to many last-minute subjective, sometimes costly, resource allocation decisions. Based on our research, we developed a decision support tool for resource planning (RP) to enhance the SOAR process. It optimizes matching professionals who have diverse delivery roles and skills to jobs and projects across geographical locations while explicitly accounting for both demand and supply uncertainties. It also embeds capabilities for managers to incorporate tacit human knowledge and judgment information into the decision-making process. With its 2009 deployment in Best Shore, Bangalore operations of HPES, the RP tool’s significant benefits include reduced service delivery costs, increased workforce utilization, and profitability.

Suggested Citation

  • Cipriano Santos & Tere Gonzalez & Haitao Li & Kay-Yut Chen & Dirk Beyer & Sundaresh Biligi & Qi Feng & Ravindra Kumar & Shelen Jain & Ranga Ramanujam & Alex Zhang, 2013. "HP Enterprise Services Uses Optimization for Resource Planning," Interfaces, INFORMS, vol. 43(2), pages 152-169, April.
    • Handle: RePEc:inm:orinte:v:43:y:2013:i:2:p:152-169
    DOI: 10.1287/inte.1110.0621
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/inte.1110.0621
    Download Restriction: no
    File URL: https://libkey.io/10.1287/inte.1110.0621?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
    ---><---

    References listed on IDEAS

    as
    1. Plott, Charles R & Sunder, Shyam, 1982. "Efficiency of Experimental Security Markets with Insider Information: An Application of Rational-Expectations Models," Journal of Political Economy, University of Chicago Press, vol. 90(4), pages 663-698, August.
    2. Plott, Charles R & Sunder, Shyam, 1988. "Rational Expectations and the Aggregation of Diverse Information in Laboratory Security Markets," Econometrica, Econometric Society, vol. 56(5), pages 1085-1118, September.
    3. Kay-Yut Chen & Leslie R. Fine & Bernardo A. Huberman, 2003. "Predicting the Future," Information Systems Frontiers, Springer, vol. 5(1), pages 47-61, January.
    4. Gang Yu & Julian Pachon & Benjamin Thengvall & Darryal Chandler & Al Wilson, 2004. "Optimizing Pilot Planning and Training for Continental Airlines," Interfaces, INFORMS, vol. 34(4), pages 253-264, August.
    5. Erwin Abbink & Matteo Fischetti & Leo Kroon & Gerrit Timmer & Michiel Vromans, 2005. "Reinventing Crew Scheduling at Netherlands Railways," Interfaces, INFORMS, vol. 35(5), pages 393-401, October.
    6. Forsythe, Robert & Forrest Nelson & George R. Neumann & Jack Wright, 1992. "Anatomy of an Experimental Political Stock Market," American Economic Review, American Economic Association, vol. 82(5), pages 1142-1161, December.
    7. Allen Holder, 2005. "Navy Personnel Planning and the Optimal Partition," Operations Research, INFORMS, vol. 53(1), pages 77-89, February.
    8. Edward P. C. Kao & Maurice Queyranne, 1985. "Budgeting Costs of Nursing in a Hospital," Management Science, INFORMS, vol. 31(5), pages 608-621, May.
    9. Saul I. Gass & Roger W. Collins & Craig W. Meinhardt & Douglas M. Lemon & Marcia D. Gillette, 1988. "OR Practice—The Army Manpower Long-Range Planning System," Operations Research, INFORMS, vol. 36(1), pages 5-17, February.
    10. Valls, Vicente & Pérez, Ángeles & Quintanilla, Sacramento, 2009. "Skilled workforce scheduling in Service Centres," European Journal of Operational Research, Elsevier, vol. 193(3), pages 791-804, March.
    11. R. N. Burns & G. J. Koop, 1987. "A Modular Approach to Optimal Multiple-Shift Manpower Scheduling," Operations Research, INFORMS, vol. 35(1), pages 100-110, February.
    12. Kay-Yut Chen & Leslie R. Fine & Bernardo A. Huberman, 2004. "Eliminating Public Knowledge Biases in Information-Aggregation Mechanisms," Management Science, INFORMS, vol. 50(7), pages 983-994, July.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Berk, Lauren & Bertsimas, Dimitris & Weinstein, Alexander M. & Yan, Julia, 2019. "Prescriptive analytics for human resource planning in the professional services industry," European Journal of Operational Research, Elsevier, vol. 272(2), pages 636-641.
    2. Chidambaram Subbiah & Andrea C. Hupman & Haitao Li & Joseph Simonis, 2023. "Improving Software Development Effort Estimation with a Novel Design Pattern Model," Interfaces, INFORMS, vol. 53(3), pages 192-206, May.
    3. Zhang, Yucheng & Xu, Shan & Zhang, Long & Yang, Mengxi, 2021. "Big data and human resource management research: An integrative review and new directions for future research," Journal of Business Research, Elsevier, vol. 133(C), pages 34-50.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lawrence Choo & Todd R. Kaplan & Ro’i Zultan, 2019. "Information aggregation in Arrow–Debreu markets: an experiment," Experimental Economics, Springer;Economic Science Association, vol. 22(3), pages 625-652, September.
    2. Dai, Min & Jia, Yanwei & Kou, Steven, 2021. "The wisdom of the crowd and prediction markets," Journal of Econometrics, Elsevier, vol. 222(1), pages 561-578.
    3. David V. Budescu & Boris Maciejovsky, 2005. "The Effect of Payoff Feedback and Information Pooling on Reasoning Errors: Evidence from Experimental Markets," Management Science, INFORMS, vol. 51(12), pages 1829-1843, December.
    4. Hedtrich, F. & Loy, J.-P. & Müller, R.A.E., 2010. "Prognosen auf Agrarmärkten: Prediction Markets – eine innovative Prognosemethode auch für die Landwirtschaft?," Proceedings “Schriften der Gesellschaft für Wirtschafts- und Sozialwissenschaften des Landbaues e.V.”, German Association of Agricultural Economists (GEWISOLA), vol. 45, March.
    5. Keser, Claudia & Markstädter, Andreas, 2014. "Informational asymmetries in laboratory asset markets with state-dependent fundamentals," University of Göttingen Working Papers in Economics 207, University of Goettingen, Department of Economics.
    6. Vernon L. Smith, 2003. "Constructivist and Ecological Rationality in Economics," American Economic Review, American Economic Association, vol. 93(3), pages 465-508, June.
    7. Andrea Albertazzi & Friederike Mengel & Ronald Peeters, 2021. "Benchmarking information aggregation in experimental markets," Economic Inquiry, Western Economic Association International, vol. 59(4), pages 1500-1516, October.
    8. Claudia Keser & Andreas Markstädter, 2014. "Informational Asymmetries in Laboratory Asset Markets with State-Dependent Fundamentals," CIRANO Working Papers 2014s-30, CIRANO.
    9. RYan Oprea & David Porter & Chris Hibbert & Robin Hanson & Dorina Tila, 2008. "Can Manipulators Mislead Prediction Market Observers?," Working Papers 08-01, Chapman University, Economic Science Institute.
    10. Thomas S. Gruca & Joyce Berg & Michael Cipriano, 2003. "The Effect of Electronic Markets on Forecasts of New Product Success," Information Systems Frontiers, Springer, vol. 5(1), pages 95-105, January.
    11. Peeters, R.J.A.P. & Wolk, K.L., 2014. "Eliciting and aggregating individual expectations: An experimental study," Research Memorandum 029, Maastricht University, Graduate School of Business and Economics (GSBE).
    12. Keser, Claudia & Markstädter, Andreas, 2014. "Informational asymmetries in laboratory asset markets with state-dependent fundamentals," University of Göttingen Working Papers in Economics 207 [rev.], University of Goettingen, Department of Economics.
    13. Nicholas Seybert & Robert Bloomfield, 2009. "Contagion of Wishful Thinking in Markets," Management Science, INFORMS, vol. 55(5), pages 738-751, May.
    14. Dorina Tila & David Porter, 2008. "Group Prediction in Information Markets With and Without Trading Information and Price Manipulation Incentives," Working Papers 08-06, Chapman University, Economic Science Institute.
    15. Joachim R. Groeger, 2016. "The Informational Content of the Limit Order Book: An Empirical Study of Prediction Markets," Papers 1609.03471, arXiv.org.
    16. David M Pennock & Sandip Debnath & Eric Glover & C. Lee Giles, 2012. "Modelling Information Incorporation in Markets, with Application to Detecting and Explaining Events," Papers 1301.0594, arXiv.org.
    17. Charles R. Plott, 2000. "Markets as Information Gathering Tools," Southern Economic Journal, John Wiley & Sons, vol. 67(1), pages 1-15, July.
    18. Lambert, Nicolas S. & Langford, John & Wortman Vaughan, Jennifer & Chen, Yiling & Reeves, Daniel M. & Shoham, Yoav & Pennock, David M., 2015. "An axiomatic characterization of wagering mechanisms," Journal of Economic Theory, Elsevier, vol. 156(C), pages 389-416.
    19. Forsell, Eskil & Viganola, Domenico & Pfeiffer, Thomas & Almenberg, Johan & Wilson, Brad & Chen, Yiling & Nosek, Brian A. & Johannesson, Magnus & Dreber, Anna, 2019. "Predicting replication outcomes in the Many Labs 2 study," Journal of Economic Psychology, Elsevier, vol. 75(PA).
    20. van Bruggen, G.H. & Spann, M. & Lilien, G.L. & Skiera, B., 2006. "Institutional Forecasting: The Performance of Thin Virtual Stock Markets," ERIM Report Series Research in Management ERS-2006-028-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.

    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:43:y:2013:i:2:p:152-169. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.