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Prescriptive analytics for human resource planning in the professional services industry

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  • Berk, Lauren
  • Bertsimas, Dimitris
  • Weinstein, Alexander M.
  • Yan, Julia

Abstract

In this paper, we examine human resource planning decisions made at firms that sell contract-based consulting projects. High levels of uncertainty in deals and revenue forecasts make it challenging for consulting firms to hire the right people to staff their projects. We present a human resource planning model using concepts from robust optimization to allow companies to dynamically make hiring decisions that maximize profit while remaining as flexible as possible, and demonstrate potential profit improvements through simulation on real data.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:272:y:2019:i:2:p:636-641
    DOI: 10.1016/j.ejor.2018.06.035
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    References listed on IDEAS

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    1. 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.
    2. Ben-Tal, Aharon & Chung, Byung Do & Mandala, Supreet Reddy & Yao, Tao, 2011. "Robust optimization for emergency logistics planning: Risk mitigation in humanitarian relief supply chains," Transportation Research Part B: Methodological, Elsevier, vol. 45(8), pages 1177-1189, September.
    3. Bernard Gendron, 2005. "Scheduling Employees in Quebec’s Liquor Stores with Integer Programming," Interfaces, INFORMS, vol. 35(5), pages 402-410, October.
    4. Dimitris Bertsimas & Dan A. Iancu & Pablo A. Parrilo, 2010. "Optimality of Affine Policies in Multistage Robust Optimization," Mathematics of Operations Research, INFORMS, vol. 35(2), pages 363-394, May.
    5. Aharon Ben-Tal & Boaz Golany & Arkadi Nemirovski & Jean-Philippe Vial, 2005. "Retailer-Supplier Flexible Commitments Contracts: A Robust Optimization Approach," Manufacturing & Service Operations Management, INFORMS, vol. 7(3), pages 248-271, February.
    6. Andris A. Zoltners & Prabhakant Sinha, 1980. "Integer Programming Models for Sales Resource Allocation," Management Science, INFORMS, vol. 26(3), pages 242-260, March.
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    Cited by:

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    2. 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.
    3. Luis Arismendy & Carlos Cárdenas & Diego Gómez & Aymer Maturana & Ricardo Mejía & Christian G. Quintero M., 2021. "A Prescriptive Intelligent System for an Industrial Wastewater Treatment Process: Analyzing pH as a First Approach," Sustainability, MDPI, vol. 13(8), pages 1-14, April.

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