Strategic Workforce Planning Under Uncertainty

Patrick Jaillet (), Gar Goei Loke () and Melvyn Sim ()
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Patrick Jaillet: Department of Electrical Engineering and Computer Science, Operations Research Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
Gar Goei Loke: Rotterdam School of Management, Erasmus University, 3062PA Rotterdam, Netherlands
Melvyn Sim: NUS Business School, National University of Singapore, Singapore 119245, Singapore

Operations Research, 2022, vol. 70, issue 2, 1042-1065

Abstract: The workforce planning problem of hiring, dismissing, and promoting has been the perennial difficulty of Human Resource (HR) management. To cope with uncertain attrition, we propose a new approach of finding a course of action that safeguards against violating organizational target-meeting constraints, such as productivity, budget, headcount, dismissal threshold, and managerial span of control. As such, this approach leads to a tractable conic optimization model that minimizes a decision criterion that is inspired by the riskiness index of Aumann and Serrano, for which its value can be associated with probabilistic and robustness guarantees in meeting constraints under uncertainty. Additionally, our model departs from the literature by considering employees’ time-in-grade, which is known to affect resignations, as a decision variable. In our formulation, decisions and the uncertainty are related. To solve the model, we introduce the technique of pipeline invariance , which yields an exact reformulation that may be tractably solved. Computational performance of the model is studied by running simulations on a real data set of employees performing the same job function in the Singapore Civil Service. Using our model, we are able to numerically illustrate insights into HR, such as the consequences of a lack of organizational renewal. Our model is also likely the first numerical illustration that lends weight to a time-based progression policy common to bureaucracies.

Keywords: Policy Modeling and Public Sector OR; manpower planning; organizational studies; stochastic; programming (search for similar items in EconPapers)
Date: 2022
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