Developing Talent from a Supply-Demand Perspective: An Optimization Model for Managers

TL;DR

This paper introduces a stochastic optimization model for talent management that helps organizations plan recruitment strategies across multiple roles and channels, considering uncertainties in candidate availability and competencies.

Contribution

It develops a novel mixed integer nonlinear programming model combined with chance-constrained programming for talent planning in complex, uncertain environments.

Findings

Model effectively manages talent recruitment in a global manufacturing context

Incorporating stochastic elements improves planning robustness

Empirical validation demonstrates practical applicability

Abstract

While executives emphasize that human resources (HR) are a firm's biggest asset, the level of research attention devoted to planning talent pipelines for complex global organizational environments does not reflect this emphasis. Numerous challenges exist in establishing human resource management strategies aligned with strategic operations planning and growth strategies. We generalize the problem of managing talent from a supply-demand standpoint through a resource acquisition lens, to an industrial business case where an organization recruits for multiple roles given a limited pool of potential candidates acquired through a limited number of recruiting channels. In this context, we develop an innovative analytical model in a stochastic environment to assist managers with talent planning in their organizations. We apply supply chain concepts to the problem, whereby individuals with specific competencies are treated as unique products. We first develop a multi-period mixed integer nonlinear programming model and then exploit chance-constrained programming to a linearized instance of the model to handle stochastic parameters, which follow any arbitrary distribution functions. Next, we use an empirical study to validate the model with a large global manufacturing company, and demonstrate how the proposed model can effectively manage talents in a practical context. A stochastic analysis on the implemented case study reveals that a reasonable improvement is derived from incorporating randomness into the problem.