16 research outputs found
Developing Talent from a Supply-Demand Perspective: An Optimization Model for Managers
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