Optimal evaluation policies for workforce: a Bayesian stochastic model

Models the situation where the productivity of members of a group, such as a salesforce, is periodically evaluated; those whose performance is sub-par are dismissed and replaced by new members. Individual productivity is modeled as a random variable, the distribution of which is a function of an unknown parameter. This parameter varies across the members of the group and is specified by a prior distribution. In this manner, the heterogeneity in the group is explicitly accounted for. The authors model the situation as a partially observable (Bayesian) stochastic control problem, and use dynamic programming techniques and the appropriate optimality equations to obtain solutions. The authors prove the existence of an optimal policy in the general case. Further, for the case when the sales process can be characterized by a beta-binomial or a gamma-poisson distribution, it is shown that the optimal policy is of the threshold type at each evaluation period, depending only on the accumulated performance up to a given period.<<ETX>>