Workplace safety and productivity will be enhanced considerably with the development and application of individualized sleep and performance prediction models. These are models that predict an individual's operational performance based on his/her unique sleep schedule, individual sleep requirements, and individual pattern of responses to sleep loss across a variety of cognitive performance domains. Progress in the individualization of such models will occur as the result of integrated efforts based on (a) an expanding understanding of the relevant physiological processes underlying the sleep/circadian/performance interactions as well as (b) novel empirical and statistical approaches. In the present paper, an overview is presented of the state of the art of the individualization of sleep and performance models with sections on current efforts in model integration, the application of Bayesian forecasting techniques to the problem of model individualization, construction of Bayesian confidence intervals for predicted performance, and the problem of generalizability of individualized model predictions – i.e., the problem of using models constructed with performance data from one cognitive domain to predict performance in another cognitive domain. Success in model individualization will ultimately be facilitated by concerted, coordinated efforts involving multiple scientific entities and stakeholders.