Workforce production planning under uncertain learning rates

In this paper, we consider a product manufacturer that seeks to leverage the potential of human learning to develop the capacity of its workforce and to reduce its costs. Unlike much of the literature in this area, we do not assume that the rate at which individuals learn is known with certainty. We present a two-stage stochastic programming model of the related production planning problem that quantifies the impact of worker assignment decisions to produce through an exponential learning curve which we linearize to yield a mixed integer linear program that can be solved efficiently. With this stochastic program, we perform a rigorously designed computational study and statistical analysis to derive tactics and managerial insights for how an organization should plan its production operations about assignment, cross-training and practicing. Results suggest that explicitly recognizing uncertainty in learning rates would reduce costs and that when dealing with assignment decisions, the leading factor to consider is the mean learning rate. On the other hand, when dealing with cross-training and practicing decisions, the learning rate variance is more predictive. We also assess the impact of explicitly considering stochastic forgetting rates in the productivity curve, finding that in the optimal assignment schedule, workers practice more and always specialize.