MODELING UNCERTAIN FORECAST ACCURACY IN SUPPLY CHAINS WITH POSTPONEMENT

We examine a situation where a manufacturer operates in a two‐mode production environment. The first mode could involve overseas vendors and manufacturing facilities. If additional units are later required, the company must use its second mode—more expensive last‐minute domestic vendors and manufacturing sites. We develop a new methodology for analyzing the impact of forecast accuracy on the decision to postpone production. We examine the interaction of forecast accuracy, shortage vs. holding costs, transportation costs and the cost of postponing production in the supply chain of a single product facing uncertain demand. Our model can be used to analyze the cost of important changes, such as increasing forecast accuracy, reducing the cost of backorders, lowering the cost of delaying production, or lowering transportation costs. Our model allows a firm to understand its overall cost structure so that it can accurately evaluate the impact of improved forecast accuracy and lowered costs in the context of postponement.     

Fluctuations in convex models of endogenous growth, II: Business cycle properties

Using ideas from the endogenous growth literature, we present a model of the endogenous determination of productivity growth based on individual worker decisions about human capital investment. We calibrate a version of the model to match long run growth facts from the US and study the business cycle properties of this model. This approach offers improvements along several dimensions over standard exogenous growth methodologies. Most importantly, our stochastic endogenous growth model generates much greater serial correlation in output growth and labor supply volatility relative to its real business cycle counterpart. We conclude that using the extra discipline of reproducing the trend productivity growth features of the data endogenously constitutes an important missing component from the real business cycle approach.