Rolling Planning Horizons: Error Bounds for the Dynamic Lot Size Model

This is the authors' abstract. We don't add key points for this paper.

A new and tight bound is placed on the error induced by imposing a finite time horizon T on the dynamic lot size model. This bound equals the value of perfect information about the data beyond the time horizon T. With this bound, one can assess the value of using the dynamic lot size model repeatedly on a rolling basis with a time horizon of T periods. When this bound is low the time horizon is long enough. When this bound is not low, it may pay to invest in forecasts of demands for later periods, thereby increasing the time horizon.