Many sequential planning problems can be represented as a shortest path problem in an acyclic network. This includes all deterministic dynamic programs as well as certain stochastic sequential decision problems. In this article, we identify a large class of shortest path problems for which a general efficient algorithm for the simultaneous solution and detection of minimal forecast horizons is developed. Detection of a such minimal forecast horizons is essential when accurate information regarding various relevant parameters is obtained progressively, i.e., when the initial information is restricted to a limited horizon of future stages only. We describe five classes of planning problems which can be efficiently addressed by the general algorithm. These classes deal with multi-item joint replenishment systems, combined inventory and routing problems, machine scheduling issues, single item stochastic inventory settings and routing problems in the plane and in space.