Volatile and non-stationary demand, very complex production processes, long production and capacity acquisition leadtimes, and extremely high and rarely reversible capacity acquisition costs make capacity planning in the high-tech industry a particularly challenging managerial task. To cope with demand surges and hedge against its uncertainty, firms can invest in order to expand their capacity for the long-term. For the short-term, however, demand is often fixed due to committed orders, and excess capacity might be available that may be used to build up inventory as a complementary means of hedging against these demand surges and uncertainty. In multi-echelon systems, these decisions need to be made simultaneously and must be aligned over the echelons. We address the strategic problem of determining the optimal inventory buildup and capacity expansion timing and amount across locations of a multi-echelon supply chain, as well as the operational problem of production planning. We model the problem as a two-stage stochastic program (SP), with strategic decisions at the first stage and operational decisions at the second stage. We enumerate the capacity expansion timing and use the deterministic equivalent formulation to optimize the other strategic decisions. Through an extensive numerical study, we provide practical managerial insights into the system behavior.