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• The integrated capacity and production planning problem is studied. • A methodology is proposed which increases problem complexity stepwise. • Deterministic and stochastic MILP models are proposed. • Local (OAT) and global (Sobol') sensitivity analyses are performed. • The methodology is applied to industry derived case studies of multi-stage networks. This work presents a methodology for capacity planning under uncertainty in general multi-stage manufacturing networks. Multiple manufacturing lines are available and the production time on each line must be divided between a set of materials. The methodology generates mixed-integer linear programming models that represent capacity expansions with economy of scale costs functions and production planning details. A deterministic model is solved to create a baseline and the impact of uncertainty is investigated by sensitivity analysis and stochastic programming. The applicability of the methodology is exemplified through two case studies derived from industrial pharmaceutical manufacturing. The methodology identifies bottlenecks that limit supply and, where required, activates, and assigns capacity expansion projects for satisfying demand subject to uncertainty. The methodology determines the best use of existing resources and the location and size of capacity expansions thereby generating a portfolio of recommendations for decision making on integrated planning of capacity and production. [ABSTRACT FROM AUTHOR]