The aim of the present work is to provide an integrated decision support approach for the design and scheduling of multipurpose batch plants under demand uncertainty allowing the assessment of alternative risk profile solutions. Based on two-stage mixed integer linear programming (MILP) model, the goal is to maximize the annualized profit of the plant operation under a set of scenarios while minimizing the associated financial risk, evaluated by the Conditional Value at Risk (CVaR) using the augmented ε-constraint method. Considering a literature example, the conclusions highlight the advantages of the proposed approach for the decision-support in industrial plant design and scheduling solutions by considering the explicit risk measure assessment towards expected financial outcomes.