Existing Sales and Operations Planning (S&OP) models typically address production, pricing, and risk- management decisions in isolation, limiting their effectiveness under commodity price uncertainty. This paper presents a stochastic mixed-integer linear programming framework that integrates decisions across multiple levels—strategic (network design, supplier selection), tactical (multi-stage production, workforce allocation, inventory positioning), and financial (dynamic pricing with LME pass-through, commodity hedging)—within a unified optimization model for aluminum extrusion manufacturing. This multi-level integration enables coordinated responses that exploit cross-functional trade-offs unavailable to sequential approaches: building inventory during low-cost periods, adjusting prices dynamically with London Metal Exchange movements, and calibrating hedge positions to anticipated demand. Computational experiments demonstrate that the integrated approach improves expected profit by 14%, reduces margin volatility by 62%, and eliminates margin violations under extreme scenarios including pandemic disruptions and commodity price crashes.