Individual customer needs and accelerating technological advances in Industry 4.0 are leading to rapid manufacturing changes, thus industrial buildings need to accommodate constantly evolving production processes. The load-bearing structure acts as crucial limiting factor regarding the building's flexibility. As structural performance is highly linked to other design-disciplines, there is a need for integrated computational solutions allowing for performance-oriented structural design and optimization in early-design stage. In order to address these issues, this paper presents the framework development of an early-stage parametric structural optimization and decision support model for integrated industrial building design. The framework combines architectural, structural, building service equipment and production process planning parameters and evaluates the impact of changing manufacturing conditions on the structural performance, automatically evaluating flexibility metrics to guide the decision-making process towards increased sustainable design. In a case study of ten real industrial construction projects, the interdependencies between discipline-specific data in industrial building design are analysed and collected in a graph data model. The proposed parametric framework is tested on a pilot project from the food production sector. Results validate the efficiency of the framework design and indicate that an optimization of the structural axis grid can save up to 25% of the material demand. A discussion on the results and next steps for further model improvement are presented.