The development and validation of a chromatography rate model for an industrial multicomponent chromatographic bioseparation is presented. The model is intended for use in a process scenario to allow specific variables critical to product quality to be studied. The chromatography provides impurity clearance whilst producing a complex product composed of six closely related variants of a dimer protein therapeutic (∼30 kDa), with their monomer subunits in a specific ratio. Impurity removal is well understood, however, achieving the correct monomer subunit ratio can pose a purification challenge. We utilise a stepwise approach to develop a model for studying the effect of feed material variability on product quality. Scale down experiments are completed to quickly generate data for estimating model parameters, before an iterative procedure is employed where the industrial process is used to refine parameters in a sequential manner, until model predictions exhibit satisfactory agreement with experimental data. Final model predictions were in good agreement with experimental product quality (within 3%). The results demonstrate how good understanding of an industrial process can help facilitate model development when an exhaustive description is not required, despite considering a chromatographic bioseparation with crude feed material and challenging purification objectives.