Significant advances have been made in the field of flow cytometry, which is a core data acquisition and analysis platform for measurement and quality control of Cell and Gene Therapies. Uptake of flow cytometry automated (supervised and unsupervised) data analysis software platforms within the clinical and biomanufacturing sectors is mixed, with a lot of operators still preferring manual gating over automation due to lack of trust and resistance to change.<br><br>Recently completed research has investigated the variability of operators when completing manual analysis of flow cytometer data across a range of cell model dimensionality and complexity. Measurement uncertainty toolsets have been uniquely applied and give a more specific definition of variability in these manual analysis scenarios compared to more traditional statistical methods. This has allowed the identification of where measurement variation is potentially introduced, such that continuous improvements can be made to the Flow Cytometer measurement process and hence to the biomanufacture of cell and gene therapies. This research is now to be extended to automated software platforms.<br><br>This new research opportunity aims to investigate the application and development of measurement uncertainty analysis to the performance of flow cytometry automated software analysis platforms, (for example SPADE, FLOCK and SWIFT), to understand their variance in specific data analysis scenarios. This provides the foundation for comparison to manual analysis techniques and to the eventual definition of a full measurement uncertainty budget for the Flow Cytometer process. This will better influence how process control can be better optimised for product quality in cell and gene therapies.