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Restricted randomised designs are essential in the pharmaceutical synthesis due to the operational restrictions and their cost‐effectiveness compared to entirely randomised designs. Specifically, the split‐plot designs are very effective in reducing the cost of an experiment in the presence of hard‐to‐change factors and/or of multi‐stage processes. In classical designs, replicated runs for pure‐error estimation are commonly employed, but they are rarely used in the more complex setting of restricted randomisation. The reason is that, in practice, experiments in industry rarely can follow all the assumptions/conditions that are included in the methodological papers. In this work, we demonstrate how a split‐plot design based on a Bayesian D$D$‐optimality criterion built to ensure more precise pure‐error estimation of the variance components can be easily adapted to fit the additional needs, the speedy implementation and the restrictions that a real case scenario in industry often imposes. We focus on the practical aspect of how to modify the complex design in a way that keeps/improves the desired qualities and on how to assess impact of changes that are more arbitrary. [ABSTRACT FROM AUTHOR]