The Taguchi design method, which uses orthogonal arrays to study the quality of characteristics using only a small number of experiments, produces outstanding outcomes when applied to industrial processes. However, nearly all industrial data is concealed via interactive data transformations, for which Box-Cox, arcsine and logit with computer-based management are proposed. The efficiency of each transformation based on the mean and signal-to-noise ratio was investigated for a different number of replicates and noise levels on the response. A total of four simulated scenarios each with 100 noisy data sets were used to examine the system performance. The numerical results indicate that Box-Cox and arcsine transformations are superior to logit transformations. Moreover, the analytical outcomes from the interactive ranges of transformation parameters via the hybridisation of variable neighbourhood search and particle swarm optimisation methods were a close fit to the natural data. In an actual computer-based application of the interactive data transformation system for a ball swaging process, Box-Cox and arcsine transformations followed the simulated numerical data and provided much more appropriate outcomes closer to the natural data. The target of a gram load control was then met via process parameters with higher levels of ranking results of contribution ratio, as expected from the natural data.