Digital transformation of quality control implements real-time monitoring and data driven control of performance of the intelligent manufacturing processes. This is achieved by effective and efficient optimization techniques for minimization of quality costs in statistical process control procedure. The present research introduces two novel optimization techniques namely standard and hybrid versions of VOMMI (Very Optimistic Method of Minimization) to economic design of widely used \(\bar x\) process control chart. To accomplish this, sample size, sampling interval and control limit coefficient of the \(\bar x\) chart are economically optimized for continuous and discontinuous process models in published literature. The performance evaluation of standard and hybrid VOMMI algorithms using 32 sets of cost and process factors from the literature yields effective economic designs of the \(\bar x\) chart for the continuous and discontinuous manufacturing processes. The similar economic designs obtained by standard and hybrid VOMMI algorithms are compared with the economic designs obtained by other algorithms reported in the literature. These comparisons show promising performance of standard and hybrid VOMMI algorithms for accurate and reliable economic designs of the \(\bar x\) chart. The comparison of convergence speed between standard and hybrid VOMMI algorithms reveals superior computational efficiency of hybrid VOMMI to standard VOMMI. The sensitivity of economic designs of the \(\bar x\) chart obtained by efficient hybrid VOMMI algorithm is analysed using resolution IV fractional factorial design and analysis of variance at 5% significance level. The sensitivity analysis identifies the critical cost and process factors for their accurate estimation in effective economic design of the \(\bar x\) chart.
