Multivariate performance analysis methods: A comparative study

It is crucial than ever to measure manufacturing losses due to non-compliance of customer specifications. To assess these losses, industry is widely using proportion of non conformance PNC for performance evaluation of their manufacturing processes. Various methods have been proposed to estimate PNC for univariate quality characteristics; however estimating an accurate PNC for non-normal multivariate correlated quality characteristics is still a challenge for researchers. In this paper we review fitting Burr XII distribution to continuous positively skewed multivariate data using different search algorithm techniques. The proportion of nonconformance PNC for process measurements is then obtained by using only Burr XII distribution, rather than through the traditional practice of fitting different distributions to real data. We also employ artificial neural network based on Burr XII distribution to estimate PNC. The results based on the proposed methods are then compared with the exact proportion of nonconformance using real data from a manufacturing process. . Using the PNC criterion, the results show that the estimated PNC values obtained based on all three methods; simulated annealing, hybrid and artificial neural network are reasonably close to the actual PNC value. However, the estimated PNC based on the simulated annealing method is the closest to the actual PNC value