The main objective is to develop and test a novel SPC method to ensure the stability of final customer characteristics by controlling the upstream characteristics of manufacturing processes, considering their importance/contribution. The originality of the proposed method lies in the use of predictive models (multivariate regression), whose coefficients are used to weigh the contribution of each upstream characteristic to predict the final characteristics. In the context of continuous improvement environments, the application of different SPC approaches to manufacturing processes were compared. The results showed that the multivariate SPC method based on partial least squares regression was superior to traditional univariate and multivariate SPC methods in terms of the predictive precision to detect downstream faults in customer characteristics. However, the use of multiple linear regression may also be an option, since the identification of what upstream characteristic is causing the out-of-control signal is simpler than that of partial least squares regression, and predictive precision in the case of each of the two methods is comparable in practical terms.

主要目标是开发和测试一种新颖的SPC方法，以通过考虑制造过程的重要性/贡献来控制制造过程的上游特征来确保最终客户特征的稳定性。所提出方法的独创性在于使用预测模型（多元回归），其系数用于权衡每个上游特征的贡献以预测最终特征。在持续改进的环境中，比较了不同SPC方法在制造过程中的应用。结果表明，基于偏最小二乘回归的多元SPC方法在检测客户特征下游故障的预测精度方面优于传统的单变量和多元SPC方法。