Enhancing Six Sigma with latent variable models: An industrial application of multivariate Six Sigma in the automotive sector

[EN] The traditional Six Sigma toolkit presents significant limitations for problem solving in Industry 4.0 settings. To address these limitations, it can be extended with latent variable-based multivariate statistical techniques such as Principal Component Analysis (PCA) and Partial Least Squares (PLS), in what has been referred to as multivariate Six Sigma. In this work, this approach is applied to address vibration performance issues in the caliper, a key component of a car's braking system. By appropriately integrating these techniques into the five-step DMAIC cycle, the root cause was satisfactorily identified, an effective corrective action was implemented, and the objectives of the project were successfully achieved. This case study provides further evidence of the practical utility of multivariate Six Sigma and demonstrates its potential for broader adoption in industrial projects.