A latent space-based multivariate capability index: A new paradigm for raw material supplier selection in industry 4.0

We present a novel Latent Space-based Multivariate Capability Index ( LSb-MC pk ) aligned with the Quality by Design initiative and used as a criterion for ranking and selecting suppliers for a particular raw material used in a manufacturing process. The novelty of this new index is that, contrary to other multivariate capability indexes that are defined either in the raw material space or in the Critical Quality Attributes (CQAs) space of the product manufactured, this new LSb-MC pk is defined in the latent space connecting both spaces. This endows the new index with a clear advantage over classical ones as it quantifies the capacity of each raw material supplier of providing assurance of quality with a certain confidence level for the CQAs of the manufactured product before manufacturing a single unit of the product. All we need is a rich database with historical information of several raw material properties along with the CQAs. Besides, we present a novel methodology to carry out the diagnosis for assignable causes when a supplier does not score a good capability index. The proposed LSb-MC pk is based on Partial Least Squares (PLS) regression, and it is illustrated using data from both an industrial and a simulation study. • A novel multivariate capability index for assessing suppliers of raw materials. • Provides assurance of quality for the CQAs before manufacturing a single unit. • Unlike traditional capability indexes, the proposed one operates in the latent space. • Provides suppliers ranking, selection and diagnosis of assignable causes.