  The subject of this paper is to introduce a novel permutation-based
nonparametric approach for the problem of ranking several multivariate
populations with respect to both experimental and observation studies to be
referred to the most useful design such as MANOVA (multivariate independent
samples) and MRCB (multivariate randomized complete block design, i.e.
multivariate dependent samples also known as repeated measures). This topic is
not only of theoretical interest but also have a practical relevance,
especially to business and industrial research where a reliable global ranking
in terms of performance of all investigated products/prototypes is a very
natural goal. In fact, the need to define an appropriate ranking of items
(products, services, teaching courses, degree programs, and so on) is very
common in both experimental and observational studies within the areas of
business and industrial research.
