Companies are increasingly adopting decentralized manufacturing strategies to manage multiple, geographically scattered manufacturing centers that are characterized not only by similar types of equipment, working methods, and productions, but also by variable mixes and volumes. This trend also applies to additive manufacturing, a well-established technology that allows the flexibility and customization of production to be increased, without significantly increasing the per unit cost. Thus, the need arises to monitor the performance of individual centers in a structured way, and to make practical comparisons of such centers. However, achieving this task is not so straightforward, given the inevitable differences in the characteristics of manufacturing centers and their productions. This paper presents a methodology that can be used to analyze and compare the production performance of a plurality of manufacturing centers from two different viewpoints: (i) quality, through a multivariate statistical analysis of product data concerning conformity with geometrical specifications, and (ii) process sustainability, with the aim of achieving a reduction in energy consumption, carbon dioxide emissions, and manufacturing time, through regression models pertaining to the selected metrics. The proposed methodology can be adopted during regular production operations, without requiring any ad hoc experimental tests. The description of the method is supported by an industrial case study.