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Nowadays, companies are increasingly adopting additive manufacturing (AM) technologies due to their flexibility and product customization, combined with non-dramatic increases in per unit cost. Moreover, many companies deploy a plurality of distributed AM centers to enhance flexibility and customer proximity. Although AM centers are characterized by similar equipment and working methods, their production mix and volumes may be variable. The purpose of this paper is to propose a novel methodology to (1) monitor the quality of the production of individual AM centers and (2) perform a benchmarking of different AM centers.

This paper analyzes the quality of the production output of AM centers in terms of compliance with specifications. Quality is assessed through a multivariate statistical analysis of measurement data concerning several geometric quality characteristics. A novel operational methodology is suggested to estimate the fraction nonconforming of each AM center at three different levels: (1) overall production, (2) individual product typologies in the production mix and (3) individual quality characteristics.

The proposed methodology allows performing a benchmark analysis on the quality performance of distributed AM centers during regular production, without requiring any ad hoc experimental test.

This research assesses the capability of distributed AM centers to meet crucial quality requirements. The results can guide production managers toward improving the quality of the production of AM centers, in order to meet customer expectations and enhance business performance.

The paper aims to define a dashboard of indicators to assess the quality performance of higher education institutions (HEI). The instrument is termed SMART-QUAL.

Two sources were used in order to explore potential indicators. In the first step, information disclosed in official websites or institutional documentation of 36 selected HEIs was analyzed. This first step also included in depth structured high managers’ interviews. A total of 223 indicators emerged. In a second step, recent specialized literature was revised searching for indicators, capturing additional 302 indicators.

Each one of the 525 total indicators was classified according to some attributes and distributed into 94 intermediate groups. These groups feed a debugging, prioritization and selection process, which ended up in the SMART-QUAL instrument: a set of 56 key performance indicators, which are grouped in 15 standards, and, in turn, classified into the 3 HEI missions. A basic model and an extended model are also proposed.

The paper provides a useful measure of quality performance of HEIs, showing a holistic view to monitor HEI quality from three fundamental missions. This instrument might assist HEI managers for both assessing and benchmarking purposes. The paper ends with recommendations for university managers and public administration authorities.