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A rather common problem in the manufacturing field includes: (i) a collection of objects to be compared on the basis of the degree of some attribute, (ii) a set of judges that individually express their subjective judgments on these objects, and (iii) a single collective judgment, which is obtained by fusing the previous subjective judgments. The goal of this contribution is to develop a new technique that combines the Thurstone’s Law of Comparative Judgment with an ad hoc response mode based on preference orderings. Apart from being relatively practical and user-friendly, this technique allows to express the collective judgment of objects on a ratio scale and is applicable to a variety of practical contexts in the field of manufacturing. The description of the proposed technique is integrated with the application to a practical case study.

Research funding is essential to promote the scientific activity of researchers and the dissemination of their results. Simplifying, funding schemes can be classified in two categories—competitive and non-competitive—with several corresponding advantages and shortcomings, which are widely discussed in the scientific literature. The researchers of Politecnico di Torino (i.e., one of the major Italian technical universities) have recently been funded through a non-competitive research funding, consisting of 14k€ for every single researcher in each of the last three years (i.e., from 2017 to 2019), for a total of 42k€. This somewhat unusual initiative—also called “diffused funding” (DF)—represents an important opportunity to investigate the effects of the relatively large allocation of non-competitive funding to single researchers. In this regard, this paper investigates the effects of the DF on the researchers’ scientific output, according to four dimensions of analysis: publishing productivity, publishing diffusion/impact, journal reputation, and international research relations. Preliminary results do not indicate any improvement in the publication output, at least in the short term.

The authors recently presented a technique (denominated “ZM”) to fuse multiple (subjective) preference rankings of some objects of interest—in manufacturing applications—into a common unidimensional ratio scale Franceschini and Maisano J Intell Manuf, 2019. Although this technique can be applied to a variety of decision-making problems in the manufacturing field, it is limited by a response mode requiring the formulation of complete preference rankings, i.e., rankings that include all objects. Unfortunately, this model is unsuitable for some practical contexts—such as decision-making problems characterized by a relatively large number of objects and field surveys—where respondents can barely identify the more/less preferred objects, without realistically being able to construct complete preference rankings. The purpose of this paper is to develop a new technique (denominated “ZMII”) which also “tolerates” incomplete preference rankings, e.g., rankings with the more/less preferred objects only. This technique borrows the underlying postulates from the Thurstone’s Law of Comparative Judgment and uses the Generalized Least Squares method to obtain a ratio scaling of the objects of interest, with a relevant uncertainty estimation. Preliminary results show the effectiveness of the new technique even for relatively incomplete preference rankings. Description is supported by an application example concerning the design of a coach-bus seat.

The problem of collective decision by design teams has received considerable attention in the scientific literature of engineering design. A much debated problem is that in which multiple designers formulate their individual preference rankings of different design alternatives and these rankings should be aggregated into a collective one. This paper focuses the attention on three basic research questions: (1) “How can the degree of concordance of designer rankings be measured?”, (2) “For a given set of designer rankings, which aggregation model provides the most coherent solution?”, and (3) “To what extent is the collective ranking influenced by the aggregation model in use?”. The aim of this paper is to present a novel approach that addresses the above questions in a relatively simple and agile way. A detailed description of the methodology is supported by a practical application to a real-life case study.

Designing quality-inspection procedures may be difficult for short-run manufacturing processes, due to poor effectiveness of the classical statistical process control (SPC) techniques for these processes. This paper proposes a practical methodology to guide quality designers in selecting the more effective and economically convenient inspection procedures. First, the process of interest is decomposed into a number of steps, in which specific defects can occur. Next, several parameters related to inspection effectiveness and cost are combined into a probabilistic model. The more effective and economically convenient inspection procedures can finally be determined using two specific synthetic indicators. A case study concerning a short-run production of hardness testing machines is presented and discussed.

When evaluating the research output of scientists, institutions or journals, different portfolios of publications are usually compared with each other. e.g., a typical problem is to select, between two scientists of interest, the one with the most cited portfolio. The total number of received citations is a very popular indicator, generally obtained by bibliometric databases. However, databases are not free from errors, which may affect the result of evaluations and comparisons; among these errors, one of the most significant is that of omitted citations. This paper presents a methodology for the pair-wise comparison of publication portfolios, which takes into account the database quality regarding omitted citations. In particular, it is defined a test for establishing if a citation count is (or not) significantly higher than one other. A statistical model for estimating the type-I error related to this test is also developed.

Similarly to the h-index and other indicators, the success-index is a recent indicator that makes it possible to identify, among a general group of papers, those of greater citation impact. This indicator implements a field-normalization at the level of single paper and can therefore be applied to multidisciplinary groups of articles. Also, it is very practical for normalizations aimed at achieving the so-called size-independency. Thanks to these (and other) properties, this indicator is particularly versatile when evaluating the publication output of entire research institutions. This paper exemplifies the potential of the success-index by means of several practical applications, respectively: (i) comparison of groups of researchers within the same scientific field, but affiliated with different universities, (ii) comparison of different departments of the same university, and (iii) comparison of entire research institutions. A sensitivity analysis will highlight the success-index's robustness. Empirical results suggest that the success-index may be conveniently extended to large-scale assessments, i.e., involving a large number of researchers and research institutions.

This paper develops a structured comparison among a sample of European researchers in the field of production technology and manufacturing systems on the basis of two research outputs: scientific publications and patents. Researchers are evaluated and compared by a variegated set of indicators concerning (1) the output of individual researchers and (2) that of groups of researchers from the same country. Whilst not claiming to be exhaustive, the results of this preliminary study provide a rough indication of the publishing and patenting activity of European researchers in the field of interest, identifying (dis)similarities between different countries with regard to their inclination to publishing and patenting. Of particular interest is a proposal for aggregating analysis results by means of maps based on publication and patent indicators. A large amount of empirical data are presented and discussed.

Although composition of bibliometric indicators appears to be desirable, in many cases it may be misleading. After a brief introduction on the properties of scales of measurement, the attention of this communication is focused on a recent composite indicator, the hg-index, suggested by Alonso et al. (Scientometrics 82(2):391---400, 2010). Specifically, hg-index has three major criticalities: (1) the hg scale is the result of a composition of the h- and g-indices, which are defined both on ordinal scales, (2) the equivalence classes of hg are questionable and the substitution rate between h and g may arbitrarily change depending on the specific h and g values, (3) the apparent increase in granularity of hg, with respect to h and g, is illusory and misleading. Argument is supported by several examples.

Evaluating the career of individual scientists according to their scientific output is a common bibliometric problem. Two aspects are classically taken into account: overall productivity and overall diffusion/impact, which can be measured by a plethora of indicators that consider publications and/or citations separately or synthesise these two quantities into a single number (e.g. h-index). A secondary aspect, which is sometimes mentioned in the rules of competitive examinations for research position/promotion, is time regularity of one researcher's scientific output. Despite the fact that it is sometimes invoked, a clear definition of regularity is still lacking. We define it as the ability of generating an active and stable research output over time, in terms of both publications/quantity and citations/diffusion. The goal of this paper is introducing three analysis tools to perform qualitative/quantitative evaluations on the regularity of one scientist's output in a simple and organic way. These tools are respectively (1) the PY/CY diagram, (2) the publication/citation Ferrers diagram and (3) a simplified procedure for comparing the research output of several scientists according to their publication and citation temporal distributions (Borda's ranking). Description of these tools is supported by several examples.

This article analyzes some of the most popular scientific journals in the Manufacturing field from the point of view of four bibliometric indicators: the ISI impact factor (ISI-IF), the Hirsch (h) index-for-journal, the total number of citations and the h-spectrum. h-spectrum is a novel tool based on h, making it possible to (i) identify a reference profile of the typical authors of a journal, (ii) compare different journals and (iii) provide a rough indication of their "bibliometric positioning" in the scientific community. Results of this analysis can be helpful for guiding potential authors and members of the scientific community in the Manufacturing area. Of particular interest is the construction of maps based on h-spectrum and ISI-IF to compare journals and monitor their bibliometric positioning over time. A large amount of empirical data are presented and discussed.