In recent times, composite indicators have gained astounding popularity in a wide variety of research areas. Their adoption by global institutions has further captured the attention of the media and policymakers around the globe, and their number of applications has surged ever since. This increase in their popularity has solicited a plethora of methodological contributions in response to the substantial criticism surrounding their underlying framework. In this paper, we put composite indicators under the spotlight, examining the wide variety of methodological approaches in existence. In this way, we offer a more recent outlook on the advances made in this field over the past years. Despite the large sequence of steps required in the construction of composite indicators, we focus particularly on two of them, namely weighting and aggregation. We find that these are where the paramount criticism appears and where a promising future lies. Finally, we review the last step of the robustness analysis that follows their construction, to which less attention has been paid despite its importance. Overall, this study aims to provide both academics and practitioners in the field of composite indices with a synopsis of the choices available alongside their recent advances.

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OR Insight publishes accessible articles on the practical application of operational research. It aims to be an interesting and stimulating publication that appeals not only to OR practitioners and consultants, but also to managers and non-specialists. OR Insight is an official journal of the OR Society

Six problem formulations exist in the multi-criteria decision analysis (MCDA): choice, sorting, ranking, description, elimination and design problems. The analytic hierarchy process (AHP) is a useful and widespread method for solving choice and ranking problems. However, it is not adapted for sorting problems. Moreover, another practical limitation of AHP is that a high number of alternatives implies a large number of comparisons. This paper presents AHPSort, a new variant of AHP, used for the sorting of alternatives into predefined ordered categories. Furthermore, AHPSort requires far less comparisons than AHP, which facilitates decision making within large-scale problems. In this paper, a real case study for supplier selection is used to illustrate our approach. First, the candidates are sorted with AHPSort within two classes: accepted and rejected suppliers. Then, a single supplier is selected with AHP among the accepted suppliers.