It was the first face-to-face ENBIS Annual Conference after the COVID pandemic.ENBIS was founded in 2000 with the aim of facilitating the advancement and application of statistical methods in business and industry. It has always combined theoretical developments with practical applications and has offered an environment for networking among people from academia, industry, and consultancy. The 13 papers contained in this special issue illustrate these principles.All the papers begin with an industrial motivation that can be fault or anomaly detection, optimizing a process like maintenance, learn and/or explain data, and so forth. All of them use simulated data and/or real data coming from case studies to illustrate the developments offered. Machine learning is present in the majority of papers as a main or secondary ingredient of the methodology developed, or as one of the methods tested and compared. We hope you will find them stimulating and inspiring.The issue is opened with two contributions devoted to control charts, a theme that has always been active in the ENBIS community. Demertzi and Psarakis 1 propose a control chart for a process whose characteristic of interest follows a Lindley distribution that is very suitable to model waiting times. Rizzo and Bucchianico 2 propose a control chart based on a generalized linear model for the case where the characteristic to be tracked depends on covariates.The following five articles deal directly with machine learning: the first three in an industrial context, the others for variable selection with a view to explicability. Maculotti et al. 3 objective is to compare different machine learning approaches with the aim of selecting the best one to predict the final quality of laser welds which allows to stick to non-destructive inspection methods as recommended by Industry 4.0. Cacciarelli et al. 4 pose the original question of online active learning (learning algorithms that can actively query the user to obtain a label) in outliers-contaminated data streams; the method consists of both constraining the area of new labels and proposing a robust estimator. Misai et al. 5 mix parametric, non-parametric, and machine learning inference methods to optimize a maintenance policy for a partially observed multi-component process. The following two articles by Calzarossa et al. 6 and Rotari et al. 7 share the same objective of variable selection when using random forest for classification or regression. The paper by Calzarossa et al. 6 focuses on the application of detecting phishing sites based on URL characteristics; variables are selected in two stages: first by a univariate exploratory analysis, then by a Lorenz Zonoid-based selection procedure. Although, at the end, the method is applied to an additive manufacturing case, Rotari et al. 7 is more methodological. They generalize Boruta's algorithm to take into account the correlation between covariates.The following set of four papers are devoted to modeling. After a short theoretical review, Kennet et al. 8 present a case s...
