Predictive process monitoring aims to produce early warnings of unwanted events. We consider the use of the machine learning method extreme gradient boosting as the forecasting model in predictive monitoring. A tuning algorithm is proposed as the signaling method to produce a required false alarm rate. We demonstrate the procedure using a unique data set on mental health in the Netherlands. The goal of this application is to support healthcare workers in identifying the risk of a mental health crisis in people diagnosed with schizophrenia. The procedure we outline offers promising results and a novel approach to predictive monitoring.

This study analyzes the effects of performance related pay on productivity exploiting a change in the payment structure of a large Dutch marketing company. Specifically, we investigate the consequences for company sales of higher fixed pay in combination with lower bonuses. Exploiting shift level data of individual workers we find that average productivity decreases when the pay structure shifts more to fixed pay. Further analysis shows that this drop in productivity is larger for older workers and for high-ability employees, while over time the negative effect of the new system becomes smaller.

Because of digitalization, many organizations possess large datasets. Furthermore, measurement data are often not normally distributed. However, when samples are sufficiently large, the central limit theorem may be used for the sample means. In this article, we evaluate the use of the central limit theorem for various distributions and sample sizes, as well as its effects on the performance of a Shewhart control chart for these large non‐normally distributed datasets. To this end, we use the sample means as individual observations and a Shewhart control chart for individual observations to monitor processes. We study the unconditional performance, expressed as the expectation of the in‐control average run length (ARL), as well as the conditional performance, expressed as the probability that the control chart based on estimated parameters will have a lower in‐control ARL than a specified desired in‐control ARL. We use recently developed factors to correct the control limits to obtain a specified conditional or unconditional in‐control performance. The results in this paper indicate that the control chart should be applied with caution, even with large sample sizes.

An open topic within statistical process monitoring is the effect on control chart properties of updating the control chart limits during the monitoring period. The challenge is to use the correct data for updating the control limits as in‐control data could be incorrectly classified as out of control and therefore not used for re‐estimating the parameters, and out‐of‐control data could be classified as in control and therefore used for re‐estimating. In the present article, we study the effect of updating the Shewhart, cumulative sum, and exponentially weighted moving average control chart limits. We simulate different scenarios: the monitoring data could be in or out of control, and the practitioner may or may not be able to find out whether the process is indeed out of control when the control chart gives a signal. The results reveal that the variation in the performance of the conditional control charts decreases significantly as a result of updating the control chart limits when the updating data are in control and also when the updating data are out of control and the practitioner is able to classify correctly data samples that produce a signal. However, when a practitioner is not able to classify a signal correctly, the advisability of updating depends on the type of control chart and the level of data contamination.

In this case study, we demonstrate the use of multilevel process monitoring in quality control. Using high school data, we answer three research questions related to high school student progress during an academic year. The questions are (1) What determines student performance? (2) How can statistical process monitoring be used in monitoring student progress? (3) What method can be used for predictive monitoring of student results? To answer these questions, we worked together with a Dutch high school and combined hierarchical Bayesian modeling with statistical and predictive monitoring procedures. The results give a clear blueprint for student progress monitoring.