A task group of the JCPDS—International Centre for Diffraction Data (ICDD) was established for the purpose of investigating a methodology which would be applicable for statistical process control monitoring of X-ray powder diffractometers. A procedure for collecting X-ray diffraction data for statistical process control purposes and the incorporation of these data into control charts are presented. The results of this task group show that, through the use of statistical process control methods, noncontrol situations for diffractometers were detected, the causes of these problems were identified, and these problems were corrected and noted on control charts.

The weighted inertial piloting of processes presented in this paper is a break-away in terms of the quality of machined mechanical workpieces compared to the traditional piloting carried out by machine setters. However, sometimes one has a stability problem during the piloting phase due to over adjustment. We propose to study the causes of this instability and to develop an approach in order to correct the problems. Once we have reminded ourselves of the basics of weighted inertial tolerancing, we will propose an approach to identify the most probable decentring. The applications of the concepts discussed in this article were tested over a period of two months. The results are very convincing, especially with the elimination of the phenomenon previously observed in identical conditions.

Statistical Process Control (SPC) methods are extensively used in manufacturing contexts to monitor production processes. This article illustrates their potential utility for monitoring the state of marine ecosystems, using survey indicators. We exemplify the use of one SPC tool, the cumulative sum (CUSUM) control chart, to detect persistent changes in the state of a system as new observations are collected, using simulated and real data. Practical guidelines are given on how the chart parameters can be tuned to achieve an acceptable compromise between the ability to detect anomalies quickly and keeping the risk of false alarms low. The common performance measures associated with control charts depend on some key assumptions being met, and the potential impact of their violation is indicated.