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Causality is important in many engineering applications for the optimization, robustness, and control of manufacturing processes. Randomized experiments have been the conventional tool for establishing and quantifying causal effects. With great advances in sensorics and information technology, the temptation to use an unprecedented amount of observational data for this purpose has been growing. Most classically trained data scientists are warned against such practice as jumping from correlation to causality is presented as a treacherous leap. Yet causal inference based on observational data has been of great interest in, for example, social and medical studies for which randomized experiments can be infeasible or even unethical. In this Quality Quandaries, we propose a compromise where observational data, with the help of process expertise, is used to establish the set of factors that will be further tested for causality. We demonstrate a practical application of our proposal through a case study in additive manufacturing. [ABSTRACT FROM AUTHOR]

We present an overview of predictive maintenance (PdM) in industrial operations, highlighting its evolution, benefits, challenges, and potential economic impact. It is assessed that there are considerable benefits associated with PdM but also its practical implementation can prove difficult due to the uncertain benefits of its application, the need for advanced IT infrastructure as well as expert personnel, and, perhaps most importantly, a lack of failure-related data for the PdM model training. We conclude with three PdM case studies to elaborate on some of the issues discussed throughout the paper. [ABSTRACT FROM AUTHOR]

The analysis of split plot experiments can be challenging due to a complicated error structure resulting from restrictions on complete randomization. Similarly, standard visualization methods do not provide the insight practitioners desire to understand the data, think of explanations, generate hypotheses, build models, or decide on next steps. This article demonstrates the effective use of trellis plots in the preliminary data analysis for split plot experiments to address this problem. Trellis displays help to visualize multivariate data by allowing for conditioning in a general way. They can also be used after the statistical analysis for verification, clarification, and communication. [ABSTRACT FROM PUBLISHER]

The article offers the author's insights on the statistical analysis of categorical data. The author discusses the usage of kappa statistic in agreement studies based on the comprehensive review of Professor Jeoren de Mast and colleagues. The author mentions that de Mast also presents the confusing behavior of using kappa statistic in fictitious datasets.

In many industrial applications, autocorrelated data are becoming increasingly common due to, for example, on-line data collection systems with high-frequency sampling. Therefore, the basic assumption of independent observations for process capability analysis is not valid. The purpose of this article is to compare decision methods using the process capability index Cpk when data are autocorrelated. This is done through a case study followed by a simulation study. In the simulation study the actual significance level and power of the decision methods are investigated. The outcome of the article is that two methods appeared to be better than the others. [ABSTRACT FROM AUTHOR]

The article demonstrates how to characterize the behavior of a chemical manufacturing process using a time series model to control and adjust the manufacturing process. The authors discuss a different interpretation of the word control, and consider the situation where the process is adjusted whenever data coming from the process indicates that the process is too far from a given target value. They describe the idea behind time series control of a process and proceed more intuitively and use the chemical process as an illustrative example.

The article analyzes the effect of a drastic change to the regular behavior of a process that interrupts the quality engineers from analyzing the input-output relationships. This change occurs when operators make significant changes while operating a process. This phenomenon is illustrated by observing changes in the temperature profile of a ceramic furnace. These changing patterns are explained by graphical patterns in scatter plots that are necessary to understand the process behavior.

Computers are increasingly employed to monitor the performance of complex systems. An important issue is how to evaluate the performance of such monitors. In this article we introduce a three-dimensional representation that we call a “waterfall chart” of the probability of an alarm as a function of time and the condition of the system. It combines and shows the conceptual relationship between the cumulative distribution function of the run length and the power function. The value of this tool is illustrated with an application to Page's one-sided Cusum algorithm. However, it can be applied in general for any monitoring system. [ABSTRACT FROM AUTHOR]

*Edited by George Box and Søren Bisgaard [ABSTRACT FROM AUTHOR]

An introduction to the journal is presented in which the guest editor discuss contributions of various researchers and practitioners to the Second Stu Hunter Conference held in Tempe, Arizona.