Over the past decades, control charts, one of the essential tools in Statistical Process Control (SPC), have been widely implemented in manufacturing industries as an effective approach for Anomaly Detection (AD). Thanks to the development of technologies like the Internet of Things and Artificial Intelligence (AI), Smart Manufacturing (SM) has become an important concept for expressing the end goal of digitization in manufacturing. However, SM requires a more automatic procedure with capabilities to deal with huge data from the continuous and simultaneous process. Hence, traditional control charts of SPC now find difficulties in reality activities including designing, pattern recognition, and interpreting stages. Machine Learning (ML) algorithms have emerged as powerful analytic tools and great assistance that can be integrating to control charts of SPC to solve these issues. Therefore, the purpose of this chapter is first to presents a survey on the applications of ML techniques in the stages of designing, pattern recognition, and interpreting of control charts respectively in SPC especially in the context of SM for AD. Second, difficulties and challenges in these areas are discussed. Third, perspectives of ML techniquesbased control charts for AD in SM are proposed. Finally, a case study of an ML-based control chart for bearing failure AD is also provided in this chapter.

This study proposes an integrated model based on the Risk Perception Attitude (RPA) framework and the Theory of Planned Behavior (TPB) model to investigate behavioral intention toward traveling in times of a health-related crisis. A survey was conducted via online networks of travelers, yielding 338 valid cases. The findings indicate that health risk perception is affected by information search about the Covid-19 disease. The relationship between health risk perception and behavioral intention toward traveling during a health-related crisis is not direct, but indirect via health self-efficacy and attitude about their future trip. The study contributes to understand a cognitive process of tourists’ behavior intention toward traveling in a health-related crisis. Practically, this study’s findings provide tourists, government agencies, tourism marketers, and policy-makers and other tourism stakeholders with important suggestions for tourism recovery during and after the pandemic.

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Investigating the effect of measurement errors on the control chart monitoring the ratio of two normal random variables is an important task to facilitate the use of this kind of control chart in practice. Moreover, a deep insight into the problem can help practitioners to find a way to reduce unexpected impacts of measurement errors on the chart performance. This paper provides a study on the performance of the exponentially weighted moving average control chart monitoring the ratio in the presence of measurement errors. We extend the linear covariate error model applied in previous studies to a more general situation, which makes the study more realistic. The numerical results show that although the precision error and the accuracy error have negative influences on the proposed chart performance when these errors are not large these influences are not significant.