PurposeThe purpose of this paper is to propose a new non-parametric phase I control chart for the problem of non-linear profile outlier detection.Design/methodology/approachThe proposed non-parametric method is based on a modified Hausdorff distance, which does not require a restrictive assumption on the form of profiles. By obtaining the distance between each profile and the baseline profile, the authors introduced an iterative optimization clustering algorithm to identify outliers by clustering distances.FindingsThe simulation results show that the proposed method can distinguish outliers for structural changes of non-linear profiles. The authors also present a real industrial case example to highlight how practitioners can implement and make use of the proposed control chart in outlier detection applications, and it achieves higher accuracy in the outlier detection of complex profiles.Practical implicationsThe research results of this paper can be applied to any manufacturing or service system whose quality characteristics are characterized by non-linear profiles. This new approach provides quality practitioners a better decision-making tool for non-linear profile outlier detection.Originality/valueDue to the complexity of real-world applications, the non-linear profiles monitoring problem is yet to be addressed. However, the related research still remains rare. And the authors’ proposed non-linear profile control chart, which does not require a restrictive assumption on the form of profiles, shows its applicability and superiority in simulation study and real-world case.

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This chapter analyzes the social value of the TV drama Entrepreneurial Age through the mining of the audience's comments, so as to provide reference for the TV drama producers in topic selection, casting, and script design. Design/methodology/approach: The research is based on a three-step approach including data crawling, two-dimension data tags, and the random forest algorithm design. Findings: This chapter finds that there are three factors related to demand of TV drama:1) the appearance and acting skill of actors; 2) the closeness between TV plays and real life; 3) whether the topic of TV plays has high attention. Value: Based on the big data of audience comments, this chapter explores the factors that influence the number of TV plays. It provides an important reference for TV drama producers on how to design the plot of TV drama, how to choose actors, and how to create topics.