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On flexible Statistical Process Control with Artificial Intelligence: Classification control charts
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AI
DOI：10.1016/j.eswa.2021.116492.png)
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Abstract
En 中文
Statistical methods enable rational and quantitative studies of systems or processes, and support management decisions. Statistical Process Control (SPC) is a standard methodology for measuring, controlling and improving the quality of processes and products through statistics. Presented by Walter A. Shewhart in the 1920's, control charts are among the most popular SPC tools for monitoring processes. However, despite their age, their application in Artificial Intelligence (AI) has only recently begun appearing in the literature. In this paper, we use SPC and AI techniques to present a new process monitoring tool. The proposed classification control chart, which we call class-chart, offers a more robust and flexible alternative to traditional SPC tools. In addition to the ability to recognize patterns and diagnose problems, regardless of the sample scenario, this new approach is capable of performing its monitoring functions on a large scale, predicting market scenarios and processes on large volumes of data. We evaluate the performance of the class-chart by the average run length in extensive numerical simulations. Finally, two real data sets are used to illustrate the applicability of the proposed control chart for classification data.
Keywords:
Average run length
Classification data
Control chart
Machine Learning
Retail market
Journal
IF:
7.5
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2.9W
Citations:
10.2W