EWMA-PRIM: Process optimization based on time-series process operational data using the exponentially weighted moving average and patient rule induction method

Currently, many manufacturing companies are obtaining a large amount of operational data from manufacturing lines due to advances in information technology. Thus, various data mining methods have been applied to analyze the data to optimize the manufacturing process. Most of the existing data mining-based optimization methods assume that the relationships between input and response variables do not change over time. However, because it often takes a long time to collect a large amount of operational data, the relationships may change during the data collection. In such a case, the operational data is regarded as time-series data and recent data should be regarded to be more important than old data. In this study, we employed a patient rule induction method (PRIM), which is one of the data mining methods applied for process optimization. In addition, we employed an exponentially weighted moving average (EWMA) statistic to assign a larger weight to the recent data. Based on the PRIM and EWMA, the proposed method attempts to obtain optimal intervals for input variables where current performance of the response is better. The proposed method is illustrated with a hypothetical example and validated through a real case study of a steel manufacturing process.

Automated summary

With advances in information technology, manufacturing companies are collecting large amounts of operational data from manufacturing lines, and data mining methods are being used to optimize the manufacturing process. However, existing methods do not take into account the change in relationships over time. This study proposes a method that combines patient rule induction method and exponentially weighted moving average statistic to optimize the manufacturing process by considering the importance of recent data.