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

Abstract

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.