Control charts for monitoring process with time trend

Purpose: Statistical process monitoring has been a relevant practice incorporated into quality management systems. In a controlled process, the variabilities of statistical parameter estimates are expected to fluctuate within a pattern over time. Whenever this pattern is not identified, the root causes must be identified, and the necessary actions should be taken. However, there are situations where special causes are present but not practically significant. Recent studies on profile monitoring have tailored solutions for efficiently detecting trends and seasonality in high-capability processes. This paper proposes using profile curves and statistical modelling based on the location control chart approach. Design/methodology/approach: Our research was carried out using decision-prescriptive models and the Design Science Research approach to identify solutions for real and complex problems. Additionally, the modeling and simulation method was utilized to assist in developing, analyzing, and testing the model, which was classified as an artifact. Findings: The modeling results clearly demonstrate that utilizing location control charts and modified graphs is essential in defining equipment adjustments. This approach guarantees the minimum acceptable capacity and maximizes the use of productive resources. Originality: This study provides novel opportunities for developing and implementing control charts in systems that do not conform to the principles of randomness but rather display intricate temporal patterns. This is particularly relevant in the context of Quality 4.0, where real-time data collection is pervasive.