Zsolt T. Kosztyán, Head of Research at iASK co-authored a brand new article. It was released in the journal titled Results in Control and Optimization in 2026.

Abstract

Risk-based control charts have recently been introduced to address measurement uncertainty. The statistical properties of a risk-based control chart for detecting a shift have not been studied. In addition to the control chart design, performance evaluation is important for detecting changes in the process. In this paper, the effectiveness of a risk-based 𝑋̄ control chart (recently introduced) in the presence of measurement uncertainty is investigated. By utilizing a risk-based model that considers the cost of decision outcomes, the impact of measurement uncertainty on the 𝑋̄ chart’s performance in both in- and out-of-control scenarios is designed and examined. To lessen the risk associated with measurement uncertainty, the Nelder–Mead search technique is employed to find the optimal control limits. The performance metrics include the total decision cost, cost ratio, probability ratio, and average run length. Simulation and real-world data analyses are employed to assess the efficiency of the risk-based chart via various performance metrics. A sensitivity analysis is conducted to identify the constraints and relevance of the risk-based 𝑋̄ chart in statistical process control.

Keywords: Average run length, Control charts, Optimization, Phases I and II, Process shift, Risk-based methodology, Statistical process control

The article with full text can be READ HERE.