Ensemble EWMA control charts for monitoring prioritized shift regimes

Traditional control charts such as Shewhart, EWMA, and CUSUM have limitations in simultaneously detecting both small and large process shifts efficiently. This article introduces an ensemble EWMA (E-EWMA) chart that addresses these issues by combining several EWMA components into a unified statistic with a single decision threshold, simplifying practical implementation and interpretation. The proposed chart is developed for Phase II monitoring under standard in-control parameter assumptions, and it applies to either individual observations or subgroup summaries (such as X¯) when observations are independent over time. The numerical comparisons based on average run length, conditional expected delay, immediate signal proportion and weighted shift regime objectives show that the E-EWMA chart can improve the detection tradeoff when several separated shift regimes are important. The results also show that a single optimized EWMA remains preferable in some small shift dominated settings, which clarifies when the additional ensemble structure is warranted. The article also details practical design considerations, including robust optimization of smoothing parameters and component weights via a gradient-boosted surrogate model and differential evolution algorithm. Further, the impact of Phase 1 parameter estimation uncertainty on chart reliability is evaluated, emphasizing recalibration strategies to maintain performance. A custom-built interactive web application is provided that guides practitioners step-by-step through the design and optimization of E-EWMA chart. Application to real-world data from a screw-driving process illustrates the practical value of the E-EWMA approach, confirming its effectiveness in rapidly identifying shifts of varying magnitudes.