Boosting quality: surveillance and monitoring

Purpose In process monitoring, manufacturers typically employ two main types of control charts: memory-less and memory-type charts. Memory-type charts, such as the Exponentially Weighted Moving Average (EWMA), generally outperform memory-less charts like the Shewhart chart. However, both types have inherent limitations that necessitate various extensions and modifications. With these considerations in mind, this study aimed to develop, present, and validate an optimal scheme for detecting non-conformities within the context of a small lamp and light bulb manufacturer based in the United Arab Emirates. Design/methodology/approach The developed scheme accounts for constraints related to inspection capacity and false alarm rates. Through comparative testing, we demonstrate the relative performance of the proposed optimal weighted Exponentially Weighted Moving Average (wEWMA) chart, which is further validated through a detection effectiveness evaluation. Findings Specifically, our findings indicate that, compared to other EWMA schemes with varying design specifications, the proposed optimal wEWMA control chart outperforms the traditional EWMA chart by 26% and the original wEWMA chart by 17%, based on the Average Number of Defectives (AND). Practical implications The proposed scheme relies solely on data readily available within the case organization, enabling operations managers to swiftly implement corrective actions to eliminate non-conforming items. Furthermore, the organization can integrate non-conformity detection into its broader quality initiatives, allowing the scheme to function as a strategic tool for both quality management and strategy–quality alignment. Originality/value The optimal wEWMA scheme enhances a previously modified EWMA model, which was designed to effectively detect various shifts in the fraction non-conforming (p). To achieve superior overall performance, this study optimizes the sample size (n) and sampling interval (h), factors that were not addressed in earlier research. The proposed optimal wEWMA scheme also holds promise for broader application across other manufacturing sectors, including household consumer goods (e.g. home appliances) and industrial products (e.g. transformers, aluminium tubes, and printed circuit boards). Future research may examine its effectiveness in monitoring multi-attribute characteristics and high-yield processes.