• Statistical QC methods and decision rules drive detection and error control, with emphasis on integrating Shewhart charts with CUSUM, robustness to outliers, and evaluation of false rejections and detection power [2], [17], [19], [15], [5].

• Off-line QC emphasizes design choices that shape manufacturability and costs: parameter design (Taguchi), experimental design for fabrication, and crafting complete inspection plans to optimize performance [4], [20], [9].

• Economic/management framing treats QC as design and policy: taxonomy of economically based procedures, economic selection of target quality, and quality planning and measurement as strategic activities [3], [9], [14], [12].

• Inspection regimes and policy design examine acceptance sampling, optimal inspection levels, and the impact of inspector fallibility and inspection errors on process control [8], [15], [11], [7].

• Measurement systems and planning across domains emphasize the Quality Measurement Plan, quality planning/analysis, and domain-specific QC like clinical chemistry to ensure reliability [18], [12], [13].