cusum

**CUSUM** is **cumulative-sum process monitoring for detecting persistent mean shifts.** - It accumulates small deviations over time so gradual drifts trigger alarms earlier than pointwise tests. **What Is CUSUM?** - **Definition**: Cumulative-sum process monitoring for detecting persistent mean shifts. - **Core Mechanism**: Running sums of deviations from target levels are compared against decision boundaries. - **Operational Scope**: It is applied in statistical process-control systems to improve robustness, accountability, and long-term performance outcomes. - **Failure Modes**: Incorrect baseline assumptions can trigger frequent false alarms under seasonal variation. **Why CUSUM Matters** - **Outcome Quality**: Better methods improve decision reliability, efficiency, and measurable impact. - **Risk Management**: Structured controls reduce instability, bias loops, and hidden failure modes. - **Operational Efficiency**: Well-calibrated methods lower rework and accelerate learning cycles. - **Strategic Alignment**: Clear metrics connect technical actions to business and sustainability goals. - **Scalable Deployment**: Robust approaches transfer effectively across domains and operating conditions. **How It Is Used in Practice** - **Method Selection**: Choose approaches by uncertainty level, data availability, and performance objectives. - **Calibration**: Set reference and control limits from in-control historical data with false-alarm targets. - **Validation**: Track quality, stability, and objective metrics through recurring controlled evaluations. CUSUM is **a high-impact method for resilient statistical process-control execution** - It is a reliable classic tool for early drift detection in production streams.