Time-Varying Limits in EWMA Monitoring
Because initial points have fewer prior samples to average, control limits expand dynamically during early stages before settling at their asymptotic steady-state width.
Advanced Category: Statistical Process Control (SPC) • ~8 min read
Exponentially Weighted Moving Average (EWMA) Chart
Robust process monitoring for autocorrelated data and early detection of minor process shifts.
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Q:
How does the Exponentially Weighted Moving Average (EWMA) chart work?
EWMA weights past observations with geometrically decreasing exponential weights: Zᵢ = λ Xᵢ + (1 - λ) Zᵢ₋₁ With λ typically set between 0.10 and 0.20, it provides optimal smoothing for non-normal continuous processes.
Smoothing Factor (λ)0.20Weight to current point
Estimated Sigma (σ)0.274Short-Term Inherent Dispersion
Out-of-Control Points6Beyond Time-Varying Limits
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