The Kalman filter also assumes every data point conveys a signal of change, however small. That approach inevitably leads to reading tea leaves (interpreting noise as signal).

It is more economical, in my experience, to start from the idea that unless there's been an actual consistent change in the process, any data you're getting reflects noise around the process mean. SPC is designed to tell those two cases apart.