Even a well-developed and validated process can drift over time. Changes in material, machine behavior, or environment can gradually shift performance.
SPC tracks that behavior continuously. It monitors key data points during production and compares them to expected variation, allowing issues to be detected early.
SPC focuses on how a process behaves over time rather than just checking final parts.
It separates two types of variation. Common-cause variation is the normal fluctuation within a stable process. Special-cause variation signals that something has changed and needs attention.
This distinction helps determine whether the process is still in control or beginning to drift.
SPC uses control charts to visualize process behavior.
Measurements like part dimensions, shot weight, or cavity pressure are plotted in sequence. These charts include upper and lower control limits based on statistical variation.
Those limits are typically set at three standard deviations above and below the mean. Values within this range represent normal behavior, while values outside indicate a shift.
Data is collected continuously or at defined intervals during production.
The average and standard deviation are calculated, and control limits are established. Each new measurement is added to the chart, creating a live view of the process.
If points stay within limits and follow a random pattern, the process remains stable. If patterns or trends appear, it signals a change that needs investigation.
SPC identifies issues before they turn into defects.
A single point outside control limits indicates a clear problem. Gradual trends toward a limit suggest the process is drifting. Repeating patterns can point to mechanical or system instability.
Catching these signals early prevents scrap and reduces downtime.
SPC is used to monitor both part quality and machine performance.
Dimensions, weight, and visual attributes are tracked alongside process variables like pressure, temperature, and cycle time. This creates a direct link between what the machine is doing and what the part becomes.
In real use, this means adjustments are made based on data rather than waiting for defects to appear.
When SPC indicates a shift, action follows a structured approach.
The process is paused if needed, the root cause is investigated, and corrections are made before production continues. This keeps the process within its validated limits and prevents further variation.
SPC maintains the conditions that support Cp and Cpk.
While capability metrics show what the process can do, SPC ensures it continues to perform that way over time. The difference shows up in consistency, where validated results are sustained during production.
SPC is integrated across development, validation, and production.
Control charts are established during qualification and carried forward into daily operations. Data is tracked, analyzed, and linked to process parameters, creating a continuous feedback loop.
This keeps the process aligned with its validated state and ensures consistent quality across every cycle.