Key takeaways
Every process varies. Two parts off the same machine are never identical to the micron. Statistical process control is the method for deciding whether a given amount of variation is just the process being itself, or a sign that something has genuinely changed and needs attention. It does this with a control chart: measurements plotted over time, with statistically derived limits.
The goal is not zero variation, which is impossible. The goal is a process that is stable and predictable, so that when it does shift, you notice quickly and for the right reason.
This distinction is the heart of SPC:
Treating common cause as special (reacting to every point) is tampering, and it provably increases variation. Treating special cause as common (ignoring real signals) lets defects through. SPC keeps you on the right side of that line.
A frequent and costly confusion. Control limits are calculated from the process data and describe what the process naturally does. Specification limits come from the customer or design and describe what is acceptable. A process can be in control (stable) yet still produce out-of-spec parts if it is not capable; conversely a process can be within spec but out of control. They answer different questions and must not be drawn on the chart as the same line.
SPC catches a drifting process before it produces a batch of scrap, which is the quality side of the same early-detection logic that drives OEE. It also stops well-meaning operators from over-adjusting a healthy process. Both protect the quality factor that feeds OEE and the loss accounting in production loss analysis.
SPC governs the quality signal; Fabrico connects that signal to the rest of the picture. When a special cause appears, the quality event sits alongside the line's downtime and OEE in one place, so you can see whether the shift coincided with a stop, a changeover, or a material change, and turn it into a tracked investigation. Fabrico is built and hosted in the EU with data residency in mind and is ISO 27001 certified. To connect quality signals to production reality, book a demo.
Teams putting this into practice often review our roundup of the cost of poor quality.
To turn this into a tool decision, see our overview of the quality management software.
It uses control charts to separate normal process variation from a genuine change. That tells you when to investigate (a special-cause signal) and, just as importantly, when to leave a stable process alone rather than over-adjusting it.
Common cause is the inherent noise of a stable process and should not be chased point by point. Special cause is a signal that something specific changed and warrants investigation. Confusing the two leads either to tampering or to missed defects.
No. Control limits are calculated from the process and describe what it naturally does. Specification limits come from the customer and describe what is acceptable. A process can be in control yet out of spec, or in spec yet out of control; they are different things.
Adjusting a stable process in response to normal common-cause variation, as if every off-target point were a problem. It is a well-documented mistake that increases variation rather than reducing it, which is why SPC emphasises acting only on real signals.
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