Glossary

Statistical process control (SPC)

A method for monitoring process variation with statistical tools to detect instability and support consistent manufacturing output.

Quality, Compliance and Traceability Scrap, Rework and Cost of Poor Quality Reduction Quality, NCR, and Continuous Improvement

Statistical process control (SPC) commonly refers to the use of statistical methods to monitor, understand, and control variation in a process over time. In manufacturing, it is used to distinguish normal process variation from signals that may indicate a shift, drift, special cause, or loss of stability.

SPC is primarily a process-monitoring discipline, not just a final inspection activity. It typically uses data collected during production, such as dimensions, weights, temperatures, torque values, fill volumes, or cycle times, and evaluates that data with tools such as control charts, run rules, and capability-related measures.

SPC includes how data is sampled, plotted, and interpreted for ongoing control of a process. It does not, by itself, guarantee that product meets specification, and it is not the same thing as simple pass/fail inspection. A process can be statistically stable yet still produce output outside specification if it is centered or designed poorly.

How SPC appears in operations

In plant and quality workflows, SPC may be embedded in shop floor systems, quality software, MES, or connected measurement equipment. Operators, technicians, or quality personnel may record measurements at defined intervals, review control charts, and respond when the data shows an out-of-control condition or a non-random pattern.

At a machining center, diameter measurements may be charted every hour to detect tool wear before parts drift out of control.
In packaging, fill-weight data may be monitored to identify a process shift rather than relying only on end-of-line rejects.
In regulated production, SPC records may be retained as part of broader quality evidence, depending on the process and system design.

What SPC includes

Collection of process data over time
Use of control charts or similar statistical monitoring tools
Evaluation of common-cause versus special-cause variation
Defined reactions when statistical signals appear
Support for process understanding and ongoing control

What SPC does not include by itself

Final product release decisions on its own
Calibration or validation of measurement systems
Root cause analysis, although SPC may trigger it
A guarantee of process capability or conformance to specifications

Common confusion

SPC is often confused with acceptance inspection, process capability, and measurement system analysis.

SPC vs. inspection: Inspection checks whether units meet requirements. SPC monitors whether the process behavior remains statistically controlled over time.
SPC vs. process capability: Capability metrics such as Cp or Cpk compare process performance to specification limits. SPC focuses first on whether the process is stable enough for those metrics to be meaningful.
SPC vs. MSA or Gage R&R: MSA evaluates whether the measurement system is reliable enough to trust the data. SPC uses that data to monitor the process.

Related standards and systems context

SPC is widely used within quality management and continuous improvement programs and may be connected to MES, QMS, ERP-integrated quality records, or manufacturing analytics. It is also commonly associated with broader manufacturing and quality frameworks, but the term itself refers specifically to statistical monitoring and control of process variation.

Design tolerances should be revisited when the process is stable and capable enough to show a recurring mismatch between what the design requires and what manufacturing can repeatedly achieve at acceptable cost and risk. Do not use process pressure as a substitute for poor tolerance decisions if it drives rework, inspection burden, yield loss, or unstable control.

How quickly can MES help us contain a defect once scrap is detected?

MES can support near-real-time defect containment, but only if data capture, logic, integrations, and response procedures are already engineered, validated, and maintained. In most regulated plants, practical reaction time is limited less by software speed and more by sensor coverage, routing logic, operator response, and change control.

What are the key cost metrics aerospace executives should watch in MES?

In aerospace, MES cost metrics only matter if they are tied to validated data, traceable events, and existing ERP/PLM/QMS realities. Executives should track total manufacturing cost per unit, conversion and rework costs, schedule-driven penalties, NFF and yield losses, and cost of poor quality, while recognizing that data completeness, integration quality, and long asset lifecycles limit precision.

Can I reuse scrap pattern models across different plants or programs?

Sometimes, but not safely by default. Scrap pattern models can transfer across plants or programs only when process definitions, product mix, routing, equipment behavior, operator practices, and data quality are comparable. In regulated, brownfield environments, reuse usually requires remapping, validation, version control, and monitored rollout rather than direct copy-and-paste deployment.

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