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                                                                                                                                           QA / QC Overview and Capabilities

This section introduces the basis of manufacturing, quality assurance, and quality control, commonly referred to as QA and QC, respectively. In regulated industries, QA and QC describe controlled processes in which data are generated and used to evaluate, troubleshoot, and optimize manufacturing processes.

QA and QC are distinct functions and should not be interchanged or considered equivalent. Together, QA and QC data provide verification that a process is controlled and reproducible and provide documented evidence of the manufacture of products that meet established quality requirements.

Quality Analysis (QA)

Quality Analysis functions during the production process, where observed and measured information is focused on defect prevention, process capability assessment, and variability. As production progresses from one step to another, testing and measurement are used to generate data called key performance indicators (KPIs). These indicators are measurable process parameters used to evaluate variability in the process, equipment, and operation and to determine the state of process control. Testing may include physical, chemical, instrumental, or other process measurements, including Process Analytical Technology (PAT).

KPI General Sequence of Actions:

1. Process Capability and Variability: Statistical Process Control (SPC): control charts, capability indices (Cp, Cpk), trend analysis, and other statistical methods to evaluate variability and conformance to established specifications.

2. Failure and Root-Cause Analysis: Investigation of process deviations and potential failure modes using tools such as Failure Mode and Effects Analysis (FMEA), Root Cause Analysis (RCA), Pareto analysis, fishbone diagrams, and 5-Why analysis.

3. Corrective and Preventive Action (CAPA): Development and implementation of corrective and preventive actions based on identified causes, followed by evaluation of process changes.

4. Process Validation and Continuous Monitoring: Validation of implemented changes followed by continued monitoring of process performance to ensure control, capability, and reproducibility are maintained.       

                                                                                                                                                                         Reference

“A Hierarchical Structure of Key Performance Indicators for Operation Management and Continuous Improvement in Production Systems.”

Kang, N., Zhao, C., Li, J., & Horst, J. A., International Journal of Production Research, 54(21), 6333–6350 (2016).

Topic: Manufacturing KPIs as metrics reflecting operational performance and specifically discusses their continuous monitoring and measurement to quantify manufacturing operations

 

Quality Control (QC)

Quality Control inspects and tests raw materials, intermediates, and finished products against established specifications. Intermediates are outputs from individual process stages. Intermediate and final product specifications are based on stakeholder and customer requirements, respectively, to qualify product acceptance and quality.  QC data follows the same sequence of actions described under QA KPI General Sequence of Actions, with intermediate acceptance by stakeholder at between production stages and final product acceptance by customer requirements.                                                                       

QA/QC requirements are drawn from ISO 9001:2026 (the basis for quality management systems across many industries), ISO/IEC 17025 (laboratory competence),    21 CFR Part 211 for pharmaceuticals, and both ISO 22716 / MoCRA requirements for cosmetics. ISO/IEC 17025 also provides the basis for accreditation of testing and calibration laboratories and can be applied by laboratories conducting testing to demonstrate consistent laboratory practices, technical competence, and adherence to established quality requirements.

Example: Statistical Process Control for QA / QC Data Treatment

  • Statistical Process Control (SPC) uses measured process data to evaluate variation, identify changes in process behavior, and determine whether a manufacturing process remains under statistical control. Measurements collected during production can be evaluated using control charts, process capability indices, and other statistical methods to distinguish expected process variation from changes that may indicate process drift or instability.

  • Process capability analysis extends this evaluation by comparing the observed process distribution with established specification limits. Cp evaluates the width of the process distribution relative to the available specification window, while Cpk also accounts for the location of the process mean within that window. These measurements provide a quantitative means of identifying a process that may still exhibit consistent variation but has begun to drift toward an out-of-specification condition.

  • The following example demonstrates how a process can maintain the same overall variability while a shift in the process mean substantially changes its capability and expected defect rate.

  • Centered Process — Lower Risk
    In Scenario A, the process mean (μ) is centered between the lower specification limit (LSL) and upper specification limit (USL). The natural process variation, represented by the 6σ process width, remains within the specification limits. When the process is centered, Cp and Cpk are equal, because the process mean is equally distant from both specification limits. The remaining distance between the process distribution and the specification limits provides a safety margin against normal process variation.

  • Process Drift — Higher Risk
    In the Scenario B, the process variation remains approximately the same, but the process mean has shifted toward the USL. Because the width of the distribution has not changed, Cp remains essentially unchanged; however, Cpk decreases because the mean is now closer to one specification limit. As the distribution approaches or crosses the USL, the probability of out-of-specification results increases, increasing the expected Defects Per Million Opportunities (DPMO).

The important point shown by the two graphs is that process variability and process centering are separate considerations. A process can maintain the same overall variation while becoming less capable because the process mean has drifted toward a specification limit. This is why Cp and Cpk are evaluated together during QA/QC statistical analysis.

Process Capability Variables

Process capability measurements provide a statistical method for evaluating process variation relative to established specification limits. Cp, Cpk, and Cpr describe different aspects of process capability, while Defects Per Million Opportunities (DPMO) provides a measure of the resulting defect frequency.

· Cp — Process Variation: Compares the total process spread (6σ) with the specification width. Cp measures potential process capability but does not account for whether the process is centered.

· Cpk — Process Capability and Centering: Evaluates process variation together with the position of the process mean relative to the nearest specification limit.

· Cp = Cpk: The process is centered within the specification limits.

· Cpk < Cp: The process has shifted off-center toward one of the specification limits.

· Cpk = 0: The process mean is located at a specification limit. A negative Cpk indicates that the process mean has moved beyond a specification limit.

· Cpk ≥ 1.33: Commonly used as a process-capability benchmark. For a centered process, a Cp/Cpk of 1.33 corresponds to the 6σ process spread occupying approximately 75% of the total specification width.

· Cpr — Specification-Window Consumption: Expresses the process spread relative to the specification width and is the reciprocal of Cp. A Cpr of 0.75 therefore corresponds to a Cp of approximately 1.33.

· DPMO — Defects Per Million Opportunities: Expresses defect frequency as the number of defects expected per one million opportunities for a defect. Increasing process variation or movement of the process mean toward a specification limit increases the probability of nonconforming output and therefore increases DPMO.