QA vs. QC, Quality Control vs. Quality Management: What’s the Difference?


QA QC In a company organized properly, quality assurance resides independent of manufacturing and operations, and quality control resides within manufacturing and operations.  But what is the difference?

In theory, the difference is that quality assurance activities are proactive and intended to prevent the production of non-conforming products. Quality control activities are reactive, intended to detect and set aside non-conforming products using inspection and testing mechanisms.

In practice, the difference is that quality assurance sets the rules and standards to achieve product quality, and quality control inspects and tests the product against those pre-set rules and standards.

Using the analogy of a college campus representing a medical device / pharma company, quality assurance would be the city’s government and administration and quality control would be the assigned police officers that patrol the college campus.  The assigned police officers (i.e. quality control) are tasked with enforcing the laws and ordinances that the city’s government and administration (i.e. quality assurance) set, within the territory of the college campus (i.e. company manufacturing operations).  If the city’s government and administration (i.e. quality assurance) was a missing piece and the laws and ordinances were set and enforced by the police officers (i.e. quality control) assigned to the territory of each college campus, consistency in the lawfulness and justice at all college campuses within the city of Chicago would at the least not be able to be guaranteed, and at the most differ considerably.

There is no such thing as a non-independent quality assurance group.  Independence from manufacturing operations and adequate authority is what validates quality assurance as quality assurance, according to FDA and ISO 13485 regulations.  Organizational freedom or independence does not necessarily require quality assurance to be a stand-alone group; however, there must not be any conflict of interest.  Typically, it is very challenging to remove all conflict of interest for quality assurance personnel without separating these individuals into a stand-alone group or organizational position. 

This article is related to the Toolkit:
25 Free Resources to Boost Your Quality Management System
To get the full details, please view your free Toolkit.

There are many conflicts of interest that come to mind when quality assurance is not its own group apart from manufacturing/operations or development.  The following table lists some of these conflicts of interest and illustrates the conflict using an example situation.


Conflicts of Interest

Example Situation

Reporting structures, department metrics and performance goals / indicators

Quality assurance personnel report to chief operations oficer. A key performance indicator (KPI) for operations is the quantity of product meeting First-Pass-Acceptance.

 

Quality assurance may be involved with this goal by providing visual inspection training to materials receiving personnel so that there are less raw materials or components accepted that have defects that cause the assembled device to be rejected during in-process or final testing. This involvement is not a conflict – if quality assurance does well at the task of providing visual inspection training to materials receiving personnel by making it the very effective and robust, it will help support the goal of increasing first-pass acceptance rates.

 

However, quality assurance is also responsible for choosing AQL inspection levels for product attributes. Quality assurance has an interest in reducing the number of non-conforming products that end up in consumers hands in order to prevent customer complaints, required reporting to regulatory authorities and adverse regulatory actions such as Recalls. If quality assurance does well at the task of choosing AQL inspection levels that give higher confidence that non-conforming product is not released from manufacturing (which require inspection of a larger sample size versus a smaller sample size), it will likely hurt operations chance of meeting the goal of increasing first-pass acceptance rates. This is a clear conflict of interest… Quality assurance would have to not do well at the task of choosing AQL inspection levels that give higher confidence that non-conforming product is not released from manufacturing in order to help support the goal of increasing first-pass acceptance rates.

 

Maintaining an unbiased approach when making quality decisions

Quality assurance personnel are routinely involved in meetings that manufacturing holds with Supplier A. Individuals from Supplier A that participate in these meetings often informally discuss concerns and doubts about their processes, personnel or resources. Because of this, the quality assurance personnel potentially have cause or concern to impose stricter quality standards on Supplier A due to informal gossip or opinions that may not be substantiated and not validated by data relevant to the supplier’s products. Imposing stricter quality standards on this supplier amounts to dedication of resources, including choosing to conduct an on-site audit of this supplier as opposed having them fill out the routine supplier survey.

 

Supplier B is currently meeting performance goals for supplier incoming inspections, and Supplier C is currently exceeding performance goals for supplier incoming inspections. Quality assurance chose to send these suppliers the routine supplier survey due to the biased determination to audit Supplier A on-site. If quality assurance did not have bias in choosing Supplier A, the chances and decision of the on-site audit being given to Supplier B or Supplier C would have been fair.

 

As it turns out, an on-site audit of Supplier B would have exposed the fact that equipment being used at their manufacturing facility is past its calibration expiration date and in fact out of spec, causing the material they supply to the company to be out of specification for an attribute that is not inspected or tested during incoming inspection, but rather later on in the manufacturing process during in-process device testing. Also as it turns out, an on-site audit of Supplier C would have exposed the fact that this supplier is exceeding performance goals because they are falsifying records and do not have adequate identification and traceability procedures. The material received by this supplier has biocompatibility requirements and the incoming inspection includes review of the applicable ISO 10993 testing records and certificates, which were falsified. The material they supply is a variation of the material specified in the design, and was not tested per ISO 10993. Because of their inadequate identification and traceability procedures, they assigned it the same part number as the original material and did not identify the design change in the product’s name or description.

 

Case Study: Conflicting Interests in Selection of Statistical Techniques

The table below is a deeper dive into the conflicting interests relating to quality assurance’s responsibility of selection of statistical technique used for design verification testing (DVT).  As you can see, development and manufacturing/ops have different interests when the listed considerations are analyzed.  If quality assurance were to be incorporated into either the development organization or the manufacturing/operations organization, they would not have the required independence to make the appropriate decision. 

 

 

Development

Manufacturing / Ops

Considerations

Interest

Corresponding Statistical Technique

Interest

Corresponding Statistical Technique

Amount of Data Required

Choose statistical technique with minimal amount of data required to ensure adequate resources and due dates are met for test report writing

Descriptive Statistics (mean, SD, Range, etc.)

 

or

 

Hypothesis Testing

Use of design verification testing to gain characterization data on product attributes to use for automated testing systems / equipment used within manufacturing

Graphical Methods

(Scatter/Line Plots, Pareto Analysis, Frequency Over Time Plots, etc.)

Sample Size

Minimal sample size or alignment of sample size to development budget for prototypes and engineering builds

Hypothesis Testing

 

or

 

Acceptance Sampling Plans (for minor/ negligible defect severities with higher AQLs)

 

or

 

Design of Experiments

Larger sample size to achieve greater predictability of manufacturing acceptance / rejection rates

Confidence Levels or Reliability Levels (high)

 

or

 

Acceptance Sampling Plans with lower AQLs

 

or

 

Analysis of Variance (ANOVA)

Cost of correcting a nonconforming product

Cost to reputation - state of the art / design excellence

Design of Experiments

 

or

 

Acceptance Sampling Plans with lower AQLs

 

or

 

Confidence Levels or Reliability Levels (high)

Avoidance of production shutdowns or recalls

Acceptance Sampling Plans with lower AQLs

 

or

 

Confidence Levels or Reliability Levels (high)

 

Specification Changes

Developing precise specifications according to materials and technology

Graphical Methods

(Scatter/Line Plots, Pareto Analysis, Frequency Over Time Plots, etc.)

 

or

 

Design of Experiments

 

or

 

Analysis of Variance (ANOVA)

Manufacturing equipment needed to test updated specifications (abilities of current DMR) and impact to process validations

Descriptive Statistics (mean, SD, Range, etc.) – can be used to evaluate Descriptive Statistics of manufacturing equipment

Supplier Performance / Supplier Evaluation – qualification of supplier for updated specifications or updated requirements (potential impacts to environmental monitoring, equipment needs, etc.)

Descriptive Statistics (mean, SD, Range, etc.) – can be used to define requirements for suppliers or requirements for incoming materials

 

Now that we understand that independence is a must in order for quality assurance to assure product quality, let’s clarify the responsibilities of both quality assurance and quality cntrol and how both these functions are intended to successfully interact with the organization. 

Quality assurance is traditionally tasked with the following responsibilities, which are intended to be proactive and prevent the production of non-conforming products.

 

Quality Assurance Responsibilities

Interactions and Responsibilities of Other Departments

Document Control

 

Change Control

 

Non-Conforming Material – Investigation & Disposition

 

Design Control

 

Software Release

 

Supplier Evaluation and Monitoring

 

Supplier Corrective Action

 

Internal Quality Audits

 

Inspection Sampling Plan Development

 

Corrective and Preventive Action

 

Quality System Management Review

 

Quality Records Control

 

Selection of Statistical Techniques

 

Customer Complaints

 

Environmental Control

 

Label Control

 

Final / Finished Product Release

 

Release of Sterilized Product

 

Device Master Record

 

Device History Record

 

Quality Trending

 

 

Quality control is traditionally tasked with the following responsibilities, which in practice are reactive and intended to detect and set aside non-conforming products using inspection and testing mechanisms.

 

Quality Control Responsibilities

Interactions and Responsibilities of Other Departments

Material Inspection

 

Material Handling and Storage

 

Non-Conforming Material – Inspection and Segregation

 

Returned Good Authorization

 

Control & Calibration of Monitoring & Measuring Devices

 

In-Process Product Inspection and Testing

 

Final Product Inspection

 

Environmental Monitoring

 

Labeling Inspection

 

 


As a regulatory affairs professional, Kim Washburn’s work has covered the U.S., European Union, Canada and over 70 global countries. She brings more than 15 years of experience in the medical device, in-vitro diagnostics, pharmaceutical and biologics industries. Her areas of expertise include regulatory approval and new product development support, regulatory labeling, medical device software, FDA compliant design control and risk management, and technical writing. Prior to consulting, she held roles at Abbott Laboratories, OrthoSensor, Nipro Diabetes Systems, and University of Miami Tissue Bank. Kim received a BS in Biology/Computer Science from Illinois State University, after transferring from West Point (United States Military Academy). She can be reached at kimberleewashburn@gmail.com.