2014-08-08 13:56:33 -
Have you implemented the data analysis requirements in QSR? If not, you could be wasting money on missed improvements. You also run the risk of an FDA Warning Letter from your next inspection. This webinar gives you the tools and techniques to implement an effective data analysis program.
Device manufacturers often don’t recognize the requirement in 820.100(a)(1) to analyze quality data to identify quality problems. Consequently, there may be unrecognized quality problems. For example, in one company a Warning Letter says that 36 of 210 complaints were for the shipping the wrong product. The company had not identified the problem, investigated it, or taken corrective action. The FDA Investigator uncovered the issue. Similarly, a company could have costly and unrecognized problems
with nonconforming product.
Corrective and Preventive Action is the most frequently cited section in device Warning Letters with data analysis as the frequently cited subsection.
This webinar can you understand the requirements and take action. For example, many companies believe there is a requirement to “trend” the data. According to FDA-CDRH this is only one tool among the statistical analysis techniques. You will learn the ISO recommended set of techniques and the Global Harmonization Task Force guidance document.
Areas Covered in the Session:
The difference between corrective action and preventive action – a pervasive problem
The requirements from QSR and an analysis of the expectations
Data analysis statistical techniques from ISO/TR 10017:2003
The GHTF guidance document for data analysis
Example Warning Letters that illustrate the issues device manufacturers face
In addition, you will receive a checklist to help your improvement project
Who will Benefit:
CA&PA personnel involved in process analysis
Quality Engineers and other involved in the quality system operation
Personnel involved improvement initiatives such as Six Sigma Initiatives
Design Engineers analyzing product performance
Risk Management personnel performing production and post-production data analysis