Quality data assurance

Blanks, H. S. The Generation and Use of Component Failure Rate Data. Quality Assurance, Vol. 

System Evaluation Subject Management Data Quality Assurance Treatment Dispensing Handling Unexpected Events Data Transformation... 

Pharmaceutical data on dosage form, including product specifications, manufacturing data, quality assurance procedures, stability data, validation procedures and bioavailability studies. 

Fig. 7. Data quality assurance. (A) comparison of wireline-derived porosity and core analysis-derived porosity. There is a good correlation between the two data sets. The intercept on the x axis shows that the wireline porosity data are overestimating porosity by about 0.024. (B) Comparison of petrographically defined quartz and wireline-derived quartz—the correlation is good and is approximately 1 1 with a zero intercept. (C) Comparison of petrographically defined dolomite and wireline-derived dolomite—the correlation is good and has an approximately 1 1 slope with a zero intercept.

Considerations of overall data quality assurance include, data quality, data quality control, standard operating procedures (SOP), good laboratory practices (GLP), and concepts of accuracy, precision, and reliability. Also included are reference methods, standard methods, referee methods, official methods (from organizations or in-depth individual studies), and high-reliability methods. 

Data quality assurance must deal with the aspects of data handling and data management shown in Table 5. 

An important topic in method validation is the quality assurance of the experimental data. Quality assurance (QA) fills multiple functions. The first function operates if the results of validation indicate something is wrong (validation fails). The QA function includes rejection of the analytical results giving instructions to the analyst to carry out method development again. The second function relates to the documentation activity of the method validation. Four important activities carried out by QA are included in this topic ... 

The (logical) access path to the equipment is considered on a very abstract level in K-RAMP. With respect to the control input, aforementioned instructions are applied. In terms of process results (process data, inline metrology data, quality assurance data) K-RAMP does not care about the mapping from the production system s sensors to process parameters but assumes that there is a rather product-independent (see previous section) mapping concept between the production system and the process segments already in place. Therefore, K-RAMP only focuses on parameters which are collected on the level of process segments and does not care about specific production system sensors or sensor busses. 

Comprehensive and flexible collection of production data (process data, inline metrology data, quality assurance data) based on single workpieces (single part tracking) provides sufficient data quality in order to derive accurate correlations. 

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