Quality Assurance• Sum of the organized arrangements with the objective of ensuring that products will be of a quality required for their intended use
Good Manufacturing Practices• Is that part of Quality Assurance that aimed at ensuring that products are consistently manufactured to a quality appropriate to their intended use
Quality Control• Is that part of GMP concerned with sampling, specifications & testing, documentation & release procedures which ensure that the necessary & relevant tests are performed & the product is released for use only after ascertaining it’s quality
QA and QC• All those planned • Operational or systematic laboratory actions necessary techniques and to provide activities used to adequate fulfill the confidence that a requirement of product will satisfy Quality the requirements of quality
QA and QC• QA is company • QC is lab based based
Introduction and BackgroundThe concepts of Statistical Process Control (SPC)were initially developed by Dr. Walter Shewhart ofBell Laboratories in the 1920s, and were expandedby Dr. W. Edwards Deming, who introduced SPC toJapanese industry after WWII.After early successful adoption by Japanese firms,Statistical Process Control has now beenincorporated by organizations around the world as aprimary tool to improve product quality by reducingprocess variation
Statistical Process Control• Monitoring quality by application of statistical methods in all stages of production• Such methods are – Based on theory of probability and – Relate qualitative and quantitative characteristics of a product to meet established standards
SQC IS Used• Estimating parameters• Tests of significance• Determining relationship between factors• Making decisions on the basis of experimental evidence
Selection of statistical method• Selection of appropriate method of statistical analysis depends on – Types of data or measurements – Sampling techniques – Design of experiments – Types of sample distribution
SQC has been used to serve• As a base for improved evaluation of materials through more representative sampling technique• As a mean of achieving sharper control in manufacturing processes• To provide logical approach to variations• Evaluation of magnitude of chance variation in quality of a product• Detection of assignable variations of product quality
Procedure• The procedure consists of – Proper sampling – Determine variations in the samples – To draw conclusion to the entire batch from the observed data – The data pattern once obtained may be utilized to predict the limits within which future data can be expected to fall as a matter of chance, and to determine when significant variations in the process have taken place
Data analysis• Data can be analyzed using suitable method of analysis e.g. – T-TEST – Analysis of variance – Inference is based on P value (0.05)
Chance variations• These variations are inevitable because any programme of production and inspection have its unique chance of causes of variations, which cannot be controlled or eliminated and often cant be identified
Assignable variations• These variations can usually be detected and corrected by statistical techniques• Such variations are usually caused by machine in a specific batch or a container
Process Variability• In order to work with any distribution, it is important to have a measure of the data dispersion or spread. This can be expressed by the range (highest less lowest), but is better captured by the standard deviation (sigma).
Why Is Dispersion So Important?• Often we focus on average values, but understanding dispersion is critical to the management of industrial processes. Consider two examples:• If a person puts one foot in a bucket of water (33oF) and one foot in a bucket of water (127oF), on average hell feel fine (80oF), but he wont actually be very comfortable
• If a person is asked to walk through a river and told that the average water depth is 3 feet he might want more information. If he is then told that the range is from zero to 15 feet, he might want to re-evaluate the trip.
Control Limits• Statistical tables have been developed for various types of distributions that quantify the area under the curve for a given number of standard deviations from the mean, which can be used as probability tables to calculate the odds that a given value is part of the same group of data used to construct the histogram• Shewhart found that control limits placed at three standard deviations from the mean in either direction provide an economical trade-off between the risk of reacting to a false signal and the risk of not reacting to a true signal - regardless the shape of the underlying process distribution
• If the process has a normal distribution, 99.7% of the population is captured by the curve at three standard deviations from the mean.• Stated another way, there is only a 0.3% chance of finding a value beyond 3 standard deviations. Therefore, a measurement value beyond 3 standard deviations indicates that the process has either shifted or become unstable (more variability).
• The illustration below shows a normal curve for a distribution with a mean of 69, a mean less 3 standard deviations value of 63.4, and a mean plus 3 standard deviations value of 74.6. Values, or measurements, less than 63.4 or greater than 74.6 are extremely unlikely. These laws of probability are the foundation of the control chart.