statistical-quality-control ppt.pptx pharmaceutical analysis

analysis

1. STATISTICAL QUALITY CONTROL Chalapathi Institute of Pharmaceutical Sciences

2. St atistical quality control is defined as an economic and effective system of maintaining and improving the quality of outputs throughout the whole operating process of specification, production and inspection based on continuous testing with random samples. The principle of statistical quality control covers almost all the aspects of production like raw materials, machines and management. Objectives: The fundamental objective of statistical quality control is to find The extent to which the product fulfil the specifications and if there are variations. The cause of variation. The limits of variation.

3. Classification of Statistical Quality Control: Ø Quality control covers all factors of production. They will be classified in the following way:
1. Quality of material
2. Quality of manpower
3. Quality of machines
4. Quality of management 1. Quality of material: Good quality materials will be used to produce better finished products. The processing will be smooth thereby reducing waste and increasing the output. 2. Quality of manpower: If the production person is trained and qualified then there will be increased efficiency and less cost of production.

4. 3. Quality of machines: If the equipment is of good quality and up-to-date, then there will be efficient work and scarify of breakdowns. 4. Quality of management: Good management will result in efficiency, better in relations and growth of business and markets. Advantages of Statistical Quality Control: It provides a means of detecting error at inspection. It improves the relationship with the customer. It reduces inspection costs. It reduces the number of rejects and saves the cost of material. It provides a basis for attainable specifications. It provides a means of determining the capability of the manufacturing process. It promotes the understanding and appreciation of quality control.

5. Types of Quality Control: Ø Statistical Quality control can be divided in to three broad categories:
1. Descriptive statistics
2. Statistical process control
3. Acceptance sampling 1. Descriptive statistics: Ø Descriptive statistics are used to describe quality characteristics and relationships. Ø The most important descriptive statistics are measures of central tendency such as the mean, measure of variability such as the standard deviation and range, and measure of the distribution of data. 1. The Mean: Ø A statistic that measures the central tendency of a set of data. Where
X̅ = The mean.
X i = Observation i = 1,2, …… .n.
n = Number of observations.

6. 2. The Range and Standard deviation:
Range: Ø The difference between the largest and smallest observations in a set of data. Standard deviation: Ø A statistic that measures the amount of data dispersion around the mean. Where
σ = Standard deviation of a sample.
X̅ = The mean.
X i = Observation i = 1,2, …… .n.
n = Number of observations in the sample.

7. 3. Distribution of data: Ø A third descriptive statistic used to measure quality characteristics is the shape of the distribution of the observed data. Symmetric distribution: Ø There are the same number of observations below and above the mean. Ø This is what we commonly find when only normal variation is present in the data. Skewed distribution: When a disproportionate number of observations are either above or below the mean. Difference between symmetric and skewed distribution

8. 2. Statistical Process Control: Ø Statistical process control involves inspecting a random sample of the output from a process and deciding whether the process is producing products with characteristics that fall within a predetermined range. Cause of Variations: The variations in the quality of products may be due to two causes. 1. Chance or Random causes: · There are some variations which are natural in the manufacturing process and cannot be removed or prevention in any way (Allowable variations). 2. Assignable causes: · Variations due to specific causes like machine faults, inexperienced workmen, wornout tools, defective raw material are called assignable causes.
The elimination of assignable variations is known as “bringing the process under control”.

9. The major tools of statistical process control are:
1. Histogram
2. Pareto chart
3. Cause-and-effect diagram
4. Flow chart
5. Control chart
6. Scatter diagram
7. Check sheet

10. 1. Histogram: Ø A histogram is a graphical representation of the distribution of numerical data. Ø It differs from a bar graph, as a bar graph relates two variables, but a histogram relates only one.

11. 2. Pareto chart: Ø Pareto chart, named after Wilfredo Pareto, is a type of chart that contains both bars and a line graph, where individual values are represented in descending order by bars, and the cumulative total is represented by the line. Ø The Pareto principle states that, for many events, roughly 80% of the effects come from 20% of the causes.

12. 3. Cause-and-effect diagram: It is also known as fishbone diagram or ishikawa diagram. It identifies major causes and breaks them down into sub-causes and further sub-divisions (if any). It is usually preceded by cause-and-effect analysis. This diagram is useful to help organize ideas and to identify relationships. Fishbone diagram is a visual tool used to identify, explore and graphically display all the possible causes related to a problem to discover root causes. The design of the diagram looks much like a skeleton of a fish.The usual approach to a fishbone diagram is to consider four problem areas namely: methods, materials, equipment, and personnel. The effect is usually a particular problem, or perhaps a goal, and it is shown.

13. 4. Flow chart: Ø A flow chart is a type of diagram that represents a workflow or process. Ø Flow chart should be created from left to right or from top to bottom.

14. 5. Control charts: Control charts are one of the most commonly used tools in statistical process control. A control chart is a graphical representation of the collected information. A control chart (also called process chart or quality control chart) is a graph that shows whether a sample of data falls within the common or normal range of variation. The common range of variation is defined by the use of control chart limits. We say that a process is out of control when a plot of data reveals that one or more samples fall outside the control limits. The center line (CL) of the control chart is the mean, or average. The upper limit (UCL) is the maximum acceptable variation. The lower control limit (LCL) is the minimum acceptable variation. Out of control the situation in which a plot of data falls outside preset control.

15. Types of control charts: Ø The different characteristics that can be measure by control charts can be divided into two groups:
1. Variables.
2. Attributes. 1. Variable control charts: Ø A product characteristic that can be measured and has continuum of values ( Ex: height, weight, or volume).
1. Mean (x-bar) charts
2. Range (R) charts

16. 1. Mean (x-bar) charts: A mean control chart is often referred to as an x-bar chart. It is used to monitor changes in the mean of a process. Changes in the process can be detected by these charts. To construct the upper and lower control limits of the chart, we use the following formulas. Where
x̅ = the average of sample means
z = standard normal variable
σ x = standard deviation of the distribution of sample means, computed as
n = sample size

17. 2. Range (R) charts: Ø A control chart that monitors changes in the dispersion or variability of process. Ø Range (R) charts are another type of control chart for variables. Whereas x-bar charts measure shift in the central tendency of the process, range charts monitor the dispersion or variability of the process.
CL = R̅
UCL = D 4 R̅
LCL = D 3 R̅

18. 2 . Attributes control charts: Ø A product characteristic that has a discrete value and can be counted.
1. P-charts
2. C-charts 1. P-Charts: Ø A control chart that monitors the proportion of defects in a sample. Ø P-charts are used to measure the proportion that is defective in a sample.
UCL = p̅ + z σ p LCL = p̅ - z σ p Where z = standard normal variable
p̅ = the sample proportion defective
σ p = the standard deviation of the average proportion defective

19. n = sample size 2. C-Charts: Ø A control chart used to monitor the number of defects per unit. Ex: Number of returned meals in a restaurant. UCL = c̅ + z √ c̅
LCL = c̅ - z √ c̅

20. 6. Scatter diagram: The scatter diagram graph pairs of numerical data, with one variable on each axis, to look for a relationship between them. If the variables are correlated, the points will fall along a line or curve.

21. 7. Check sheet: Ø The check sheet is a simple document that is used for collecting data in real time and at the location where the data is generated. Ø The data it captures can be quantitative or qualitative. Ø When the information is quantitative, the check sheet is sometimes called a tally sheet.

22. 3. Acceptance sampling: This involves random inspection of a sample of goods. Based on the results of the sample, a decision is made as to whether a batch of goods should be accepted or rejected.