CONCEPTS OF SATISTICAL QUALITY CONTROL .

CONCEPTS OF STATISTICAL QUALITY CONTROL

1. QUALITY CONTROL Concept of statistical quality control DR.PRASADARAO MANCHINENI PRINCIPAL M.A.M.COLLEGE OF PHARMACY

2. Concept of statistical quality control Statistical Quality Control (SQC) is a methodology that uses statistical techniques to monitor, control, and improve the quality of a process or product. It involves the use of statistical methods to: 1. Monitor process performance 2. Detect variations or anomalies 3. Identify root causes of problems 4. Implement corrective actions 5. Verify effectiveness of changes

3. Key concepts in SQC : 1. Control Charts: Graphical representations of process performance over time, used to detect deviations from expected behavior. 2. Process Capability: Measures the ability of a process to produce output within specified limits . 3. Statistical Process Control (SPC): Uses statistical methods to monitor and control processes in real-time . 4. Sampling: Selecting a subset of data to represent the entire population. 5. Hypothesis Testing: Statistical methods to test hypotheses about process performance . 6. Confidence Intervals: Estimates of population parameters with a specified level of confidence . 7. Process Improvement: Identifying and implementing changes to improve process performance.

4. SQC aims to : 1. Reduce variability 2. Improve consistency 3. Increase efficiency 4. Enhance customer satisfaction 5. Reduce waste and rework

5. Tools and techniques used in SQC : Tools and techniques used in SQC: 1. Control charts (e.g., X-bar, R, p, np) 2. Histograms 3. Pareto analysis 4. Scatter plots 5. Regression analysis 6. Design of Experiments (DOE) 7. Statistical software (e.g., Minitab, R, Python)

6. Control Charts Control Charts are a fundamental tool in Statistical Quality Control (SQC) used to monitor and control processes over time. They help detect deviations from expected behavior, allowing for swift corrective action. Types of Control Charts: 1. X-bar Chart (Average Chart): Monitors process mean. 2. R Chart (Range Chart): Monitors process variability. 3. p Chart (Proportion Chart): Monitors proportion of defects .4. np Chart (Number of Defects Chart): Monitors number of defects. 5. c Chart (Count Chart): Monitors number of defects per unit. 6. u Chart (Average Count Chart): Monitors average number of defects per unit. 7. Individuals Chart (X Chart): Monitors individual data points .8. Moving Average Chart: Monitors process mean over a specified period .9. Exponential Weighted Moving Average (EWMA) Chart: Monitors process mean with weighted averages.

7. Components of a Control Chart: 1. Center Line (CL): Represents the process mean or expected value. 2. Upper Control Limit (UCL): Upper bound for acceptable variation. 3. Lower Control Limit (LCL): Lower bound for acceptable variation. 4. Control Limits: UCL and LCL together define the acceptable range. 5. Data Points: Individual measurements plotted over time.

8. Interpretation of Control Charts: 1. In-control process: Data points within control limits, indicating stable process. 2. Out-of-control process: Data points outside control limits, indicating instability. 3. Trends: Patterns or shifts in data points over time. 4. Shifts: Sudden changes in process mean. 5. Drifts: Gradual changes in process mean.

9. Control Charts By using Control Charts, organizations can: 1. Monitor process stability 2. Detect deviations quickly 3. Take corrective action 4. Improve process capability 5. Reduce variability 6. Enhance quality

10. Process Capability (Cp) It is a statistical measure in Quality Control that assesses a process's ability to produce output within specified limits or tolerances. It evaluates the process's inherent variability in relation to the desired specifications.

11. Process Capability Key aspects of Process Capability: 1. Cp (Capability Index): Measures the ratio of the specification width to the process's natural variability (6 σ). - Cp = (USL - LSL) / (6 σ) - USL = Upper Specification Limit - LSL = Lower Specification Limit - σ = Process standard deviation 2. Cpk ( Centered Capability Index): Measures the process's ability to produce output around the target value. - Cpk = min [(USL - μ) / (3σ), (μ - LSL) / (3 σ)] - μ = Process mean 3. Pp (Process Performance Index): Measures the process's actual performance. - Pp = (USL - LSL) / (6 σ_ p) - σ_ p = Process performance standard deviation 4. Ppk (Process Performance Capability Index): Measures the process's actual performance around the target value. - Ppk = min [(USL - μ) / (3σ_ p), ( μ - LSL) / (3 σ_ p)]

12. Process Capability Interpretation of Process Capability indices:- Cp ≥ 1.33: Process is considered capable.- 1.00 ≤ Cp < 1.33: Process is considered marginally capable.- Cp < 1.00: Process is considered not capable.- Cpk ≥ 1.33: Process is considered centered and capable.- Pp ≥ 1.00: Process performance is acceptable.

13. Process Capability Process Capability analysis helps organizations: 1. Evaluate process performance 2. Identify opportunities for improvement 3. Set realistic specifications 4. Reduce variability 5. Improve customer satisfaction By understanding Process Capability, organizations can ensure their processes are operating within desired limits, leading to improved quality and reduced waste.

14. Statistical Process Control (SPC) Statistical Process Control (SPC) is a method of monitoring and controlling processes using statistical techniques to ensure consistent quality and detect deviations from expected behavior.

15. Statistical Process Control (SPC) Key components of SPC: 1. Process Monitoring: Continuous collection of data on process performance. 2. Control Charts: Graphical representation of process data over time. 3. Statistical Analysis: Identification of trends, patterns, and anomalies. 4. Corrective Action: Implementation of changes to maintain process stability.

16. Statistical Process Control (SPC) SPC objectives: 1. Detect and correct deviations: Identify and address process shifts or drifts. 2. Maintain process stability: Ensure consistent process performance. 3. Improve process capability: Reduce variability and improve quality. 4. Reduce waste and rework: Minimize defects and errors.

17. Statistical Process Control (SPC) SPC tools and techniques: 1. Control Charts (X-bar, R, p, np, c, u) 2. Histograms 3. Pareto Analysis 4. Scatter Plots 5. Regression Analysis 6. Design of Experiments (DOE) 7. Statistical software (Minitab, R, Python)

18. Statistical Process Control (SPC) SPC implementation steps: 1. Identify critical processes 2. Establish control limits 3. Collect and analyze data 4. Interpret control charts 5. Take corrective action 6. Continuously monitor and improve

19. Statistical Process Control (SPC) Benefits of SPC: 1. Improved quality 2. Reduced variability 3. Increased efficiency 4. Enhanced customer satisfaction 5. Cost savings By applying SPC, organizations can ensure consistent quality, reduce waste, and drive continuous improvement.

20. Sampling Sampling in Statistical Quality Control (SQC) involves selecting a subset of data or units from a larger population to represent the entire group. This allows for: 1. Inspection efficiency: Reduced time and cost 2. Improved accuracy: Increased precision 3. Representative results: Inferences about the population Types of Sampling: 1. Random Sampling: Every unit has an equal chance of selection 2. Stratified Sampling: Division into subgroups (strata) with random sampling within each 3. Systematic Sampling: Selection at fixed intervals (e.g., every 10th unit) 4. Cluster Sampling: Selection of groups (clusters) and random sampling within each 5. Acceptance Sampling: Sampling to determine whether to accept or reject a batch

21. Sampling Sampling Methods: 1. Single Sampling: One sample taken to make a decision 2. Double Sampling: Two samples taken to make a decision 3. Multiple Sampling: More than two samples taken to make a decision 4. Sequential Sampling: Samples taken until a decision is reached Sampling Plans: 1. Single Stage Sampling: One sampling stage 2. Two-Stage Sampling: Two sampling stages with a second stage if necessary 3. Multi-Stage Sampling: More than two sampling stages

22. Sampling Factors affecting Sampling:1. Sample size 2. Sampling frequency 3. Sampling method 4. Population size 5. Variability Benefits of Sampling: 1. Cost savings 2. Time savings 3. Improved accuracy 4. Representative results 5. Data-driven decisions By applying appropriate sampling techniques, organizations can ensure reliable results, reduce costs, and make informed decisions in SQC.

23. Hypothesis Testing Hypothesis Testing in Statistical Quality Control (SQC) is a statistical method used to make inferences about a population based on a sample of data. It involves testing a hypothesis about a population parameter to determine if there is enough evidence to reject or fail to reject the hypothesis.

24. Hypothesis Testing Key concepts: 1. Null Hypothesis (H0): A statement of no effect or no difference. 2. Alternative Hypothesis (H1): A statement of an effect or difference. 3. Test Statistic: A numerical value calculated from the sample data. 4. P-value: The probability of observing the test statistic under the null hypothesis. 5. Alpha (α): The maximum probability of rejecting the null hypothesis when it is true (Type I error). 6. Beta (β): The probability of failing to reject the null hypothesis when it is false (Type II error).

25. Hypothesis Testing Types of Hypothesis Tests: 1. One-sample tests: Compare a sample to a known population parameter. 2. Two-sample tests: Compare two samples to determine if they come from the same population. 3. Paired tests: Compare two related samples (e.g., before and after). Common Hypothesis Tests: 1. Z-test: For large samples, tests means and proportions. 2. T-test: For small samples, tests means. 3. ANOVA (Analysis of Variance): Tests means across multiple groups. 4. Regression Analysis: Tests the relationship between variables.

26. Hypothesis Testing Steps in Hypothesis Testing: 1. State the null and alternative hypotheses 2. Choose a significance level (α) 3. Collect and analyze the data 4. Calculate the test statistic and p-value 5. Make a decision (reject or fail to reject H0)6. Interpret the results

27. Hypothesis Testing By applying Hypothesis Testing, organizations can: 1. Detect changes in processes 2. Evaluate the effectiveness of changes 3. Compare products or processes 4. Make informed decisions 5. Reduce risks and errors

28. Confidence Intervals in Statistical Quality Control (SQC) Confidence Intervals in Statistical Quality Control (SQC) are statistical ranges within which a population parameter is expected to lie with a certain level of confidence. They provide a margin of error around a sample statistic to estimate the population parameter.

29. Confidence Intervals Key concepts: 1. Confidence Level: The probability that the interval contains the population parameter (e.g., 95%). 2. Margin of Error: The maximum distance between the sample statistic and the population parameter. 3. Sample Statistic: A numerical value calculated from the sample data (e.g., mean, proportion). 4. Population Parameter: The true value of the population characteristic (e.g., population mean, population proportion).

30. Confidence Intervals Types of Confidence Intervals: 1. One-sample intervals: Estimate a single population parameter. 2. Two-sample intervals: Compare two population parameters. 3. Prediction intervals: Estimate a future observation. Common Confidence Intervals: 1. Confidence Interval for a Mean: Estimates the population mean. 2. Confidence Interval for a Proportion: Estimates the population proportion. 3. Confidence Interval for a Standard Deviation: Estimates the population standard deviation.

31. Confidence Intervals Steps to construct a Confidence Interval: 1. Choose a confidence level 2. Select a sample 3. Calculate the sample statistic 4. Determine the margin of error 5. Construct the interval

32. Confidence Intervals Interpretation of Confidence Intervals: 1. If the interval does not contain the null hypothesis value, reject the null hypothesis. 2. If the interval contains the null hypothesis value, fail to reject the null hypothesis. 3. The width of the interval indicates the precision of the estimate.

33. Confidence Intervals By using Confidence Intervals, organizations can: 1. Estimate population parameters 2. Compare products or processes 3. Make informed decisions 4. Quantify uncertainty 5. Determine sample sizes

34. Process Improvement Process Improvement in Statistical Quality Control (SQC) involves identifying and implementing changes to processes to improve their performance, efficiency, and quality. It aims to reduce variability, defects, and waste, and to enhance customer satisfaction.

35. Process Improvement Key concepts: 1. Identify opportunities for improvement: Analyze data to find areas for improvement. 2. Define project goals and objectives: Establish clear targets for improvement. 3. Analyze the current process: Map and understand the existing process. 4. Design and test improvements: Develop and pilot new processes or changes.5. Implement and monitor changes: Roll out and track the effectiveness of improvements.6. Continuously evaluate and improve: Regularly assess and refine processes.

36. Process Improvement Tools and techniques for Process Improvement: 1. Flowcharting: Visualize processes to identify inefficiencies. 2. Fishbone diagrams: Identify and organize potential causes of problems. 3. Pareto analysis: Focus on the most significant issues. 4. Root cause analysis: Identify underlying causes of problems. 5. Design of Experiments (DOE): Test and optimize process variables. 6. Lean and Six Sigma methodologies: Systematic approaches to process improvement.

37. Process Improvement Benefits of Process Improvement: 1. Increased efficiency 2. Reduced defects and errors 3. Improved customer satisfaction 4. Cost savings 5. Enhanced competitiveness 6. Continuous learning and improvement

38. Process Improvement By applying Process Improvement principles, organizations can: 1. Streamline processes 2. Eliminate waste 3. Enhance quality 4. Increase productivity 5. Improve employee engagement 6. Drive innovation and growth

39. THANK Q DR.PRASADARAO MANCHINENI PRINCIPAL M.A.M.COLLEGE OF PHARMACY