SPC.pptx, STATISTICAL PROCESS CONTROL,SPCC

SPC,STATISTICAL PROCESS CONTROL, SPC, STATISTICAL PROCES CONTROL

1. Statistical Process Control 1

2. Content Introduction Statistical process control Importance of SPC Quality Measurement and Manufacturing The Shewhart control chart Advantages of statistical control Control Chart 2

3. Introduction [2] Statistical Quality Control (SQC) is the term used to describe the set of statistical tools used by quality professionals. SQC Categories Descriptive Statistics Statistical Process Control Acceptance Sampling 3

4. Descriptive statistics [3] :  are brief descriptive coefficients that summarize a given data set, which can be either a representation of the entire or a sample of a population. Descriptive statistics are broken down into measures of central tendency and measures of variability (spread). Statistical process control (SPC ) [4] : is a method of quality  control  which employs  statistical  methods to monitor and  control  a  process . This helps to ensure that the  process  operates efficiently, producing more specification-conforming products with less waste (rework or scrap) Acceptance sampling [5] :  uses statistical sampling to determine whether to accept or reject a production lot of material. It has been a common quality control technique used in industry. It is usually done as products leaves the factory, or in some cases even within the factory. 4

5. Statistical process control [] Statistical process control is the application of statistical method of to the measurement and analysis of variation in a process. A process is a collection of activities that converts inputs into outputs or results. Though use of control charts, statistical process control assists in detecting special (or assignable) causes of variation in both in process parameters and end of the process (product) parameters. 5

6. S tatistic P rocess C ontrol Definition [2] Statistics : It is branch of mathematics concerned with collecting, organizing, and interpreting data . Population : A process produce thousands product in one shift – 8 hours - 480 minutes. Sample Selected: We want data for outer diameter of that product. We decided to collect data of 10 parts every hour. Data Collector : OD of product is measured for 10 parts every hour of that shift. Data organized: Make an excel sheet of data collected with different ranges. Data interpreted : Organize data can be further interpreted and use for process control. 6

7. Process: Combination of people, materials, methods, machines, environment and measurement to produce a goods or services. Control: System/Procedure policy to achieve results that confirms to requirements. 7

8. Importance of SPC [2] Reduces waste. Reduction in the time which is required to produce the product. Detecting errors at inspection. Reduce inspection cost Save cost of material by reducing number of rejects. More uniform quality of production Customer satisfaction It provides direction for long term reduction in process variability. 8

9. Quality Measurement and Manufacturing [6] Quality measurement is central to the process of quality control: “what gets measured gets done.” Measurement is basic for all three operational quality processes and for strategic management Quality control measurement – provides feedback and early warnings of problems. Operational quality planning measurement quantifies customer needs and product and process capabilities. Quality improvement measurements can motivate people, prioritize improvement opportunities, and help in diagnosing causes. 9

10. Sr. No. Random (common) causes Description Assignable (special) causes 1 Consists of many individual causes Consist of one or just a few individual causes 2 Any one random cause results in minute amount of variation (but many random causes act together to yield a substantial total). Any one assignable cause can results in large amount of variation. 3 Examples are human variation in setting control dials, slight vibration in machines, and NS slight variation in raw material. Examples are operator, a faulty setup, or a batch of defective raw materials.   Interpretation 4 Random variation cannot be eliminated from a process economically.   Assignable variation can be detected; action to eliminate the cause is usually economically justified. 5 An observation within the control limits of random variation means that the process should not be adjusted. An observation beyond control limits means that the process should be investigated and corrected. 6 With only random variation, the process is sufficiently stable to use sampling procedures to predict the quality of total production or do process optimization studies. With assignable variation present, the process is not sufficiently stable to use sampling procedures for prediction. 10

11. The Shewhart control chart [1] It is most desirable to provide umpires with tools that can help to distinguished between special causes and common causes. An elegant tool for this purpose is the Shewhart control chart (or just control chart) shown in figure In figure the horizontal scale is time and the vertical scale is quality performance. The plotted points show quality performance as time progresses. The chart also exhibits three horizontal lines. The middle line is the average of past performance and is; therefore, the excepted level of performance. The other two lines are statistical “limit lines” they are indented to separate special causes from common causes from causes, based on some chosen level of probability, such as 1 chance in 100. 11

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13. Point within control limit Point A on the chart differs from the historical average. However, since point A is within the limit lines, this difference could be due to common causes (at a probability of, more than 1 in 100). Hence we assume that there is no special cause. In the absence of special causes, the prevailing assumption include Only common causes are present. The process is in a state of “statistical control.” The process is doing the best it can The variations must be endured. No action need be taking action may make matters worse (a phenomenon known as “hunting” or “tampering”). 13

14. Points outside of the control limits: Point B differs from the historical averages, but is outside of the limit lines. Now the probability is against this being the result of common causes, less than 1 chance in 100. Hence, we assume that point B is the results of special causes. Traditionally such “out-of-control” point becomes nominations for corrective action. 14

15. Advantages of statistical control:[1] Provides means of detecting error at inspection. Leads to more uniform quality of production. Improves the relationship with the customer. It reduces cost. It reduces the number of rejects and saves the cost of material. It determines the capability of the manufacturing process It provides direction for long term reduction in process variability. It is stable process and operates with less variability. For some types of quality problems, the statistical tool are more than useful- the problems cannot be solved at all without using the appropriate statistical tools. The SPC movement has succeeded in training a great many supervisors and workers in basic statistical tools. The resulting increases in statistical literacy have made it possible for them to improve their grasp of the behavior of process and products. In addition, many have learned that decision based on data collection and analysis yield superior results. 15

16. Control Chart [1] Traditional Shewhart control charts are divided into two categories: Variable charts (those using continuous, measurement data), Attribute charts (those using count data). Selecting the proper type of control chart is the different types are described further below. 16

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18. X̄ and R chart Also called the " Average and range " chart. X̄ refers to the average of a rational subgroup and measures the central tendency of the response variable over time. R is the range (Difference between highest and lowest values in each subgroup), and the R chart measure the gain or loss in uniformity within a subgroup which represents the variability in the response variable over time. Note that, because specification limits apply to individual values rather than average (averages inherently vary less than the component individual values), control limits cannot be compared to specification limits which should not be placed on control chart for averages 18

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20. X̄ and s chart The average and standard deviation chart is similar to the X̄ and R chart , but the standard deviation (instead of the range) is used in the s chart. Although an s chart is statistically more efficient than the range for subgroup sizes greater than 2, a range chart is easier to compute and understand and is traditionally used for subgroup sizes smaller than about 10. 20

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22. X-mR chart Also known as I-mR chart, this charts individual measures and a moving range. It is used when the rational subgroup size = 1(such that there are no multiple measures from which to obtain an average). 22

23. Z-mR Chart This is similar to the X-mR chart, except that the individual values are standardize through a Z transformation. This is useful for short runs in which there are fewer than the recommended 20 to 30 needed to establish one of the preceding charts. 23

24. Example of Control Chart Attribute Data Whereas control charts for variable data require numerical measurement (e.g., line width from a photo resist process), control charts for attribute data require only a count of observations of a characteristics (e.g., the number of nonconforming items in a sample). There also are called categorical data because units are classified into group such as pass or fail. 24

25. p chart Also called a proportional chart, this tracks the proportion or percentage of nonconforming units (percentages defective) in each sample over time. 25

26. np chart This chart is used to track the number of nonconforming (defective) units in each sample over time. An np chart should only be used when the number of units sampled is constant (or nearly so). 26

27. c chart Used to track the number of nonconforming (i.e., defects, rather than defective units as in the p chart). 27

28. u chart A variation of c chart, and analogous to the np chart, this chart tracks the number of nonconformities (defects) per unit in a sample of n units. As with the np chart, the number of units should be approximately constant. 28

29. Conclusion From the above we concluded that SPC is a effective tool of quality and process control in all type of industry not only manufacturing industry where quality and customer satisfaction is the major concern. The power of SPC lies in the ability to examine a process and the sources of variation in that process, using tools that give weightage to objective analysis over subjective opinions and that allow the strength of each source to be determined numerically 29

30. How to create Control Chart 30

31. References Juran’s Quality Handbook, Sixth Edition, Joseph M.Juran and Joseph A.De Feo, ASQ Publications page no.213-215, 571,575-578. https://www.slideshare.net/Raviraj-Jadeja/statistical-quality-control-19754262?from_action=save https://www.investopedia.com/terms/d/descriptive_statistics.asp https://asq.org/quality-resources/statistical-process-control https://en.wikipedia.org/wiki/Acceptance_sampling https://www.slideshare.net/AnkitaGorhe/statistical-process-control-125275380?from_action=save 31

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