P chart & c-chart

1. P-CHART & C-CHART GROUP NO:B5 GROUP MEMBERS: PRIYANKA K NITHU K S RANJITH SARATH V VISHNU DAS

2. STATISTICAL PROCESS CONTROL It involves monitoring the production process to detect and prevent poor quality. It is a statistical procedure using control charts to see if any part of a production process is not functioning properly and could cause poor quality. It is a tool for identifying problems in order to make improvement.

3. QUALITY MEASURES ATTRIBUTE OF THE PRODUCT VARIABLE MEASURES

4. ATTRIBUTE: An attribute is a product characteristics such as colour , surface texture,cleanlines , smell and taste. Attribute can be evaluated quickly with a discrete response such as good or bad. If quality specification are complex and extensive , a simple attribute test might be used to determine whether or not a product or service is defective.

5. Variable measures: A product characteristics that is continuous and can be measured.

6. Spc applied to Hospitals Grocery store Airlines Fast food restuarant

7. Control charts A graph that establishes the control limits of a process. These are graphs that visually show if a sample is within statistical control limits. Two basic purpose: to establish the control limits for a process. To monitor the process to indicate when it is out of control.

8. Statistical control charts It is one of graph to monitor a production process. Samples are taken from the process periodically , and observation are plotted on the graph . If any observation is outside the upperlimit or lower limit on the graph, it indicate that something is wrong in the process. ie , it is not in control which may cause defective or poor quality items.

9. Where control charts used? Control charts are used at critical points in the process where historically the process has shown a tendency to go out of control . At points where if the process goes out of control it is particularly harmful and costly. It is frequently used at the beginning of a process to check the quality of raw materials and delivers for a service operation.

10. Used before a costly or irreversible point in process After which the product is difficult to rework Before or after assembly or painting operations that might cover defects. Before the outgoing final product or service is shipped or delivered

11. Types of the control charts Variables control charts Variable data are measured on a continuous scale. For example: time, weight, distance or temperature can be measured in fractions or decimals. Applied to data with continuous distribution Eg: X chart and R chart Attributes control charts Attribute data are counted and cannot have fractions or decimals. Attribute data arise when you are determining only the presence or absence of something: success or failure, accept or reject, correct or not correct. For example, a report can have four errors or five errors, but it cannot have four and a half errors. Applied to data following discrete distribution Eg: P chart and C chart (http://www.asq.org/learn-about-quality/data-collection-analysis-tools/overview/control-chart.html)

12. Two types : P-chart & C-chart for attributes Mean X and range R for variables

13. P chart Also called the percent defective chart Uses the proportion of defective items in a sample as the sample statistic. P-chart can be used when it is possible to distinguish between defective and non defective items and to state the number of defectives as a percentage of the whole.

14. © Wiley 2010 14 Control Charts for Attributes –P-Charts & C-Charts Attributes are discrete events: yes/no or pass/fail Use P-Charts for quality characteristics that are discrete and involve yes/no or good/bad decisions Number of leaking caulking tubes in a box of 48 Number of broken eggs in a carton Use C-Charts for discrete defects when there can be more than one defect per unit Number of flaws or stains in a carpet sample cut from a production run Number of complaints per customer at a hotel

15. © Wiley 2010 15 P-Chart Example: A production manager for a tire company has inspected the number of defective tires in five random samples with 20 tires in each sample. The table below shows the number of defective tires in each sample of 20 tires. Calculate the control limits. Sample Number of Defective Tires Number of Tires in each Sample Proportion Defective 1 3 20 .15 2 2 20 .10 3 1 20 .05 4 2 20 .10 5 1 20 .05 Total 9 100 .09 Solution:

16. UCL= UPPER CONTROL LIMIT LCL= LOWER CONTROL LIMIT Z= the no. of standard deviations from the process average.=3 P= process % defective of a sample P bar =process mean percent defective

17. © Wiley 2010 17 P- Control Chart

18. C -Chart Also called the number of defective per sample area. It applies to the no. of nonconformities in sample of constant size C=no. of nonconformities in each sample. The CL of this chart are based on poisson distribution.

19. Application of c chart To control the no. of nonconforming rivets in an aircraft wing. To control the number of imperfection observed in a galvanized sheet To control the no. of defects in final assemblies(like TV, radio, computer)

20. © Wiley 2010 21 C-Chart Example : The number of weekly customer complaints are monitored in a large hotel using a c-chart. Develop three sigma control limits using the data table below. Week Number of Complaints 1 3 2 2 3 3 4 1 5 3 6 3 7 2 8 1 9 3 10 1 Total 22 Solution:

21. © Wiley 2010 22 C- Control Chart

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