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Quality Control

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Quality Control

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    2. Phases of Quality Assurance

    3. Inspection

    4. How Much to Inspect

    5. Where to Inspect in the Process Raw materials and purchased parts Finished products Before a costly operation Before an irreversible process Before a covering process

    6. Examples of Inspection Points

    7. Centralized vs. On-Site Inspection

    8. Statistical Process Control (SPC)

    9. Process Variability

    10. Variation

    11. Sampling and Sampling Distribution

    12. Sampling and Sample Distribution

    13. Sampling Distribution

    14. Normal Distribution

    15. Control Process Statistical Control Process Define Measure Compare Evaluate Correct Monitor results

    16. Control Chart: The Voice of the Process

    17. Control Chart

    18. Control Limits

    19. Sampling and Process Distribution Example An industrial process that makes 3-foot sections of plastic pipe produces pipe with an average inside diameter of 1 (one) inch and a standard deviation of 0.05 inch. A). If you randomly select one piece of pipe, what is the probability that its inside diameter will exceed 1.02 inches, assuming the population is normal? B). If you select a random sample of 25 pieces of pipe, what is the probability that the sample mean will exceed 1.02 inches?

    20. SPC Errors

    21. Type I Error

    22. Observations from Sample Distribution

    23. Control Charts for Variables Mean charts Used to monitor the central tendency of a process. X bar charts Range charts Used to monitor the process dispersion R charts

    24. Control Charts for Variables

    25. X bar charts Center line is the grand mean (X double bar) Points are X bars

    26. Range (R) Charts Center line is the average Range (R bar) Points are R D3 and D4 values are tabled according to n (sample size)

    27. Examples for Mean and Range charts

    28. Examples for Mean and Range charts

    29. Examples for Mean and Range charts Special metal Screw

    30. Mean Charts Results

    31. Range Charts Results Special Metal Screw

    33. Control Charts for Variables Using Sigma Mean Charts

    34. Mean and Range Charts

    35. Mean and Range Charts

    36. Using Mean and Range Charts

    37. Control Chart for Attributes p-Chart - Control chart used to monitor the proportion of defectives in a process c-Chart - Control chart used to monitor the number of defects per unit

    38. Use of p-Charts When observations can be placed into two categories. Good or bad Pass or fail Operate or don’t operate When the data consists of multiple samples of several observations each

    39. P- Charts

    40. P-Charts Example Hometown Bank (using Z=3)

    41. P-Charts Results

    42. C- Charts c charts – used to count defects in a constant sample size

    43. Use of c-Charts Use only when the number of occurrences per unit of measure can be counted; non-occurrences cannot be counted. Scratches, chips, dents, or errors per item Cracks or faults per unit of distance Breaks or Tears per unit of area Bacteria or pollutants per unit of volume Calls, complaints, failures per unit of time

    44. C-Charts Example A Highway intersection averages 3 accidents per month. Last month 7 accidents occurred. Has something changed at the intersection? Use z=3.

    45. Managerial Considerations At what point in the process to use control charts What size samples to take What type of control chart to use Variables Attributes

    46. Run Tests

    47. Nonrandom Patterns in Control charts Trend Cycles Bias Mean shift Too much dispersion

    49. Run Tests Example The number of defective items per sample for 11 samples is shown below. Determine if random patterns are present in the sequence using both median and Up-down tests.

    50. Specifications or tolerances Range of acceptable values established by engineering design or customer requirements Process variability Natural variability in a process Process capability Process variability relative to specification One of the unique properties of a normal distribution is that 99.74% of the observations will fall within 3 standard deviation (? ) from the mean (µ). Thus, we expect a process that is in control to produce the majority of the output (99.74%) between µ-3? and µ+3?, where µis the process mean. That is, the natural tolerance limits of the process are µ?3?, and thus we use 6? as a measure of process capability. Process Capability

    51. Process Capability

    52. Process Capability Ratio

    53. Process Capability Ratio

    54. Process Capability Index

    55. Process Capability Ratio & Index Example The intensive care unit lab process has an average turnaround time of 26.1 minutes and a standard deviation of 1.2 minutes. The target (nominal) value for this service is 25 minutes with an upper specification limit of 30 minutes and a lower specification limit of 20 minutes. Management wants to have four-sigma performance for the lab. Is the lab capable of this level of performance?

    56. Reliability Series System Parallel System

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