1 / 42

Statistical Quality Control

Statistical Quality Control. Chapter 6 OPS 370. Statistical Process/Quality Control at Honda. https://www.youtube.com/watch?v=a9hBmlWRjEc. Two Scoops of Raisins in a Box of Kellogg’s Raisin Bran. Statistical Quality Control. Illustrations. 1. BASF – catalytic cores for pollution control

stephanie-cross
Télécharger la présentation

Statistical Quality Control

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Statistical Quality Control Chapter 6 OPS 370

  2. Statistical Process/Quality Control at Honda https://www.youtube.com/watch?v=a9hBmlWRjEc

  3. Two Scoops of Raisins in a Box of Kellogg’s Raisin Bran

  4. Statistical Quality Control

  5. Illustrations • 1. BASF – catalytic cores for pollution control • 2. Milliken – industrial fabrics • 3. Thermalex– thermal tubing • 4. Land’s End – customer service, order fulfillment • 5. Hospital pharmacy

  6. SQC Categories • 1. Statistical Process Control (SPC) • 2. Acceptance Sampling

  7. Types of Quality Data

  8. Variation

  9. Sources of Variation

  10. Cost of Variation

  11. Taguchi Loss Function

  12. Taguchi Loss Function

  13. SPC Methods-Control Charts

  14. Control Charts

  15. Developing a Control Chart

  16. A Process is “In Control” if • No sample points are outside limits • Most sample points are near the process average • About an equal number of sample points are above and below the average • Sample points appear to be randomly distributed

  17. Control Charts for Attributes

  18. Control Charts for Attributes

  19. Control Chart Z-Value

  20. P Chart Calculations

  21. 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 shows the number of defective tires in each sample of 20 tires. Calculate the proportion defective for each sample, the center line, and control limits using z = 3.00.

  22. P-Chart Example, cont.

  23. C-Chart Calculations

  24. 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.

  25. C-Chart Example, cont.

  26. Control Charts for Variables • 1. Control chart for variables are used to monitor characteristics that can be measured, such as length, weight, diameter, time • 2. X-bar Chart: Mean • A. Plots sample averages • B. Measures central tendency (location) of the process • 3. RChart: Range • A. Plots sample ranges • B. Measures dispersion (variation) of the process • 4. MUST use BOTH charts together to effectively monitor and control variable quality charateristics

  27. Factor for x-Chart Factors for R-Chart Sample Size (n) A2 D3 D4 2 1.88 0.00 3.27 3 1.02 0.00 2.57 4 0.73 0.00 2.28 5 0.58 0.00 2.11 6 0.48 0.00 2.00 7 0.42 0.08 1.92 8 0.37 0.14 1.86 9 0.34 0.18 1.82 10 0.31 0.22 1.78 11 0.29 0.26 1.74 12 0.27 0.28 1.72 13 0.25 0.31 1.69 14 0.24 0.33 1.67 15 0.22 0.35 1.65 R-Chart Calculations

  28. Example for Variable Control Charts • A quality control inspector at the Cocoa Fizz soft drink company has taken three samples with four observations each of the volume of bottles filled (ounces). Use the data below to develop R and X-bar control charts with three sigma control limits for the 16 oz. bottling operation.

  29. R-Chart Example

  30. Factor for x-Chart Factors for R-Chart Sample Size (n) A2 D3 D4 2 1.88 0.00 3.27 3 1.02 0.00 2.57 4 0.73 0.00 2.28 5 0.58 0.00 2.11 6 0.48 0.00 2.00 7 0.42 0.08 1.92 8 0.37 0.14 1.86 9 0.34 0.18 1.82 10 0.31 0.22 1.78 11 0.29 0.26 1.74 12 0.27 0.28 1.72 13 0.25 0.31 1.69 14 0.24 0.33 1.67 15 0.22 0.35 1.65 X-bar Chart Calculations

  31. X-barChart Example

  32. Interpreting Control Charts • A process is “in control” if all of the following conditions are met. • No sample points are outside limits • Most sample points are near the process average • About an equal number of sample points are above and below the average • Sample points appear to be randomly distributed

  33. Control Chart Examples 1 2

  34. Limits Based on Out of Control Data 3 4

  35. Process Capability

  36. Process Capability

  37. Specification Limits Control Limits Individual Measurements Sample Means

  38. Design Specifications Process Design Specifications Process Process Capability

  39. Design Specifications Process Design Specifications Process Process Capability

  40. Computing Process Capability

  41. Cpand CpkExample • Specifications for a soda bottling process call for a target value of 16.0 oz. with a tolerance of ± 0.2 oz. • Process performance measures are • Mean: µ = 15.9 oz. • Std. Deviation: σ = 0.05 oz. • Compute the Cp value for this bottling process and indicate whether or not it is capable based on the Cp value. • Compute the Cpk value for this bottling process and indicate whether or not it is capable based on the Cpk value.

  42. Example Calculations

More Related