3QC3-CH3.docx

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11 Apr 2012
Department
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Operations Management
Chapter 3: Statistical Process Control
Statistical process control (SPC): Involves monitoring the production process to detect and
prevent poor quality
- Employee training in SPC is a fundamental principle of TQM
The Basics of Statistical Process Control
Sample: A subset of the items produced to use for inspection
- All processes have variability-random and nonrandom (identifiable, correctable)
- SPC is a tool for identifying problems in order to make improvements
Attribute: A product characteristic that can be evaluated with a discrete response
Variable Measure: A product characteristic that is continuous and can be measured (weight,
length)
- A service defect is a failure to meet customer requirements
Control chart: A graph that established the control limits of a process
Patterns
- A pattern can indicate an out-of-control process even if sample values are within control limits
Run: A sequence of sample values that display the same characteristic
Pattern Test: Determines if the observations within the limits of control chart display a
nonrandom pattern
- There are several general guidelines associated with the zones for identifying patterns in a
control chart, where none of the observations are beyond the control limits:
1) 8 consecutive points on 1 side of the centerline
2) 8 consecutive points up and down
3) 14 points alternating up and down
4) 2 out of 3 consecutive points in zone a (on one side of the centerline)
5) 4 out of 5 consecutive points in zone A and B on one side of the centerline
Control limits: The upper and lower bands of a control chart
Types of charts; attributes p and c, and variables, x bar and r
Sigma limits are the number of standard deviations
A process is in control when:
1) There are no sample points outside the control limits
2) Most points are near the process average (ie.the centerline), without too many close to the
control limits
3) Approximately equal numbers of sample points occur below and above the centerline
4) The points appear to be randomly distributed around the centerline
* A sample point can be within the control limits and the process still be out of control
- After a control chart is established, it is used to determine when a process goes out of control
and corrections need to be made
Control Charts for Attributes
P-Chart: Uses the proportion defective in a sample
- Can be used when it is possible to distinguish between defective and nondefective items and to
state the number of defectives as a percentage of the whole
- A sample of n items is taken periodically from the production or service process, and the
proportion of defective items in the sample is determined to see if the proportion falls within the
control limits on the chart
- Assumed that as the sample size gets larger, the normal distribution can be used to
approximate the distribution of the proportion defective
C-Chart: Uses the actual number of defects per item in a sample
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