GMS401 Chapter 10
Terms in this set (35)
Statistical Quality Control
Uses statistical techniques and sampling in monitoring and testing of quality of goods and services
Appraisal of a good or service against a standard
determines to accept or reject a product
Statistical Process Control
Is concerned with statistical evaluation of the product in the production process. To do the SPC, the operator takes periodic samples from the process and compares them with predetermined limits.
Statistical Process Control Planning Process
1) Define the quality characteristics important to customers, and how each is measured.
2) For each Characteristics:
a)Determine a quality Control Point b) Plan how inspection is to be done, how much to inspect, and whether centralized or on-site c) Plan the corrective action
Inspection Points are
1) At the beginning of process.-- There is little sense in paying for goods that do not meet quality standards and in spending time and effort on material that is bad to begin with.
2) At the end of process.-- Customer satisfaction and company's image are at stake here, and repairing or replacing products in the field is usually much more costly than doing it at the factory.
3) At the operation where a characteristic of interest to customers is first determined. In particular, before a costly, irreversible, or covering (e.g., painting) operation.
If inspection activities increase, inspection costs increase, but the costs of undetected defects decrease. The goal is to minimize the sum of these two costs.
immovable product (e.g. ship), simple or handheld measuring equipment, automated inspection
In Lab Inspection
specialized equipment, skilled quality control inspectors, more favorable test environment
Random Variation (chance)
Natural variation in the output of a process, created by countless minor factors
Non-random variability in process output; a variation whose cause can be identified
Statistical Process Control steps
Take periodic samples from process, then compare to predetermined limits: If outside limits, stop process and take corrective action. If inside limits, continue process
Is a time-ordered plot of a sample statistic, with limits. Used to distinguish between random and assignable variation (or equivalently no shift and a shift in the process). Basis for it is the Sampling Distribution. Upper and Lower limits define range of acceptable varation
The dividing lines for the value of sample statistic between concluding no process shift and a process shift, hence random and assignable variation
Type I Error
Concluding that a process has shifted (i.e an assignable variation is present) when it has not (i.e only random variation is present)
Type II Error
Concluding that a process has not shifted (i.e only random variation is present) when it has (i.e. an assignable variation is present)
Out of Control Points in Control Chart
Abnormal variation most likely due to assignable causes
Within Control Points in Control Chart
Normal variation due to Random Variation (Chance)
Main Task of SPC
is to distinguish assignable from random variation
Is the variability of a sample statistic, which is the theoretical distribution of the values of the statistic for all possible samples of a given size from the process.
Central limit theorem
implies that sampling distributions will be approximately Normal, even if the population (i.e., the process) is not.
If the process has only random variability
Then the sample mean should most likely fall between +/-2 (with 95.5 percent probability for Normal distribution) or +/-3 (with 99.7 percent probability for Normal distribution) standard deviations of the process mean
If the process has doesn't have random Var
If it doesn't, then we can conclude that the process mean most likely has changed, and hence there is most likely an assignable cause.
Control Limits Compute
Are calculated for +/-2 (with 95.5 percent probability for Normal distribution) or +/-3 (with 99.7 percent probability for Normal distribution) standard deviations of the process mean.
Observations from Sample Distribution
each sample mean is compared to the extremes of the sampling distribution (i.e., the control limits) to judge if it is within the acceptable (random) range.
Sample Mean Control Charts (X-bar)
Used to monitor the mean of a process.
Sample Range Control Chart (R chart)
Used to monitor the process spread or dispersion
Used to monitor the proportion of defectives in a process. When observations are placed in 2 categories: good or bad, pass or fail. And when data consists of multiple samples of several observations each.
Used to monitor the number of defects per unit. Use only when number of occurrences per unit of measure can be counted;non-occurrences cannot be counted.Scratches, chips, dents, or errors per item
generate data that are counted.
generate data that are measured.
Range of acceptance values established by engineering design or customer requirements
Natural variability in a process
Process variability relative to specification
Process Capability Indices
Process centered means the process mean is in the center of the limits. Sometimes the limits are smaller, or even zero, on one side of the process mean.