22 terms

Control Chart

-periodically take a sample from a process

-calculate a statistic of interest from the sample

-plot the statistic on a control chart

-determine if the process is in control

-prevent quality problems

-calculate a statistic of interest from the sample

-plot the statistic on a control chart

-determine if the process is in control

-prevent quality problems

signs of a process in control

-no sample points outside limits

-most points near process average

-about equal number of points above and below centerline

-points appear randomly distributed

-most points near process average

-about equal number of points above and below centerline

-points appear randomly distributed

normal distribution

the empirical rule applies

we set control limits at +-3 sigmas

2.6/1000 observations will fall outside of control limits

we set control limits at +-3 sigmas

2.6/1000 observations will fall outside of control limits

common causes of variation

-ever-present factors that contribute to small, random shifts in output

-difficult to track to a source

-inherent in the process

-present when in control

-what we want to see in a chart

-difficult to track to a source

-inherent in the process

-present when in control

-what we want to see in a chart

special causes of variation

-identifiable factors that induce variation beyond the inherent variation in the system

-can usually be tracked to a source

-process is not in control when present

-want to remove

-driving to school example, car accident

-can usually be tracked to a source

-process is not in control when present

-want to remove

-driving to school example, car accident

attribute data

product characteristic evaluated with a discrete choice

integers

integers

variable data

product characteristics that can be measured on a continuous scale

real numbers

real numbers

initial construction of a control chart

-decide what to measure or count

-collect the sample data

-plot the samples on the control chart

-determine if the data is in control

-collect the sample data

-plot the samples on the control chart

-determine if the data is in control

control charts for attribute data

p-chart and c-chart

p-chart

percentage of defects found in a sample

can only make a proportion is there is a finite number of defects

can only make a proportion is there is a finite number of defects

c-chart

count the number of defects found in an item

p bar

total number of defects / total number of observations

use 3 sigma

use 3 sigma

c bar

total number of defects / k (number of samples)

control charts for variable data

x bar chart, r chart

R bar

sum of the ranges / k

x double bar

sum of the means / k

process capability

the natural variation of a process relative to the variation allowed by the design specifications

comparing what we are producing with what we need to produce

comparing what we are producing with what we need to produce

design specifications

sets of instructions that say the product will process fine if we follow these rules

3 sigma quality

design specifications are 3 sigma from the process average

6 sigma quality

design specifications are 6 sigma from the process average

6 sigma is the tolerance

then find 3 sigma by dividing the tolerance by 2

then find upper and lower bounds

6 sigma is the tolerance

then find 3 sigma by dividing the tolerance by 2

then find upper and lower bounds

process capability ratio

if less than 1, it implies that there is too much variation for 3 sigma quality

R bar

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