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Chapter 10: Statistical Quality Control
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Mapa Mental sobre Chapter 10: Statistical Quality Control, criado por Cam Ying Luo em 06-11-2013.
Mapa Mental por
Cam Ying Luo
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Chapter 10: Statistical Quality Control
Introduction
Statistical Quality Control
use stat tech & sampling to monitor & test Q of goods & services
Acceptance Sampling: accept or reject a product
Stat process control: if process is operating within acceptable limits
Best company: design Q into process, reduce need for inspections
Inspection
appraisal of goods/services against standards
Phases of Q Control
least progressive: Acceptance sampling (inspection before & after production)
medium progressive: Statistical process control (corrective action during production)
most progressive: Continuous improvement & Six Sigma (Q built into process)
Stat Process Control Planning process
1. DEFINE important Q characteristics & how to measure
2. For each charact
a. determine Q control point
b. PLAN
i. HOW to inspect
ii. HOW MUCH to inspect
iii. WHERE centralized or on-site
c. plan the corrective action process
Inspection
How much/How often; Where/When; Centralized vs. On-site
Inputs (acceptance sampling) ==> Transformation (stat process control) ==> Outputs (acceptance sampling)
WHERE to inspect
raw materials & purchased parts
before costly operation; before irreversible process; before covering process
finished products
Inspection costs
GRAPH
Centralized vs. On-site
On-Site
immovable product; simple/handled measuring equip; automated inspection
In-Lab
specialized equip, skilled Q control inspectors; more favourable test env
Statistical Process Control
def: stat evaluation of output of a process during production
1. QC steps
2. type of Variations
3. control charts
4. design control charts
5. individual unit & moving range charts
6. control charts for attributes
7. using control charts
SPC steps
compare to predetermined limits
if OUTSIDE limits, stop process & take corrective action
if INSIDE limits, continue process
Control Chart
purpose: monitor process output to distinguish between random & assignable variation
def: time ordered plot of stat obtained from ongoing process
UCL & LCL define range of acceptable variation
Normal Distribution
Standard deviation
Control limits: dividing lines between random & nonrandom deviations from the process mean (+/- 2SD, 3SD)
out of control
abnormal V most likely due to assignable causes
normal V due to chance
Errors
Type I error: process has changed when it has not
e.g. assignable V is present when only random V is present ==> LOWER than LCL, producers risk
Type II error: process has not changed when it has
e.g. only random V is present when assignable V is present ==> HIGHER than UCL, consumers risk
CHART
types of variations
random V
natural V in output, created by countless minor factors
assignable V
V whose source can be identified
Design control chart
1. determine a sample size
2. obtain 20 to 25 samples
3. establish & graph preliminary control limits
4. plot sample stat values on control chart
5. any points outside CL?
NO: assume no assignable cause
YES: investigate & correct
Control charts for attributes
variables: measured
sample mean
monitor mean of process & x-bar charts
sample range
monitor process dispersion & R charts
sample mean chart
FORMULAS & EXAMPLES
Alternative method
FORMULAS & EXAMPLES
sample range control chart
FORMULAS & EXAMPLES
variables: counted
p-chart: monitor proportion of defectives in process:
use: observations into 2 categories (e.g. good or bad); multiple samples of several observations each
FORMULAS
c-chart: monitor # of defects/unit
use: # of occurrences per unit of measure can be counted; non-occurrences can't be counted (e.g. breaks per unit of area)
FORMULAS
Choose right control chart
continuous measurement
n=1
individual unit & moving range charts
n>or=2
sample mean & range charts
discrete measurement
2 types of results can be counted
p-chart
only occurrences can be counted
c-chart
QUESTIONS
Process Capability
PC indices
process is centered ==> Cp
FORMULA
process is NOT centered ==> Cpk
FORMULA
EXAMPLES
PC analysis
< 1 = not capable
1. redesign process; 2. use alternate; 3. more inspection; 4. relax specfication
> 1 = better than needed
charge more for better quality?
Cpk = 0.4253: produce a relatively high # of defects
many companies look for > 1.3; six-sigma looks for 2
terminology
design specifications / aka 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
Six Sigma Quality
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