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Process Improvement
Dr. Ron Tibben-Lembke
Quality Dimensions Quality of Design
Quality characteristics suited to needs and wants of a market at a given cost
Continuous, never-ending improvement Quality of Conformance
Predictable degree of uniformity and dependability, in line with target price
Quality of Performance How is product performing in the marketplace? Are customers happy with the product? Durability, service considerations
Defining Quality Hard to define, like art, but you know it
when you see it. Some common terms from your definitions
Consistency (conformance) Conformance to a standard Ability of a product or service to meet stated or
implied needs (design, performance)
Responsibility for Quality Who’s responsible for quality?
Quality of Design Quality of Consistency Quality of Performance
Ensuring quality How can we make sure that we are
delivering quality to the customer?
SDSA Cycle Standardize:
Get employees to agree on how the process is done, using best practices from each
Flowchart the process Key indicators of process peformance
Do- Conduct planned experiments using best-practice methods on trial basis
Study- Collect & analyze data on key indicators to evaluate best-practice methods
Act- standardize best-practice methods and formalize through training
PDSA Cycle Reduce difference between customers’
needs and process performance Plan: create a plan to improve or innovate
the best-practice method from the SDSA cycle
Do: test plan on trial basis Study: study impact on key measurements Act: Take appropriate corrective actions
Statistics
Designed Size
12.5 13 13.5 14 14.5 15 15.5 16 16.5 17 17.5
Natural Variation
12.5 13 13.5 14 14.5 15 15.5 16 16.5 17 17.5
0
20
40
60X
Time
Process Control Charts
Graph of sample data plotted over time
UCL
LCL
Process Average ± 3
Natural Variation
0
20
40
60X
Time
Process Control Charts
Graph of sample data plotted over time
UCL
LCL
Process Average ± 3
Assignable Cause Variation
Natural Variation
X
Theoretical Basis of Control Charts
As sample size gets large enough ( 30) ...
Central Limit Theorem
X
Theoretical Basis of Control Charts
As sample size gets large enough ( 30) ...
sampling distribution becomes almost normal regardless of population distribution.
Central Limit Theorem
X
X
Theoretical Basis of Control Charts
Mean
Central Limit Theorem
x
x
n
X
Standard deviation
X
Theoretical Basis of Control Charts
95.5% of allX fall within ± 2X
Properties of normal distribution
XX
Theoretical Basis of Control Charts
95.5% of allX fall within ± 2X
Properties of normal distribution
X
Theoretical Basis of Control Charts
Properties of normal distribution
99.7% of allX fall within ± 3X
X
Theoretical Basis of Control Charts
95.5% of allX fall within ± 2X
Properties of normal distribution
99.7% of allX fall within ± 3X
X
Setting Control Limits Type I error – concluding a process is not
under control, when it really is Type II error – concluding a process is
under control, when it really is not
Rules for Out of Control Points Rule 1: Out of control if any point outside
control limits Rule 2: any 2 out of 3 consecutive points
fall in one of the A zones on same side of centerline
Rule 3: Any 4 of 5 consecutive points fall in B zone or higher on same side
Rule 4: 8 in a row on same side Rule 5: 8 or more in a row increasing or
decreasing
Rules for Out of Control Points 6 An unusually small number of runs
above and below the centerline (lots of up, down runs)
Rule 7: 13 consecutive points fall within zone C on either side of centerline
Run Tests
A
B
C
C
B
A
3σ
3σ
2σ
2σ
1σ
1σ
mean
Attributes vs. Variables
Attributes: Good / bad, works / doesn’t count % bad (C chart) count # defects / item (P chart)Variables: measure length, weight, temperature (x-bar
chart) measure variability in length (R chart)
p Chart Control Limits
# Defective Items in Sample i
Sample iSize
UCL p zp
n
p
X
n
p
ii
k
ii
k
(1 - p)
1
1
p Chart Control Limits
# Defective Items in Sample i
Sample iSize
UCL p zp p)
n
p
X
n
p
ii
k
ii
k
(1
1
1
z = 2 for 95.5% limits; z = 3 for 99.7% limits
# Samples
n
n
k
ii
k
1
p Chart Control Limits
# Defective Items in Sample i
# Samples
Sample iSize
z = 2 for 95.5% limits; z = 3 for 99.7% limits
UCL p z
LCL p z
n
n
kp
X
n
p
p
ii
k
ii
k
ii
k
1 1
1
and
n
p p) (1
p p)
n
(1
p Chart ExampleYou’re manager of a 500-room hotel. You want to achieve the highest level of service. For 7 days, you collect data on the readiness of 200 rooms. Is the process in control (use z = 3)?
© 1995 Corel Corp.
p Chart Hotel Data
No. No. NotDay Rooms Ready Proportion
1 200 16 16/200 = .0802 200 7 .0353 200 21 .1054 200 17 .0855 200 25 .1256 200 19 .0957 200 16 .080
p Chart Control Limits
n
n
k
ii
k
1 14007
200
p Chart Control Limits
16 + 7 +...+ 16
p
X
n
ii
k
ii
k
1
1
1211400
0864.n
n
k
ii
k
1 14007
200
p Chart Control Limits Solution
pp 3 0864 3.n
p p) (1
200
.0864 * (1-.0864)
p
X
n
ii
k
ii
k
1
1
1211400
0864.n
n
k
ii
k
1 14007
200
16 + 7 +...+ 16
p Chart Control Limits Solution
0864 0596 1460. . . or & .0268
pp 3 0864 3.n
p p) (1
200
.0864 * (1-.0864)
p
X
n
ii
k
ii
k
1
1
1211400
0864.n
n
k
ii
k
1 14007
200
16 + 7 +...+ 16
0.00
0.05
0.10
0.15
1 2 3 4 5 6 7
P
Day
p Chart Control Chart Solution
UCL
LCL
C-Chart Control Limits # defects per item needs a new chart How many possible paint defects could
you have on a car? C = average number defects / unit
UCL c zC c
LCL zC cc
