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