Operations management, 9e by krajewski itzman malhotra chapter 05

5

Quality And Performance

PowerPoint Slides
by Jeff Heyl

For Operations Management, 9e by
Krajewski/Ritzman/Malhotra
© 2010 Pearson Education
5–1


Costs of Quality
 A failure to satisfy a customer is considered a
defect
 Prevention costs
 Appraisal costs
 Internal failure costs
 External failure costs
 Ethics and quality

5–2


Total Quality Management

Customer
satisfaction

Figure 5.1 – TQM Wheel

5–3


Total Quality Management
 Customer satisfaction
 Conformance

to specifications

 Value
 Fitness

for use

 Support
 Psychological

impressions

Employee involvement
 Cultural

change

 Teams

5–4


Total Quality Management

 Continuous improvement
 Kaizen
A

philosophy

 Not

unique to quality

 Problem

solving process

5–5


The Deming Wheel
Plan

Act

Do

Study
Figure 5.2 – Plan-Do-Study-Act Cycle
5–6


Six Sigma

Process average OK;
too much variation

Process variability OK;
process off target
X X
X X
XX XX
X

X
X
X

X

X

X X
X

X

Reduce
spread

Process
on target with
low variability

Center
process

X
XX
X
X
X XX

Figure 5.3 – Six-Sigma Approach Focuses on Reducing Spread and Centering the Process
5–7


Six Sigma Improvement Model
Define

Measure

Analyze

Improve

Control
Figure 5.4 – Six Sigma Improvement Model
5–8


Acceptance Sampling
 Application of statistical techniques
 Acceptable quality level (AQL)

 Linked through supply chains

5–9


Acceptance Sampling
Firm A uses TQM or Six
Sigma to achieve internal
process performance

Buyer
Manufactures
furnaces

fan

Motor inspection

Yes

Accept
motors?

mo

tors

Firm A
Manufacturers
furnace fan motors

TARGET: Buyer’s specs

Supplier uses TQM or Six
Sigma to achieve internal
process performance
fan

bla

des

No
Blade inspection

Yes

Accept
blades?

Supplier
Manufactures
fan blades
TARGET: Firm A’s specs

No

Figure 5.5 – Interface of Acceptance Sampling and Process
Performance Approaches in a Supply Chain
5 – 10

Statistical Process Control
 Used to detect process change
 Variation of outputs
 Performance measurement – variables
 Performance measurement – attributes
 Sampling
 Sampling distributions

5 – 11


Sampling Distributions
1. The sample mean is the sum of the observations
divided by the total number of observations
n

x=

∑x
i =1

i

n

where
xi
n
x

= observation of a quality characteristic (such as tim
= total number of observations
= mean

5 – 12


Sampling Distributions
2. The range is the difference between the largest
observation in a sample and the smallest. The
standard deviation is the square root of the
variance of a distribution. An estimate of the
process standard deviation based on a sample is
given by
σ=

∑( x − x)
i

n −1

2

or σ =

2
x
∑ i−

(∑ x )

n −1

2

i

n

where
σ

= standard deviation of a sample
5 – 13


Sample and Process Distributions
Mean

Distribution of
sample means

Process
distribution

25

Time

Figure 5.6 – Relationship Between the Distribution of Sample
Means and the Process Distribution
5 – 14


Causes of Variation
 Common causes
 Random,

unavoidable sources of variation

 Location
 Spread
 Shape

Assignable causes
 Can

be identified and eliminated

 Change
 Used

in the mean, spread, or shape

after a process is in statistical control

5 – 15

Assignable Causes
Average

(a) Location

Time

Figure 5.7 – Effects of Assignable Causes on the Process
Distribution for the Lab Analysis Process
5 – 16


Assignable Causes
Average

(b) Spread

Time

Figure 5.7 – Effects of Assignable Causes on the Process
Distribution for the Lab Analysis Process
5 – 17


Assignable Causes
Average

(c) Shape

Time

Figure 5.7 – Effects of Assignable Causes on the Process
Distribution for the Lab Analysis Process
5 – 18


Control Charts
 Time-ordered diagram of process performance


Mean



Upper control limit



Lower control limit

Steps for a control chart
1. Random sample
2. Plot statistics
3. Eliminate the cause, incorporate improvements
4. Repeat the procedure
5 – 19


Control Charts
UCL

Nominal

LCL

Assignable
causes likely
1

2

3

Samples
Figure 5.8 – How Control Limits Relate to the Sampling
Distribution: Observations from Three Samples
5 – 20


Control Charts

Variations

UCL
Nominal
LCL

Sample number
(a) Normal – No action
Figure 5.9 – Control Chart Examples

5 – 21


Control Charts

Variations

UCL
Nominal
LCL

Sample number
(b) Run – Take action
Figure 5.9 – Control Chart Examples
5 – 22


Control Charts

Variations

UCL
Nominal
LCL

Sample number
(c) Sudden change – Monitor
Figure 5.9 – Control Chart Examples
5 – 23

Control Charts

Variations

UCL
Nominal
LCL

Sample number
(d) Exceeds control limits – Take action
Figure 5.9 – Control Chart Examples
5 – 24


Control Charts
 Two types of error are possible with control charts
 A type I error occurs when a process is thought to
be out of control when in fact it is not
 A type II error occurs when a process is thought to
be in control when it is actually out of statistical
control
 These errors can be controlled by the choice of
control limits

5 – 25