Transcript Document

IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT
IES 303
Chapter 5: Process Performance and Quality
Objectives:
• Understand the quality from customer’s and producer’s perspectives
• Understand how to construct control charts
• Understand how to determine if a process is capable of producing service
or product to specification
Week 5-6
December 8-15, 2005
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IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT
What is Quality?
Which one has a higher quality?
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Source: Russell and TaylorIII (2005)
IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT
Meaning of Quality:
Consumer’s Perspective

Fitness for use
 ________________________
__________________________

Quality of design
 ___________________________
__________________________

A Mercedes and a Ford are equally “fit
for use,” but with different design
dimensions
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Source: Russell and TaylorIII (2005)
IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT
Quality Measure in
Manufacturing Industry
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IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT
Quality Measure in
service industry

Nature of defect is different in services

Service defect is a failure to meet
customer requirements

Example of Quality Measure

________________________________

________________________________

________________________________ “quickest, friendliest, most
accurate service
available.”
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IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT
Costs of Poor Process Performance and Quality
1. ________________

Preventing defects before they happen

Ex: redesigning process/product/service, training employees,
working with suppliers
2. ________________

Costs incurred in assessing the level of performance attained by
the firm’s processes

As preventive measure improve performance, appraisal costs
decrease because fewer resources and efforts are needed
3. ________________

Costs resulting from defects discovered during the production of
a service / product
4. ________________

Cost that arise when a defect is discover after the customer has
receive the service / product
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IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT
Total Quality Management
(TQM)
Customer
satisfaction
Figure 5.2
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IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT
ProblemSolving
Process
Deming
Wheel
(PDCA)
Plan
Act
Do
Check
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IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT
Variation of Output
Standard Deviation/
Spread
Mean
____________________
____________________
More consistent process
____________________
____________________
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IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT
Causes of Variations
1. ___________________
 Variation inherent in a process
 Unavoidable variation but can be reduced through
improvements in the system
2.
___________________
 Variation due to identifiable factors or unusual incidents
 Ex: ______________________________________________
 A process that is operating in the presence of assignable
causes is said to be out of control
 Can be modified through operator or management action
 If ignored, tend to produce poor quality products or services
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IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT
Basics of Control Charts


Control charts : _________________________________
_______________________________________________
Control limits : _________________________________
Out of control
Upper
A process is generally considered to be control
in control if
limit

No sample points outside the
control limits

Most points are near the process
Lower
average, without too many close to control
limit
the control limits

Approximately equal number of
sample points above and below the
center line (process average)

Randomly distributed around the
centerline (no pattern)
Process
average
1
2
3
4
5
6
7
8
9
10
Sample number
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IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT
Control Chart Examples
UCL
Nominal
LCL
Assignable
causes likely
1
Figure 5.6
2
Samples
3
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IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT
Control Chart Examples
Variations
Figure 5.7 (a)
UCL
Nominal
LCL
_____________________
_____________________
_____________________
Sample number
_____________________
_____________________
_____________________
UCL
Variations
Figure 5.7 (b)
Nominal
LCL
Sample number
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Figure 5.7
IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT
Control Chart Examples
Figure 5.7 (c)
Variations
UCL
Nominal
LCL
_____________________
_____________________
_____________________
Sample number
_____________________
_____________________
_____________________
UCL
Variations
Figure 5.7 (d)
Nominal
LCL
Sample number
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IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT
Control Chart Examples
Variations
UCL
Nominal
LCL
Sample number
Figure 5.7 (e)
_____________________
_____________________
_____________________
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IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT
Two Types of Error


___________________

Occurs when the employee concludes that the process is out
of control based on a sample result that falls outside control
limits, when in fact it was due to randomness

False Alarm

Producer’s risk
___________________

Occurs when the employee concludes that the process is in
control and only randomness is present, when actually the
process is out of statistical control

Consumer’s risk
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IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT
Types of Control Charts
 Charts for variables

Continuous scale measure.

Ex: length, weight, dimensions, time
1. ________________________
2. ________________________
 Charts for attributes
 Discrete responses.

Ex: counts; good / bad; pass / fail; on-time / late
1. ________________________
2. ________________________
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IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT
Variable Control Charts
x-bar and R-Charts
In control process: BOTH process average and variability must be in control


Possible that small range/variability but average is out of limit, or

In limit average, but large variability
A2, D3, D4 are pre-calculated from sample size (n) See Table 5.1 page
210
x-bar Chart
x  averageof samplemeans
R-Chart
R = range of each sample
k = number of samples
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IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT
Ex 1: Slip-Ring Diameter
adapted from Russell and Taylor (2003)
(see also example 5.1)
Construct x-bar and R chart and conclude
OBSERVATIONS (SLIP-RING DIAMETER, CM)
SAMPLE k
1
2
3
4
5
x
R
1
2
3
4
5
6
7
8
9
10
5.02
5.01
4.99
5.03
4.95
4.97
5.05
5.09
5.14
5.01
5.01
5.03
5.00
4.91
4.92
5.06
5.01
5.10
5.10
4.98
4.94
5.07
4.93
5.01
5.03
5.06
5.10
5.00
4.99
5.08
4.99
4.95
4.92
4.98
5.05
4.96
4.96
4.99
5.08
5.07
4.96
4.96
4.99
4.89
5.01
5.03
4.99
5.08
5.09
4.99
4.98
5.00
4.97
4.96
4.99
5.01
5.02
5.05
5.08
5.03
0.08
0.12
0.08
0.14
0.13
0.10
0.14
0.11
0.15
0.10
50.09 1.15
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IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT
Ex 2: Light Bulb
The Watson Electric Company produces light bulbs. The following
data on the number of lumens for 40-watt light bulb were
collected when the process is in control.
Observation
Sample
1
2
3
4
1
604
612
588
600
2
597
601
607
603
3
581
570
585
592
4
620
605
595
588
5
590
614
608
604
a.
Calculate control limits for R
and x-bar charts
b.
A new sample is obtained:
570, 603, 623, and 583. Is
the process still in control?
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IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT
Attribute Control Charts
p- and c-charts
p-chart


Proportion defective items
in the sample
__________________
c-chart

Number of defects

__________________
z  thenumber of standarddeviationsfromprocessaverage
p  thesampleproportiondefective;an estimateof theprocessaverage
 p  standarddeviationof thesampleproportion
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IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT
Ex 3: Western Jeans Company
adapted from Russell and Taylor (2003)
Also see example 5.3 (pg 215)
 The Western Jeans Company wants to establish a pchart to monitor the production process. The company
believes that approximately 99.74% of the variability
in the production process (corresponding to 3-sigma
limits) is random and should be within control limits,
whereas .26% of the process variability is not random
and suggests that the process is out of control
 The company has taken 20 samples (one per day for
20 days), each containing 100 pairs of jeans (n = 100)
and inspect them for defects. The results show in the
table
Construct a p-chart to determine when the production
process might be out of control
Sample
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
no. of
defectiv
e
6
0
4
10
6
4
12
10
8
10
12
10
14
8
6
16
12
14
20
18
200
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IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT
Ex 4: Housekeeping service
adapted from Russell and Taylor (2003)
Also see example 5.4 (pg 216)




Housekeeping service
Measure of, for example, dirty
sheets, bedcovers, pillow, missing
room and toilet supplies, and etc.
Data in the table are the results
from 15 inspection samples
(rooms) conducted at random
during 1-month period
Use 3-sigma limit and construct cchart
Sample
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Number
of defects
12
8
16
14
10
11
9
14
13
15
12
10
14
17
15
190
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IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT
Ex 5: Highway Accident
The AA County Highway Safety Department monitors
accidents at the intersection B. There are 3 accidents
on average per month.
a.
Construct an appropriate control chart with 3-sigma
control limits
b.
Last month, 7 accidents occurred at the intersection. Is
it sufficient evidence to justify a claim that something
has changed in the intersection?
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IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT
Process Capability
 Range of natural variability in
To determine whether the
process is capable of producing
non-defective unit
Nominal value
Six sigma
process

Measured with control charts.
Four sigma
 Process cannot meet
specifications if natural
variability exceeds tolerances
Two sigma
 3-sigma quality

Specifications equal the
process control limits.
Lower
specification
Upper
specification
 6-sigma quality
 Specifications twice as large
as control limits
Mean
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Figure 5.13
IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT
Process Capability
adapted from Russell and Taylor (2003)
(a) Natural variation exceeds
design specifications;
Design
Specifications
____________________
____________________
____________________
(b) Design specifications and
natural variation the same;
Process
Design
Specifications
____________________
____________________
____________________
Process
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IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT
Process Capability
adapted from Russell and Taylor (2003)
(c) Design specifications greater
than natural variation;
Design
Specifications
____________________
____________________
____________________
(d) Specifications greater than
natural variation, but process
off center;
Process
Design
Specifications
____________________
____________________
____________________
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Process
IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT
Process Capability Measures

Process Capability Ratio (Cp)

Process Capability Index (Cpk)
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IES 303 Engineering Management & Cost Analysis | Dr. Karndee Prichanont, SIIT
Ex 6: Process Capability
A part has a length specification of 5 inches
with tolerances of + .004 inches. The
current process has an average length of
5.001 inches with a standard deviation of
.001 inches.
Calculate the Cp and Cpk for this process.
Indicate the capability of the current
process.
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