Chapter

1
Chapter 5 Chapter 6 Supplement
Product Service Design and Reliability
Statistical Process Control
Product Decision

• The good or service the organization provides
  society
• Top organizations typically focus on core
  products
• Customers buy satisfaction, not just a physical
  good or particular service
• Fundamental to an organization's strategy with
  implications throughout the operations function

2
Product Strategy Options

• Differentiation
• Shouldice Hospital
• Low cost
• Taco Bell
• Rapid response
• Toyota

3
Product Life Cycles

• May be any length from a few hours to decades
• The operations function must be able to introduce
  new products successfully

4
Product Life Cycles
Negative cash flow
5
Product Life Cycle
Introduction

• Fine tuning
• Research
• Product development
• Process modification and enhancement
• Supplier development

6
Product Life Cycle
Growth

• Product design begins to stabilize
• Effective forecasting of capacity becomes
  necessary
• Adding or enhancing capacity may be necessary

7
Product Life Cycle
Maturity

• Competitors now established
• High volume, innovative production may be needed
• Improved cost control, reduction in options,
  paring down of product line

8
Product Life Cycle
Decline

• Unless product makes a special contribution to
  the organization, must plan to terminate offering

9
Product Development System
Item 1
Here we deal with Items 1, 2 and 3
Item 2
Item 3
10
What Does the Customer Want How Can the
FirmMeet Their Expectation

• Identify customer wants
• Identify how the good/service will satisfy
  customer wants
• Relate customer wants to product hows
• Identify relationships between the firms hows
• Develop importance ratings
• Evaluate competing products

11
QFD House of Quality
QFD Quality Function Deployment
QFD House
12
House of Quality Example
Your team has been charged with designing a new
camera for Great Cameras, Inc. The first action
is to construct a House of Quality
13
House of Quality Example
14
House of Quality Example
15
House of Quality Example
16
(No Transcript)
17
House of Quality Example
18
House of Quality Example
19
House of Quality Example
G Good F Fair P Poor
20
House of Quality Example
21
House of Quality Example
Customer rating of Importance 5 is highest
Completed House of Quality
A total weight importance scores of gt 100 is
considered a Good product with the overall
rating of G
G
The total weighted score of our firm is 142
22927273225
22
Value Engineering

• Benefits
• Reduced complexity of products
• Additional standardization of products
• Improved functional aspects of product
• Improved job design and job safety
• Improved maintainability of the product
• Robust design

Design it so it is simpler and can be simpler to
manufacture
23
Cost Reduction of a Bracket through Value
Engineering
24
Issues for Product Development

• Robust design
• Modular design
• Computer-aided design (CAD)
• Computer-aided manufacturing (CAM)
• Virtual reality technology
• Value analysis
• Environmentally friendly design

25
Robust Design

• Product is designed so that small variations in
  production or assembly do not adversely affect
  the product
• Typically results in lower cost and higher quality

Tolerances
26
Modular Design

• Products designed in easily segmented components
• Adds flexibility to both production and marketing
• Improved ability to satisfy customer requirements

Wiring harness in GM Cars
27
Computer Aided Design (CAD)

• Using computers to design products and prepare
  engineering documentation
• Shorter development cycles, improved accuracy,
  lower cost
• Information and designs can be deployed worldwide

Searay Boats and their pleasure crafts 3 D AutoCad
28
Extensions of CAD

• Design for Manufacturing and Assembly (DFMA)
• Solve manufacturing problems during the design
  stage
• 3-D Object Modeling
• Small prototype development
• Exploded Views
• International data exchange

29
Computer-Aided Manufacturing (CAM)

• Utilizing specialized computers and program to
  control manufacturing equipment
• Often driven by the CAD system

Controlling Machines, Designing how the
Interact Statistical Process Control and Quality
Applications
30
Benefits of CAD/CAM

• Product quality
• Shorter design time
• Production cost reductions
• Database availability
• New range of capabilities

Boeing 777 Aircraft
31
Virtual Reality Technology

• Computer technology used to develop an
  interactive, 3-D model of a product from the
  basic CAD data
• Allows people to see the finished design before
  a physical model is built
• Very effective in large-scale designs such as
  plant layout

32
Value Analysis

• Focuses on design improvement during production
• Seeks improvements leading either to a better
  product or a product which can be produced more
  economically

Flat Screen TV and Monitors
33
Defining The Product

• First definition is in terms of functions
• Rigorous specifications are developed during the
  design phase
• Manufactured products will have an engineering
  drawing
• Bill of material (BOM) lists the components of a
  product

34
Specification Hierarchy US Military
Functional Specification System
Specification Product Specification Interface
Design Specification Production
Specification Drawings Test Specifications Accepta
nce Criteria
35
Product Documents

• Engineering drawing
• Shows dimensions, tolerances, and materials
• Shows codes for Group Technology
• Bill of Material
• Lists components, quantities and where used
• Shows product structure

36
Monterey Jack CheeseSpecification
(a) U.S. grade AA. Monterey cheese shall
conform to the following requirements (1)
Flavor. Is fine and highly pleasing, free from
undesirable flavors and odors. May possess a
very slight acid or feed flavor. (2) Body and
texture. A plug drawn from the cheese shall be
reasonably firm. It shall have numerous small
mechanical openings evenly distributed throughout
the plug. It shall not possess sweet holes,
yeast holes, or other gas holes. (3) Color.
Shall have a natural, uniform, bright and
attractive appearance. (4) Finish and appearance
- bandaged and paraffin-dipped. The rind shall
be sound, firm, and smooth providing a good
protection to the cheese.
Code of Federal Regulation, Parts 53 to 109,.
Revised as of Jan. 1, 1985, General Service
Administration
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Engineering Drawings
38
Bills of Material
Panel Weldment
39
Bills of Material
BBQ Bacon Cheeseburger
40
Assembly Drawing

• Shows exploded view of product
• Details relative locations to show how to
  assemble the product

41
Assembly Chart

• Identifies the point of production where
  components flow into subassemblies and ultimately
  into the final product

42
Route Sheet
Lists the operations and times required to
produce a component
43
Work Order
Instructions to produce a given quantity of a
particular item, usually to a schedule
44
Engineering Change Notice (ECN)

• A correction or modification to a products
  definition or documentation
• Engineering drawings
• Bill of material

Quite common with long product life cycles, long
manufacturing lead times, or rapidly changing
technologies
45
Hierarchy of forms
Work Order
ECN
Routing Sheet
Bill of Materials
Bill of Materials
Assembly Drawings
Assembly Drawings
Assembly Chart
Assembly Chart
Assembly Chart
Product
46
Configuration Management

• The need to manage ECNs has led to the
  development of configuration management systems
• A products planned and changing components are
  accurately identified and control and
  accountability for change are identified and
  maintained

47
Product Lifecycle Management

• Integrated software that brings together most, if
  not all, elements of product design and
  manufacture
• Product design
• CAD/CAM, DFMA
• Product routing
• Materials
• Assembly
• Environmental

48
Application of Decision Trees to Product Design

• Particularly useful when there are a series of
  decisions and outcomes which lead to other
  decisions and outcomes

49
Application of Decision Trees to Product Design
Procedures

• Include all possible alternatives and states of
  nature - including doing nothing
• Enter payoffs at end of branch
• Determine the expected value of each branch and
  prune the tree to find the alternative with the
  best expected value

Expected Value EV Probability of Outcome X
Outcome Value
50
Decision Tree Example
High sales 2,500,000 25,000 units
CAD costs 500,000
Cost 40/unit
Low sales 800,000 8,000 units
51
Decision Tree Example
CAD costs 500,000
EMV (purchase CAD system) (.4)(1,000,000)
(.6)(- 20,000) EMV (purchase CAD system) 388,000
52
Decision Tree Example
1,000,000
388,000
-20,000
Sales Mfg cost Net Engs Net net

• 2,500,000
• -1,000,000
• 1,500,000
• -268,000
• 1,232, 000

1,232,000
620,000
800,000 -320,000 480,000 -268,000 212,000
Sales Mfg cost Net Engs Net net
212,000
0
53
Transition to Production

• Know when to move to production
• Product development can be viewed as evolutionary
  and never complete
• Product must move from design to production in a
  timely manner
• Most products have a trial production period to
  insure producibility
• Responsibility must also transition as the
  product moves through its life cycle

54
Chapter 17
Reliability
55
Reliability
The ability of a product, service, part,
subassembly, or system to perform its intended
function under a prescribed set of conditions.
Measured as a probability of proper functioning
a number between (0 and 1) or Mean Time Between
Failure (MTBF). MTBF is the average time
between failures of product or a component or a
system. The bigger this number, usually
measured in hours the more reliable the item or
system.
Important Tactics

• Reliability
• Improving individual components
• Providing redundancy

56
Some Definitions
Operating Conditions the set of conditions under
which the items reliability is
specified. Failure the situation in which the
item, product, part, subassembly, system ceases
to perform as intended. Robust Design that
design of a product or service which allows it
to function over a broad ranges of conditions.
Reliability of a component 1 of Failures
/ of components tested
Example 1000 units tested with 2 failures
Reliability (1 2 / 1000)
1 - .002 0.998
57
Series or Cascaded Components
P1
P2
P3
P4
P5
Pn
Output
Rule 1 Ptotal P1P2P3P4P5 .Pn
Input
What Happens as n goes up?
Example 5 component system
Let P1 .9 P2 .9 P3 .9 P4 .95 P5
.95 Ptotal .657925
58
Redundant Components or Fault Tolerant
Switched Redundancy
P1
Success is when P1 or P2 occurs, i.e., If 1 is
turned and fails 2 is turned on
Input

Output
On/Off
P2
Rule 2 Ptotal P1 (1 P1)xP2 or P2 (1
P2)xP1
Example Let P1 .9 and P2 .8
Ptotal .9 (1 - .9)x.8 .98 or equivalently
.8 (1 -.8)x.9 .98 Note System Reliability
is higher than either component.
59
Redundant Components or Fault Tolerant
Un-switched or total redundancy
P1
Input

Output
P2
P3
Success is when P1 or P2 or P3 occurs or
all three or combinations of the three occur
Rule 3 Ptotal 1 P (of all failing) or 1
(1 - P1)(1 P2)(1 P3)
Example Let P1 .9 P2 .8 and P3 .75
Ptotal 1 (1 - .9)(1 - .8)(1 - .75) 1 -
.005 0.995 Note system reliability is much
higher than the components
60
Example Complex System
Input
What is Aggregate Reliability?
P1
P3
P2

On/Off
P4
P5

P6
P7
Output
P8
61
Example Complex System
Input
What is Aggregate Reliability?
Break it down into calculable subsystems
P1
P3
Subsystem 3
P2

On/Off
P4
P5

Subsystem 1
P6
Subsystem 2
P7
Output
P8
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Input
Psbs2 P3 (1 P3)P4 or P4 (1
P4)P3
Psbs1 P1P2
Subsystem 3
Subsystem 2
Psbs3 1 (1 P5)(1 P6)(1 P7)
(1 P8)
Subsystem 1
Composite System
Output
63
Composite System
Input
Psbs2 P3 (1 P3)P4 or P4 (1
P4)P3 0.97
Psbs1 P1P2 0.855
Subsystem 3
Subsystem 2
Psbs3 1 (1 P5)(1 P6)(1 P7)
(1 P8) 0.993
Subsystem 1
Let P1 .9 P2 .95 P3 .85 P4 .8 P5
.75 P6 .8 P7 .65 P8 .6
Ptotal Psbs1 Psbs2 Psbs3 Ptotal
.855x.97x.993 0.82354
Output
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Everyday Practical System Television
Video
CRT
.99995 .99982
Antenna System
RF Amp.
Tuner
IF Section
Demod
Speakers
Audio
.99998 .99995 .99995
.99995 .99996
Psys .9999590067 video .99952079 audio
.99998 .99975
Micro Chip Audio
Video/ Audio
.999995 .999995 .99999
65
MTBF
Average length of time, hours, between failures
of a of a product or component
Bath Tub Curve
Failures
Time T
0
Old Age Failure due to wear out
Infant Mortality
Normal Period Few Random Failures
66
Reliability with MTBF
Average length of time, hours, between failures
of a product or component
Period during which Reliability can be Modeled
Note exponent in Formula is T/MTBF and is unit
less
Failures
P (no Failure) e -(T/MTBF) P (failure) 1 e
(T/MTBF) e is natural logarithm 2.7183
Time T
0
Old Age Failure due to wear out
Infant Mortality
Normal Period Few Random Failures
67
Chapter 6 Supplement
Statistical Process Control

• Variability is inherent in every process
• Natural or common causes
• Special or assignable causes
• Provides a statistical signal when assignable
  causes are present
• Detect and eliminate assignable causes of
  variation

68
Natural Variations

• Also called common causes
• Affect virtually all production processes
• Expected amount of variation
• Output measures follow a probability distribution
• For any distribution there is a measure of
  central tendency and dispersion
• If the distribution of outputs falls within
  acceptable limits, the process is said to be in
  control

69
Assignable Variations

• Also called special causes of variation
• Generally this is some change in the process
• Variations that can be traced to a specific
  reason
• The objective is to discover when assignable
  causes are present
• Eliminate the bad causes
• Incorporate the good causes

70
Samples
To measure the process, we take samples and
analyze the sample statistics following these
steps
(a) Samples of the product, say five (5) boxes of
cereal taken off the filling machine line, vary
from each other in weight
Finding the weight statistic
71
Samples
To measure the process, we take samples and
analyze the sample statistics following these
steps
(b) After enough samples are taken from a stable
process, they form a pattern called a distribution
72
Samples
To measure the process, we take samples and
analyze the sample statistics following these
steps
(c) There are many types of distributions,
including the normal (bell-shaped) distribution,
but distributions do differ in terms of central
tendency (mean), standard deviation or variance,
and shape
73
Samples
To measure the process, we take samples and
analyze the sample statistics following these
steps
(d) If only natural causes of variation are
present, the output of a process forms a
distribution that is stable over time and is
predictable
Figure S6.1
74
Samples
To measure the process, we take samples and
analyze the sample statistics following these
steps
(e) If assignable causes are present, the process
output is not stable over time and is not
predicable
75
Control Charts
Constructed from historical data, the purpose of
control charts is to help distinguish between
natural variations and variations due to
assignable causes
76
Types of Data
Variables
Attributes

• Characteristics that can take any real value
• May be in whole or in fractional numbers
• Continuous random variables

• Defect-related characteristics
• Classify products as either good or bad or count
  defects
• Categorical or discrete random variables

77
Central Limit Theorem
Regardless of the distribution of the population,
the distribution of sample means drawn from the
population will tend to follow a normal curve
78
Implications of Central Limit Theorem
µn
sn
µ3
µ2
s3
s2
µ1
s1
µ ? µi/n X
s ? si/?n
79
Process Control
80
Population and Sampling Distributions
Distribution of sample means
Figure S6.3
81
Sampling Distribution
-s
s
82
Steps In Creating Control Charts

• Take samples from the population and compute the
  appropriate sample statistic
• Use the sample statistic to calculate control
  limits and draw the control chart
• Plot sample results on the control chart and
  determine the state of the process (in or out of
  control)
• Investigate possible assignable causes and take
  any indicated actions
• Continue sampling from the process and reset the
  control limits when necessary

83
Control Charts for Variables
84
Setting Chart Limits
85
Setting Control Limits
For 99.73 control limits, z 3
X ? Xi 16
86
s
-s
Z
87
Setting Control Limits
Control Chart for sample of 9 boxes
88
Setting Chart Limits
The Max µ min u
89
Control Chart Factors
Table S6.1
90
Setting Control Limits
91
Setting Control Limits
92
Setting Control Limits
93
R Chart

• Type of variables control chart
• Shows sample ranges over time
• Difference between smallest and largest values in
  sample
• Monitors process variability
• Independent from process mean

94
Setting Chart Limits
For R-Charts
95
Setting Control Limits
96
End
97
Mean and Range Charts
Figure S6.5
98
Mean and Range Charts
Figure S6.5
99
Automated Control Charts
100
Control Charts for Attributes

• For variables that are categorical
• Good/bad, yes/no, acceptable/unacceptable
• Measurement is typically counting defectives
• Charts may measure
• Percent defective (p-chart)
• Number of defects (c-chart)

101
Control Limits for p-Charts
Population will be a binomial distribution, but
applying the Central Limit Theorem allows us to
assume a normal distribution for the sample
statistics
102
p-Chart for Data Entry
103
p-Chart for Data Entry
104
p-Chart for Data Entry
Possible assignable causes present
105
Control Limits for c-Charts
Population will be a Poisson distribution, but
applying the Central Limit Theorem allows us to
assume a normal distribution for the sample
statistics
106
c-Chart for Cab Company
107
Patterns in Control Charts
Normal behavior. Process is in control.
Figure S6.7
108
Patterns in Control Charts
One plot out above (or below). Investigate for
cause. Process is out of control.
Figure S6.7
109
Patterns in Control Charts
Trends in either direction, 5 plots. Investigate
for cause of progressive change.
Figure S6.7
110
Patterns in Control Charts
Two plots very near lower (or upper) control.
Investigate for cause.
Figure S6.7
111
Patterns in Control Charts
Run of 5 above (or below) central line.
Investigate for cause.
Figure S6.7
112
Patterns in Control Charts
Erratic behavior. Investigate.
Figure S6.7
113
Which Control Chart to Use
Variables Data
114
Which Control Chart to Use
Attribute Data

• Using the p-chart
• Observations are attributes that can be
  categorized in two states
• We deal with fraction, proportion, or percent
  defectives
• Have several samples, each with many observations

115
Which Control Chart to Use
Attribute Data

• Using a c-Chart
• Observations are attributes whose defects per
  unit of output can be counted
• The number counted is often a small part of the
  possible occurrences
• Defects such as number of blemishes on a desk,
  number of typos in a page of text, flaws in a
  bolt of cloth

116
Process Capability

• The natural variation of a process should be
  small enough to produce products that meet the
  standards required
• A process in statistical control does not
  necessarily meet the design specifications
• Process capability is a measure of the
  relationship between the natural variation of the
  process and the design specifications

117
Process Capability Ratio

• A capable process must have a Cp of at least 1.0
• Does not look at how well the process is centered
  in the specification range
• Often a target value of Cp 1.33 is used to
  allow for off-center processes
• Six Sigma quality requires a Cp 2.0

118
Process Capability Ratio
Insurance claims process
119
Process Capability Ratio
Insurance claims process
120
Process Capability Ratio
Insurance claims process
Process is capable
121
Process Capability Index

• A capable process must have a Cpk of at least 1.0
• A capable process is not necessarily in the
  center of the specification, but it falls within
  the specification limit at both extremes

122
Process Capability Index
New Cutting Machine
123
Process Capability Index
New Cutting Machine
124
Process Capability Index
New Cutting Machine
Both calculations result in
New machine is NOT capable
125
AQL and LTPD

• Acceptable Quality Level (AQL)
• Poorest level of quality we are willing to accept
• Lot Tolerance Percent Defective (LTPD)
• Quality level we consider bad
• Consumer (buyer) does not want to accept lots
  with more defects than LTPD