Chapter 5
Structured Trade-Offs for
Multiple Objective Decisions:
Multi-Attribute Utility Theory
Methods to assign weights to objectives and measures
Methods to create a non-linear single utility function for a
measure when appropriate
Most of the chapter’s tables and figures are included in the file.
Instructor must decide how many and which examples to use.
©Chelst & Canbolat
Value-Added Decision Making
1
MAUT Process
TASKS
• Structure
STEPS
Identify
Requirements
• Describe Alternatives
• Clarify Preferences
• Analyze
Weighted Sum
Synthesize
Determine
Objectives
TECHNIQUES
Identify
Measures
Identify
Alternatives
Gather data for
each alternative for
each measure
Assign
weights
Conduct
Sensitivity
Analysis
Create a common
scale for each
measure
Conduct
Comparative
Analysis
©Chelst & Canbolat
Value-Added Decision Making
Creativity &
Expert Judgment
Individual Analyses
Swing Weight &
Mid-Level Splitting
Evaluate
Hybrid
Alternative(s)
Chapter 5
2
Weights and Utility Functions  Decision
maker(s) preferences

Weights (across objectives and measures)
 reflect the relative value assigned to individual
objectives and individual measures

Utility function (Scale within a measure)
 Deterministic
Reflects relative value (utility) of increasing or decreasing a
measure
Linear utility function is default  relative value is strictly
proportional to the measure
 Probabilistic
Reflects attitude towards risk
9/19/2011
©Chelst & Canbolat
Value-Added Decision Making
3
(Maximize) Additive utility function: A weighted sum of n
different utility functions takes on the following form for
assumed linear additive independence between measures
and objectives:
9/19/2011
©Chelst & Canbolat
Value-Added Decision Making
Chapter 5
4
Assign weights to objectives and measures
Tradeoffs
9/19/2011

Direct assessment of weights

SMART method – swing weights

Top-Down – hierarchical
©Chelst & Canbolat
Value-Added Decision Making
Chapter 5
5
Tradeoffs:Value and Technical
Decision
Process
rs
e
ak ts
M igh
n e
o
i
is s W
c
s
D e sse
A
Value
Tradeoff
9/19/2011
E
Un ngi
de ne
rs e rs
ta
nd & M
Re an
la ag
tio e
n s rs
hi
p
Engineers & Managers
Struggle
©Chelst & Canbolat
Value-Added Decision Making
Technical
Tradeoff
Chapter 5
6
Example of Tradeoff Types :
Cost and Service retail outlet
Technical trade-off
How much will waiting time decrease by adding one more
cashier? (queuing theory)
How much will customer satisfaction improve if waiting time
is reduced by two minutes?
Value trade-off
How much would a company be willing to spend to reduce
waiting time by two minutes?
How much more would a customer be willing to pay to
reduce waiting time by two minutes?
©Chelst & Canbolat
Value-Added Decision Making
Chapter 5
7
Difference Confusing for Experienced
Managers and Decision Makers
Value Tradeoffs are
NOT
Technical Relationship Tradeoffs
Experienced Managers and Designers routinely make
technical relationship tradeoffs.
They are less comfortable with softer issue of value
tradeoffs
©Chelst & Canbolat
Value-Added Decision Making
Chapter 5
8
Example: Light Bulb Selection
Classic tradeoff: Cost vs. Performance

Bill Frail has recently been promoted to a product
development manager position and he will move to his
new office. His new office is being repaired now. He will
select light bulbs for the office. In the office, there are 10
bulb fixtures.
What is the best bulb for Mr. Frail? How much value
does he place on performance relative to cost?
9/19/2011
©Chelst & Canbolat
Value-Added Decision Making
Chapter 5
9
Activity: Directly assign weights to performance
and cost for bulb selection –
Weights must sum to one.
Measure
Weight
Performance
………...
Cost
………...
Sum Total
1
©Chelst & Canbolat
Value-Added Decision Making
Chapter 5
10
Data: Three Alternative Bulbs




9/19/2011
10 lamps each with a bulb, 3000 hours a year per lamp
Bulb life
60 & 75 W bulbs: 1500 hours
(20 bulbs/year)
Long-life 100 W bulb:3000 hours (10 bulbs/year)
Electric rate: $0.10/kWh
Annual Operating Cost: kWh/year*0.10
Bulb
Watt
Annual Operating .
Cost ($)
Annual Purchase
Cost ($)
Total Annual
Cost ($)
60
180
9
189
75
225
10
235
100
300
15
315
©Chelst & Canbolat
Value-Added Decision Making
Chapter 5
11
No alternative is best on both measures
Total Annual Cost ($)
350
300
250
75 Watt
200
150
100
9/19/2011
100 Watt
60 Watt
65
75
Bulb Watts
©Chelst & Canbolat
Value-Added Decision Making
110
Chapter 5
12
Determining Weights: Consider ranges
Swing Weight method – Two Measures

Rank the alternatives by considering the measure ranges

Assign 100 points to the highest ranked measure range

Assess the relative importance of “swinging” the next
highest ranked criterion as a percentage of the highest
ranked selection criterion's 100 point Swing Weight.

Compute each measure’s relative weight by normalizing the
individual Swing Weights.
©Chelst & Canbolat
Value-Added Decision Making
Chapter 5
13
Activity:Assign weights 10 bulbs
 Fill in the table below: look at the ranges & assign points,
 Calculate normalized final weight.
Measure
Least
Preferred
Value
Most
Preferred
Value
Rank
Order
Performance
60w
100w
……
.….
……
Annual Cost
$350
$150
……
.….
……
Total
Points
…..
Final
Weight
1.00
Do not be surprised if there are significant differences from
previously assigned weights that ignored ranges.
©Chelst & Canbolat
Value-Added Decision Making
Chapter 5
14
Repeat Activity:Assign weights for 1000 bulbs
(Inexpensive motel)
 Replace 1000 bulbs – Cost Range Changed
Measure
Least
Preferred
Value
Most
Preferred
Value
Rank
Order
Points
Final
Weight
Performance
60w
100w
…..
…..
…..
Annual Cost
$35,000
$15,000
…..
…..
…..
…..
1
Total
Do not be surprised if there are significant differences from
previously assigned weights.
©Chelst & Canbolat
Value-Added Decision Making
Chapter 5
15
Range specification should impact
assignment of weights

Different weights for different ranges as well as different
decision contexts (office or hotel)

Minimum Range to use when assigning weights:
 Difference between best and worst measures for alternatives
considered.

Preferable: Pick a range that is realistic for the problem
and allows for the possibility of other realistic alternatives
to be added
 The new alternatives may have values outside a too narrowly
specified initial range.
9/19/2011
©Chelst & Canbolat
Value-Added Decision Making
Chapter 5
16
Interpretation of Weights – Range Impact

Assume performance rated highest and cost is 2nd most
important.
 Assume the range on cost was assigned 67 points relative to the
100 points for the highest ranked range
 Weights are 100/167 = .60 and 67/167 = .40

Assume Performance range of 40 watts (60 to 100 watts)
 This means that an alternative earns 0.60 utility units as the
performance increases by 40 watts.
 = Every watt increase adds 0.015 utility to total score

Now imagine the same assigned weights but a broader
range  from 25 to 100 watts
 This means an alternative earns 0.60 utility units as the
performance increases by 75 watts.
 = Every watt increase adds ONLY 0.008
©Chelst & Canbolat
Value-Added Decision Making
Chapter 5
17
Weight Assignment Interview Question

Wrong phrasing: How much weight to cost?

Appropriate phrasing
 A: How much weight for a cost that ranges from $350 to
$150 relative to a performance range of 60 to 100 watts
 B: How much weight for cost that ranges from $400 to
$100 relative to performance range of 60 to 100 watts

Answers to A and B should be different.

Assume 1000 bulbs instead of just 10.
 C: How much weight for cost that ranges from $35000 to
$15000 relative to performance range of 60 to 100 watts
9/19/2011
©Chelst & Canbolat
Value-Added Decision Making
Chapter 5
18
Next step – Common units  Single Utility
Scale the Relative Values of a Measure
Proportional – Linear: DEFAULT assumption
2. Choose curve’s rough shape and evaluate points
3. Mid-level splitting
4. Direct Assessment – for category variables
1.
9/19/2011
©Chelst & Canbolat
Value-Added Decision Making
Chapter 5
19
Proportional Scores (SUF): Bulb Selection

Assign 0 and 1 for worst and best levels of each objective,
respectively
SUFp (100 Watt)=1 (Best) & SUFp (60 Watt)=0 (Worst)
SUFc ($150)=1(Best) &
SUFc ($350)=0 (Worst)
 A general formula for the linear utility score
SUFi
(x i) 
x  WorstValue
BestValue  WorstValue
 The 75 Watt bulb’s performance:
SUFp
(75 Watt) 
75  60
 0.375
100  60
 The 75 Watt bulb’s utility for cost: SUFc (75 Watt) 
9/19/2011
©Chelst & Canbolat
Value-Added Decision Making
235  350
 0.575
150  350
Chapter 5
20
Activity: Proportional Scores for Light Bulb
Cost range was $150 (best) to $350 (worst)
 Calculate the utility score for cost measure for the 60-watt
and 100-watt bulbs
The 60 Watt bulb’s utility for cost:
SUFc ($189)=
The 100 Watt bulb’s utility for cost:
SUFc ($315)=
9/19/2011
©Chelst & Canbolat
Value-Added Decision Making
Chapter 5
21
SUF : Bulb Selection – Linearity Assumptions
Performance
60 Watt Bulb
0
75 Watt Bulb
0.375
100 Watt Bulb
1
Cost
0.805
0.575
0.175
1.00
1.00
100 Watt
0.90
0.80
60 Watt
0.80
0.70
Utility
Utility
0.60
0.50
0.40
0.60
75 Watt
0.40
75 Watt
0.30
100 Watt
0.20
0.20
0.10
0.00
60 Watt
0.00
150
60

9/19/2011
75
100
Performance
(Watt)
200
250
300
350
Cost ($)
Linear Utility Going from 61 to 60 watts performance has the same value in
utility as going from 100 to 99 watts
©Chelst & Canbolat
Value-Added Decision Making
Chapter 5
22
Calculate TOTAL Utility Score

This decision maker less concerned about price and more
concerned about performance. It is his lamps he must see by.

Assigns 0.40 to cost and 0.60 to performance
Calculate the TOTAL utility score for a 60 watt bulb

 U=wpSUFp(Performance)+wcSUFc(Cost)
 U(60 Watt)=0.60*(0.00)+0.40*(0.805)=0.322
 Activity: calculate utility of 75 watt bulb
 __________________________________
 Activity: calculate utility of 100 watt bulb
 __________________________________
©Chelst & Canbolat
Value-Added Decision Making
Chapter 5
23
Calculate Total Utility
U=WpSUFp(Performance)+WcSUFc(Cost)
 The weighted utilities

 U(60 Watt)= 0.60 *(0.000)+0.40*(0.805)=0.322
 U(75 Watt)= 0.60 *(.375)+ 0.40 *(0.575)=0.455
Alternative
Utility
100-Watt Bulb
0.675
75-Watt Bulb
0.454
60-Watt Bulb
0.317
Maximize Performance
Minimize Cost
©Chelst & Canbolat
Value-Added Decision Making
Chapter 5
24
Moneymark – financial service phone line
Activity: Rank, assign points and calculate weights
Strategy
4 staff
5 staff
6 staff
Objective
Minimize
waiting time
Annual Cost (Dollars)
Waiting Time (Minutes)
115,200
144,000
172,800
11.1
1.9
0.5
Least
Most
Rank
Points Weight
Preferred Preferred Order
Measure
Waiting time
(minutes)
Annual cost
Minimize cost
(dollars)
12
0
175,000
115,000
©Chelst & Canbolat
Value-Added Decision Making
Chapter 5
25
Assume 0.4 weight for cost
Waiting
Servers Time Utility
4
11.1
0.075
5
1.9
0.842
6
0.5
0.958
Cost
Dollars Utility
$115,200 0.997
$144,000 0.517
$172,800 0.037
Total Score
Utility
0.444
0.712
0.590
Weights  how much money would manager be willing to spend to
reduce waiting time from 11.1 minutes to 1.9 minutes and to 0.5 minutes)
Alternative
Utility
5 staff
6 staff
4 staff
0.712
0.590
0.444
Waiting Time
Cost
©Chelst & Canbolat
Value-Added Decision Making
Chapter 5
26
Interview Process: Relative importance
weights

Discuss measure ranges
 Define goals and measures
 Provide measure level ranges
 State assumptions

Condition the Responses
 Provide relevant information

Elicit / Verify Responses
 Conduct interview
 Record response / rationale
 Check “belief” of sub-totals
9/19/2011
©Chelst & Canbolat
Value-Added Decision Making
Chapter 5
27
Whose Values and Weights to be traded off?

Decision Maker(s)  represent values organization
 Senior executives

Customer or Subject Matter Expert(s)  reflect
values of the ultimate customer or end user.
 Marketing experts or representative users
 Engineers who understand relationship between design
parameters and performance on key measures of interest.

Financial services phone line
 Waiting time is customer perspective
 Cost is company perspective
9/19/2011
©Chelst & Canbolat
Value-Added Decision Making
Chapter 5
28
Activity – used car: large number of measures
Preferred
Objective
Reliability
Total Cost
Aesthetics
Measure
Least
Most
Mileage
130,000
80,000
Dependability ratings
2 circles
4 circles
Purchase cost
Mpg
Maintenance Annual
Longevity
Color
Interior
Exterior
$6,500
20 mpg
$600
5 years
Dark
Poor
Poor
Neither
works
2
None
$2,500
30 mpg
$400
3 years
Light
Excellent
Excellent
A/C & Heater
Accessories
Seating Capacity
Sound System
Rank
Order
Points
Weight
Measure
Both work
6 or more
Radio & CD
Sum
9/19/2011
©Chelst & Canbolat
Value-Added Decision Making
29
Other Weighting Methods

Direct Tradeoffs
 How much is it worth in dollars to increase the value of
this other measure

Large Hierarchy
 Allocate weight to broad categories with range awareness
Rank order: reliability, total cost, aesthetics, and accessories
Directly assign weights to each major category
 Subdivide the allocation with each category
Within aesthetics  Rank order: color, interior and exterior
Directly assign local weights to each measure
 Global weight = product of objective weight and local
measure weight
9/19/2011
©Chelst & Canbolat
Value-Added Decision Making
Chapter 5
30
Hierarchical Approach – Top down
Objective
Rank
Weights
Objective
3
0.20
Measure
Least
Most
Mileage
125,000
80,000
Dependability
2 circles
4 circles
Purchase cost
$6,000
$1,000
Total Cost
mpg
Maintenance
Longevity
Color
20 mpg
$600
5 years
Dark
30 mpg
$400
3 years
Light
1
0.40
Aesthetics
Interior
Poor
Excellent
4
0.15
Exterior
Poor
Excellent
A/C & Heater
Neither
work
Both work
2
6 or more
None
Radio & CD
Reliability
Accessories Seating
Sound System
2
Sum
9/19/2011
0.25
Rank
Weight
Measure
Weight
Global
2
0.45
0.09
1
0.55
0.11
1
2
4
2
2
0.4
0.25
0.1
0.25
0.4
0.16
0.10
0.04
0.10
0.04
3
0.15
0.015
1
0.45
0.045
3
0.3
0.075
2
0.3
0.075
1
0.4
0.10
1
©Chelst & Canbolat
Value-Added Decision Making
31
Utility or Value Function  common scale

Convert score on each measure to a point on a
zero-to-one scale

Default assumption = linearity or proportional


9/19/2011
Often reasonable assumption or approximation
Construct nonlinear function

Approximate shape

Direct assessment

Mid-level splitting (time consuming)
©Chelst & Canbolat
Value-Added Decision Making
Chapter 5
32
Motivate Need for Non-Linear Utility Function

Home Choice: 3, 4 , or 5 bedrooms
 Are 4 bedrooms midway in value between 3 and 5?

Kitchen remodeling: range is 12 weeks to 18 weeks
 Are 15 weeks midway in value between 12 and 18?

Waiting Time on Phone: range is 0 to 12 minutes
 Is 6 minutes midway in value between 0 and 12?

Suggest a measure with a non-linear utility function
 Describe Context ______________________________
9/19/2011
©Chelst & Canbolat
Value-Added Decision Making
Chapter 5
33
Activity: Direct Assessment - Bedrooms
SUF(5) =1
 SUF(3) = 0
 SUF(4) = ?


Which change produces a greater value improvement?
 If Change 1 – Improve from 3 to 4
SUF(4) > 0.5
 If Change 2 – Improve from 4 to 5
SUF(4) < 0.5


9/19/2011
Which is greater for you _________________
Specify your SUF(4) = _________
©Chelst & Canbolat
Value-Added Decision Making
Chapter 5
34
Common Units of “Utility”
Conversion To Common Units
Single-Measure Utility Function (SUF)
1
Decreasing Rate of Value
Constant Rate of Value
Increasing Rate of Value
Combination
0

9/19/2011
Measure Level
There is no right or wrong SUF for a measure.
 Shape of SUF depends on the context and personal
preferences.
 Preferences should be captured well enough to
understand and analyze the current situation.
©Chelst & Canbolat
Value-Added Decision Making
Chapter 5
35
Management “Targets” for Measures lead
to Non-Linear Utility Function
**Over-Emphasis on achieving
a specific Target leads to an
extremely non-linear utility
function.
 Steep curve near target and
until target is achieved
 Relatively flat curve past target
1
9/19/2011
©Chelst & Canbolat
Value-Added Decision Making
Utility

0
Target
Measure
Shallower SUF allows for
more tradeoff opportunities
Chapter 5
36
Key: SELECT Shape of Curve

Decreasing rate of value - Concave
 Small increments in the measure add “significant” value.
 Less and less value added as approaching most preferred level. (e.g.
each additional bedroom)

Increasing rate of value - Convex
 small increments from least preferred level add “little” value.
 As level improves each additional fixed increment has even greater
value.
 Largest incremental value occurs as measure approaches most
preferred value. (e.g. NBA Draft 24th to 23rd and 2nd to 1st)

Combination – S shaped
 Small increases from least preferred value or as approach most
preferred add significant value. (e.g. acceleration for normal car
driver)
9/19/2011
©Chelst & Canbolat
Value-Added Decision Making
Chapter 5
37
How Much Non-Linear Detail? Office space
1500 sq feet adequate
 Could make do with 1000 sq feet
 Could use extra space up to 2000 sq feet

1.00
Utility
0.75
0.50
0.25
0.00
1000
1200
1400
1600
1800
2000
Floor Area
9/19/2011
©Chelst & Canbolat
Value-Added Decision Making
Chapter 5
38
Mid-Level splitting  Specify Utility and Find Value

Mid-level Splitting: continuous measures

1.
2.
3.
9/19/2011
Divide utility range between 0 and 1 into equal intervals
Determine measure level with 0.5 utility
Determine measure level with 0.25 utility
Determine measure level with 0.75 utility
©Chelst & Canbolat
Value-Added Decision Making
Chapter 5
39
Activity: Mid-Level Splitting - Time Waiting on hold
Step 1
Step 2
Step 3
Utility
Minutes
0
12
0.5
X
1
0
0
12
0.25
Y
0.5
X
Specified in step 1
0.5
X
Specified in step 1
0.75
Z
1
0
©Chelst & Canbolat
Value-Added Decision Making
Specify
X=
Y=
Z=
Chapter 5
40
Nancy Chicila of MONEYMARK – interview
Range from 0 to 12 minutes – Find X0.5
Let M = (Best level + Worst level)/2 = 6 = midpoint of total range
U(0) = 1 and U(12) = 0
Ask which change produces a greater value improvement.
• Change 1: Improve from 12 to 6 min
• Change 2: Improve from 6 to 0
If, for example, the answer is that Change 2 has a greater impact, this implies
U(6) − U(12) < 0.5 and U(0) − U(6) > 0.5
Because U(0) = 1 and U(12) = 0 then
 U(6) < 0.5 and  the “time” value X0.5 < 6 minutes.
In Nancy’s opinion, unless the waiting decreases to less than 5 minutes, the
utility score does not reach 0.5. She sets 4 minutes as the 0.5 level.
 X0.5 = 4.
©Chelst & Canbolat
Value-Added Decision Making
Chapter 5
41
Nancy Chicila of MONEYMARK – interview
Find X0.25 & X0.25
In this example, X0.5 = 4
Calculate midpoint (12 + 4)/2 = 8
• Change 1: Is there greater value in improving from 12 to 8?
• Change 2: Or is there greater value in improving from 8 to 4?
Nancy preferred Change 2.
X0.25 < 8 minutes, and  she set X0.25 = 7.
Calculate midpoint (4 − 0)/2 = 2
• Change 1: Is there greater value in improving from 4 to 2?
• Change 2: Is there greater value in improving from 2 to 0?
Nancy viewed change 2 as more significant, since it eliminated all
waiting time.
She then sets the midpoint at 1.5 min, which means that
 X0.75 = 1.5.
©Chelst & Canbolat
Value-Added Decision Making
Chapter 5
42
Nancy’s Mid-level splitting for waiting on hold
1
1
1
Utility
Utility
Utility
0
0
0
0.
12.
Waiting Time (Minutes)
0.
0.
Waiting Time (Minutes)
Level:
7
Utility:
12.
Waiting Time (Minutes)
12.
Level:
1.5
Utility:
0.75
0.25
Preference Set = NEW PREF. SET
©Chelst & Canbolat
Value-Added Decision Making
Chapter 5
43
Comparison of linear and non-linear utility 
increases difference between 6 and 5 servers
Servers
4
5
6
Time
11.1
1.9
0.5
Utility of time
Linear
Non-Linear
0.08
0.03
0.84
0.70
0.96
0.91
©Chelst & Canbolat
Value-Added Decision Making
Chapter 5
44
Rank ordering unchanged but total score gap
between 1st and 2nd narrowed
Linear Utility for Waiting Time
Alternative Utility
5 staff
0.712
6 staff
0.590
4 staff
0.444
Waiting Time
Cost
Non-linear Utility for Waiting Time
Alternative
Utility
5 staff
6 staff
4 staff
0.626
0.558
0.419
Waiting Time
Cost
©Chelst & Canbolat
Value-Added Decision Making
Chapter 5
45
Two ways of representing Uncertainty

Include Probabilistic data for a specific measure
in the Data Matrix for various alternatives
 Three-Point Estimate
 Discrete Distribution - approximations
 Continuous Distribution

SEPARATE Risk Measure
 Separate Measure – label
Low Risk (Most Preferred),
Medium, and
High Risk (Least preferred)
9/19/2011
©Chelst & Canbolat
Value-Added Decision Making
Inclusion of Uncertainty in MAUT: Bulbs
random number of hours of operation
affects operating cost
Bulb Annual Probability
Operating Cost
$275
0.4
$300
0.3
$350
0.3
©Chelst & Canbolat
Value-Added Decision Making
Separate Risk Measure
Less risky vs more risky alternative
Supplier Choice – More information or Less
Used Car – More variability by brand in reliability
Worker – Current employee vs. new employee for
MGT
 Activity – Example and Context ____________




©Chelst & Canbolat
Value-Added Decision Making
Word description of risk level of suppliers
Word description?
Low risk/uncertainty
Medium risk/uncertainty
High risk/uncertainty
©Chelst & Canbolat
Value-Added Decision Making
Uncertainty: Impact and Implementations in LDW
Uncertainty range may lead to changes in rankings
depending upon which values actually occurred
(example kitchen remodeler)
 Logical Decisions

 Allows for explicit incorporation of values and their
probabilities
 Linear utility function  rankings will be based on
expected value of each uncertain variable
Utility (Expected value) = Expected value of utility
 Non-linear utility  Cannot simply use expected values
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Value-Added Decision Making
50
Uncertainty within measures: Cost & Delay
Input uncertainty into data for measure
Measure
Total labor cost
Total material cost
Build Rite
$34,000
$20,000
0% (p=0.33)
2% (p=0.34)
7% (p=0.33)
13 weeks
Quality Build
$26,000
$12,000
2% (p=0.33)
5% (p=0.34)
9% (p=0.33)
10 weeks
Cost Conscious
$25,000
$10,000
6% (p=0.33)
9% (p=0.34)
15% (p=0.33)
9 weeks
Weeks of delay
On time (p=0.33),
1 week late (p=0.34)
2 weeks late (p=0.33)
1 week late(p=0.33)
2 weeks late(p=0.34)
3 weeks late (p=0.33)
2 weeks late (p=0.33)
3 weeks late (p=0.34)
4 weeks late (p=0.33)
Cleanliness scale
Follow-up and resolution scale
Creativity scale
Brand & store reputation scale
Percent use of subcontractors
Fit and finish scale
Years in business
Quality of references scale
Clean
Adequate
Highly creative
Top of line
25%
Excellent
12 (Good)
Excellent
Messy
Highly responsive
Creative
2nd Best Brand
40%
Good
8 (OK)
Good
Dirty
Adequate
Mundane
2nd Best Brand
65%
Good
22 (Excellent)
OK
Cost overrun history
Duration kitchen unavailable
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Value-Added Decision Making
Chapter 5
51
Figure 5.15: Stacked bar results for kitchen
remodeling
Alternative
Utility
Build Rite
0.651
Quality Build
0.630
Cost Conscious 0.462
Max. Quality
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Min. Cost
Min. Hassle
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Value-Added Decision Making
Kitchen remodeler: uncertainty for cost & delay
 scores overlap in top 2 alternatives
Alternative
Build Rite
Quality Build
Cost Conscious
Utility
0.651
0.630
0.462
©Chelst & Canbolat
Value-Added Decision Making
Chapter 5
53
Group Decision Making: Practical


Influence diagrams and goals hierarchy provide structured
group communication  approach consensus
Decomposition in objectives, measures, weights and utilities
allows for multiple inputs and perspectives



Separates data collection and expert judgment from weighting process
Rationales for weights  Understanding of core differences
Logical Decisions allows the analyst to simultaneously
incorporate separate weights for multiple decision makers

Often even though weights differ, rank orderings may not differ
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Value-Added Decision Making
Chapter 5
54
Additional Concepts
Arrow’s impossibility theorem for consistent
group decisions
 Non-additive utility functions

 Motivation
 Formula
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55
Arrow’s Impossibility Theorem:
Consistent group aggregation of preferences
Arrow and average of preferences
Arrow and majority rule vote
Yes
No
Result
A>B
2
1
A>B
B>C
2
1
B>C
A>C
1
2
A<C
SME 1 SME 2 SME 3 Average
A
1
3
2
2
B
2
1
3
2
C
3
2
1
2
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Value-Added Decision Making
Chapter 5
56
Non-linear additive utility function
Multiplicative form
U= k1u1(x1) + k2u2(x2) + (1- k1- k2) u1(x1) u2(x2)
 Craftsmanship (k1+ k2 < 1)

Gap &Misalignment measures
 Bad on either undermines craftsmanship


Competitiveness (k1+ k2 > 1)
Pricing & Styling measures
 Excellent on either makes products competitive

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Value-Added Decision Making
Chapter 5
57
Craftsmanship (k1+ k2 < 1)
Gaps and Misalignment measures
U= 0.2u1(x1) + 0.2u2(x2) + (0.6)u1(x1) u2(x2)

1
2
3
4
5
Poor on either  undermines craftsmanship
Alternative
Excellent and poor
Very good and weak
Both very good
Both good
Both OK
0.2
Gap
1
0.9
0.9
0.75
0.5
Weights
0.2
Misalignment
0
0.1
0.9
0.75
0.5
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Value-Added Decision Making
0.6
Product
0
.09
0.81
0.56
0.25
Total
0.2
.25
0.85
0.64
0.35
Chapter 5
58
Competitiveness (k1+ k2 > 1)
Pricing & Styling measures
U= 0.8u1(x1) + 0.8u2(x2) + (-0.6) u1(x1) u2(x2)

1
2
3
4
5
Excellence on either  product competitive
Alternative
Excellent and poor
Very good and weak
Both very good
Both good
Both OK
0.8
Price
1
0.9
0.9
0.75
0.5
Weights
0.8
Styling
0
0.1
0.9
0.75
0.5
©Chelst & Canbolat
Value-Added Decision Making
-0.6
Product
0
.09
0.81
0.56
0.25
Total
0.8
.75
0.95
0.86
0.5
Chapter 5
59
MAUT Process
TASKS
• Structure
STEPS
Identify
Requirements
• Describe Alternatives
• Clarify Preferences
• Analyze
Weighted Sum
Synthesize
Determine
Objectives
TECHNIQUES
Identify
Measures
Identify
Alternatives
Gather data for
each alternative for
each measure
Assign
weights
Conduct
Sensitivity
Analysis
Create a common
scale for each
measure
Conduct
Comparative
Analysis
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Value-Added Decision Making
Creativity &
Expert Judgment
Individual Analyses
Swing Weight &
Mid-Level Splitting
Evaluate
Hybrid
Alternative(s)
Next
Chapter
Chapter 5
60
Additional Figures from text
5.8 Used car scores
 5.17, 5.19 and 5.20 Nuclear emergency
management
 5.21 and 5.23 Coating Process

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Value-Added Decision Making
61
Figure 5.8: Ranking of the alternatives
for the used car example
Alternative
Utility
Chevrolet Cavalier
Honda Civic
Ford Ranger
Mazda Miata
0.613
0.470
0.440
0.411
Total Cost
Aesthetics
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Accessories
Reliability
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Value-Added Decision Making
Figure 5.17: Goals hierarchy for nuclear
emergency management case
Thyroid
cancer
Health
Other
cancers
Positive
effects
Overall
SocioPsychological
Negative
effects
Cost
Political
cost
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Value-Added Decision Making
Figure 5.19: Ranking nuclear emergency
management strategies-base case scenario
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Value-Added Decision Making
Figure 5.20: Ranking nuclear emergency
management strategies-worst case scenario
Alternative
9/19/2011
Utility
Strategy 0
0.043
Strategy 1
0.431
Strategy 2
0.636
Strategy 3
0.762
Strategy 4
0.781
Costs
Other cancers
Political cost
Soc. psych negative
Soc. psych positive
Thyroid cancer
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Value-Added Decision Making
Figure 5.21: Goals hierarchy for coating
process selection
Max.
Performance
Max.
Reliability
Select the Best
Coating Process
Min. Cost
Max.
Flexibility
Flexibilityc
Min. Weight
Weight
Max. Coating
Control
Min. Foreign
Material
Foreign Material
Min. Facilities
& Tooling
Facilities &
Tooling Cost
Min. Labor
Labor Cost
Min. Material
Min. Scrap
Min. Development
Time
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c
Coating Control
c
Material Cost
Scrap Cost
Development
Time
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Value-Added Decision Making
Figure 5.23: Stacked bar ranking for the
coating processes
Alternative
Utility
Selective Spray 0.702
Sil-Gel Potting
0.650
Coat and Extract 0.596
Minimize Cost
Maximize Reliability
Minimize Development Time
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Maximize Performance
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Value-Added Decision Making