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To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Sixth Edition © 2002 Prentice Hall, Inc. All rights reserved.
Supplement A -
Decision Making
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To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Sixth Edition © 2002 Prentice Hall, Inc. All rights reserved.
Break-Even Analysis
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Break-Even Analysis
Patients ( Q )
D o
l l a r s
( i n t h o u s a n
d s
)
400 –
300 –
200 –
100 –
0 – | | | |500 1000 1500 2000
Example A.1
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To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Sixth Edition © 2002 Prentice Hall, Inc. All rights reserved.
Break-Even Analysis 400 –
300 –
200 –
100 –
0 –
Patients ( Q )
D o
l l a r s
( i n t h o u s a n
d s
)
| | | |500 1000 1500 2000
Example A.1
Quantity Total Annual Total Annual
(patients) Cost ($) Revenue ($)(Q ) (100,000 + 100 Q ) (200 Q )
0 100,000 02000 300,000 400,000
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To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Sixth Edition © 2002 Prentice Hall, Inc. All rights reserved.
Break-Even Analysis 400 –
300 –
200 –
100 –
0 –
Total annual revenues
Patients ( Q )
D o
l l a r s
( i n t h o u s a n
d s
)
| | | |500 1000 1500 2000
(2000, 400)
Example A.1
Quantity Total Annual Total Annual(patients) Cost ($) Revenue ($)
(Q ) (100,000 + 100 Q ) (200 Q )
0 100,000 02000 300,000 400,000
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To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Sixth Edition © 2002 Prentice Hall, Inc. All rights reserved.
Break-Even Analysis
Total annual revenues
Total annual costs
Patients ( Q )
D o
l l a r s
( i n t h o u s a n
d s
)
400 –
300 –
200 –
100 –
0 – | | | |500 1000 1500 2000
Fixed costs
(2000, 400)
(2000, 300)
Example A.1
Quantity Total Annual Total Annual(patients) Cost ($) Revenue ($)
(Q ) (100,000 + 100 Q ) (200 Q )0 100,000 0
2000 300,000 400,000
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Break-Even Analysis 400 –
300 –
200 –
100 –
0 –
Figure A.1
Total annual revenues
Total annual costs
Patients ( Q )
D o
l l a r s
( i n t h o u s a n
d s
)
| | | |500 1000 1500 2000
Fixed costs
Break-even quantity
(2000, 400)
(2000, 300)
Profits
Loss
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Sensitivity Analysis 400 –
300 –
200 –
100 –
0 –
Total annual revenues
Total annual costs
Patients ( Q )
D o
l l a r s
( i n t h o u s a n
d s
)
| | | |500 1000 1500 2000
Fixed costs
Profits
Loss
Example A.2
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Sensitivity Analysis 400 –
300 –
200 –
100 –
0 –
Example A.2
Total annual revenues
Total annual costs
Patients ( Q )
D o
l l a r s
( i n t h o u s a n
d s
)
| | | |500 1000 1500 2000
Fixed costs
Profits
Loss
Forecast = 1,500
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Sensitivity Analysis 400 –
300 –
200 –
100 –
0 –
Total annual revenues
Total annual costs
Patients ( Q )
D o
l l a r s
( i n t h o u s a n
d s
)
| | | |500 1000 1500 2000
Fixed costs
Profits
Loss
Example A.2
Forecast = 1,500
pQ – ( F + cQ )
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Sensitivity Analysis 400 –
300 –
200 –
100 –
0 –
Total annual revenues
Total annual costs
Patients ( Q )
D o
l l a r s
( i n t h o u s a n
d s
)
| | | |500 1000 1500 2000
Fixed costs
Profits
Loss
Example A.2
Forecast = 1,500
pQ – ( F + cQ )
200(1500) – [100,000 + 100(1500)]
$50,000
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Make-or-Buy Decisions
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Make-or-Buy Decisions
Figure A.2
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Make-or-Buy Decisions
Q = F m – F b
c b – c m
Q = 12,000 – 2,4002.0 – 1.5
Example A.3
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Make-or-Buy Decisions
Example A.3
Q = F m – F b
c b – c m
Q = 19,200 salads
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Preference Matrix
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Preference Matrix
Performance Weight Score Weighted ScoreCriterion ( A) (B ) (A x B )
Market potentialUnit profit marginOperations compatibilityCompetitive advantageInvestment requirement
Project risk
Threshold score = 800
Example A.4
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Preference Matrix
Performance Weight Score Weighted ScoreCriterion ( A) (B ) (A x B )
Market potential 30Unit profit margin 20Operations compatibility 20Competitive advantage 15Investment requirement 10
Project risk 5
Threshold score = 800
Example A.4
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Preference Matrix
Performance Weight Score Weighted ScoreCriterion ( A) (B ) (A x B )
Market potential 30 8Unit profit margin 20 10Operations compatibility 20 6Competitive advantage 15 10Investment requirement 10 2
Project risk 5 4
Threshold score = 800
Example A.4
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Preference Matrix
Performance Weight Score Weighted ScoreCriterion ( A) (B ) (A x B )
Market potential 30 8 240Unit profit margin 20 10 200Operations compatibility 20 6 120Competitive advantage 15 10 150Investment requirement 10 2 20
Project risk 5 4 20
Threshold score = 800
Example A.4
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Preference Matrix
Performance Weight Score Weighted ScoreCriterion ( A) (B ) (A x B )
Market potential 30 8 240Unit profit margin 20 10 200Operations compatibility 20 6 120Competitive advantage 15 10 150Investment requirement 10 2 20
Project risk 5 4 20
Weighted score = 750
Threshold score = 800
Example A.4
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Preference Matrix
Performance Weight Score Weighted ScoreCriterion ( A) (B ) (A x B )
Market potential 30 8 240Unit profit margin 20 10 200Operations compatibility 20 6 120Competitive advantage 15 10 150Investment requirement 10 2 20
Project risk 5 4 20
Weighted score = 750
Threshold score = 800
Example A.4
< 800
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Preference Matrix
Performance Weight Score Weighted ScoreCriterion ( A) (B ) (A x B )
Market potential 30 8 240Unit profit margin 20 10 200Operations compatibility 20 6 120Competitive advantage 15 10 150Investment requirement 10 2 20
Project risk 5 4 20
Weighted score = 750
Threshold score = 800
Example A.4
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Preference Matrix
Figure A.3
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Under Certainty
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Under Certainty
Alternative Low HighSmall facility 200 270Large facility 160 800Do nothing 0 0
PossibleFuture Demand
Example A.5
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Under Certainty
Example A.5
Alternative Low HighSmall facility 200 270Large facility 160 800Do nothing 0 0
PossibleFuture Demand
If future demand will be low —
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Under Certainty
Example A.5
Alternative Low HighSmall facility 200 270Large facility 160 800Do nothing 0 0
PossibleFuture Demand
If future demand will be low —Choose the small facility.
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Under Certainty
Example A.5
Alternative Low HighSmall facility 200 270Large facility 160 800Do nothing 0 0
PossibleFuture Demand
If future demand will be low —Choose the small facility.
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Under Uncertainty
Alternative Low HighSmall facility 200 270Large facility 160 800Do nothing 0 0
PossibleFuture Demand
Example A.6
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Under Uncertainty
Alternative Low HighSmall facility 200 270Large facility 160 800Do nothing 0 0
PossibleFuture Demand
Maximin —
Example A.6
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Under Uncertainty
Alternative Low HighSmall facility 200 270Large facility 160 800Do nothing 0 0
PossibleFuture Demand
Maximin —
Best of the worst
Example A.6
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Under Uncertainty
Alternative Low HighSmall facility 200 270Large facility 160 800Do nothing 0 0
PossibleFuture Demand
Maximin —Small
Best of the worst
Example A.6
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Under Uncertainty
Alternative Low HighSmall facility 200 270Large facility 160 800Do nothing 0 0
PossibleFuture Demand
Maximin - SmallMaximax -
Example A.6
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Under Uncertainty
Alternative Low HighSmall facility 200 270Large facility 160 800Do nothing 0 0
PossibleFuture Demand
Maximin —SmallMaximax —
Best of the best
Example A.6
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Under Uncertainty
Alternative Low HighSmall facility 200 270Large facility 160 800Do nothing 0 0
PossibleFuture Demand
Maximin —SmallMaximax —Large
Best of the best
Example A.6
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Under Uncertainty
Alternative Low HighSmall facility 200 270Large facility 160 800Do nothing 0 0
PossibleFuture Demand
Maximin —SmallMaximax —LargeLaplace —
Example A.6
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Under Uncertainty
Alternative Low HighSmall facility 200 270Large facility 160 800Do nothing 0 0
PossibleFuture Demand
Maximin —SmallMaximax —LargeLaplace —
Best weighted payoff
Small facility 0.5(200) + 0.5(270) = 235
Example A.6
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Under Uncertainty
Alternative Low HighSmall facility 200 270Large facility 160 800Do nothing 0 0
PossibleFuture Demand
Maximin —SmallMaximax —LargeLaplace —
Best weighted payoff
Small facility 0.5(200) + 0.5(270) = 235Large facility 0.5(160) + 0.5(800) = 480
Example A.6
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Under Uncertainty
Alternative Low HighSmall facility 200 270Large facility 160 800Do nothing 0 0
PossibleFuture Demand
Maximin —SmallMaximax —LargeLaplace —Large
Best weighted payoff
Small facility 0.5(200) + 0.5(270) = 235Large facility 0.5(160) + 0.5(800) = 480
Example A.6
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Under Uncertainty
Alternative Low HighSmall facility 200 270Large facility 160 800Do nothing 0 0
PossibleFuture Demand
Maximin —SmallMaximax —LargeLaplace —LargeMinimax Regret —
Example A.6
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Under Uncertainty
Alternative Low HighSmall facility 200 270Large facility 160 800Do nothing 0 0
PossibleFuture Demand
Example A.6
Maximin —SmallMaximax —LargeLaplace —LargeMinimax Regret —
Best worst regret
RegretLow Demand High Demand
Small facility 200 – 200 = 0 800 – 270 = 530
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Under Uncertainty
Alternative Low HighSmall facility 200 270Large facility 160 800Do nothing 0 0
PossibleFuture Demand
Example A.6
Maximin —SmallMaximax —LargeLaplace —LargeMinimax Regret —
Best worst regret
RegretLow Demand High Demand
Small facility 200 – 200 = 0 800 – 270 = 530Large facility 200 – 160 = 40 800 – 800 = 0
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Under Uncertainty
Alternative Low HighSmall facility 200 270Large facility 160 800Do nothing 0 0
PossibleFuture Demand
Example A.6
Maximin —SmallMaximax —LargeLaplace —LargeMinimax Regret —Large
Best worst regret
RegretLow Demand High Demand
Small facility 200 – 200 = 0 800 – 270 = 530Large facility 200 – 160 = 40 800 – 800 = 0
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Under Uncertainty
Alternative Low HighSmall facility 200 270Large facility 160 800Do nothing 0 0
PossibleFuture Demand
Maximin —SmallMaximax —LargeLaplace —LargeMinimax Regret —Large
Example A.6
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Under Risk
Alternative Low HighSmall facility 200 270Large facility 160 800Do nothing 0 0
PossibleFuture Demand
P small = 0.4
P large = 0.6
Example A.7
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Under Risk
Alternative Low HighSmall facility 200 270Large facility 160 800Do nothing 0 0
PossibleFuture Demand
P small = 0.4
P large = 0.6
Example A.7
Alternative Expected Value
Small facility 0.4(200) + 0.6(270) = 242
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Under Risk
Alternative Low HighSmall facility 200 270Large facility 160 800Do nothing 0 0
PossibleFuture Demand
P small = 0.4
P large = 0.6
Example A.7
Alternative Expected Value
Small facility 0.4(200) + 0.6(270) = 242Large facility 0.4(160) + 0.6(800) = 544
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Under Risk
Highest Expected Value
Alternative Low HighSmall facility 200 270Large facility 160 800Do nothing 0 0
PossibleFuture Demand
P small = 0.4
P large = 0.6
Example A.7
Alternative Expected Value
Small facility 0.4(200) + 0.6(270) = 242Large facility 0.4(160) + 0.6(800) = 544
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Under Risk Figure A.4
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Perfect Information
Alternative Low HighSmall facility 200 270Large facility 160 800Do nothing 0 0
PossibleFuture Demand
P small = 0.4
P large = 0.6
Example A.8
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Perfect Information
Alternative Low HighSmall facility 200 270Large facility 160 800Do nothing 0 0
PossibleFuture Demand
P small = 0.4
P large = 0.6
Example A.8
Event Best Payoff
Low demand 200High demand 800
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Perfect Information
Event Best Payoff
Low demand 200 EV perfect = 200(0.4) + 800(0.6) = 560High demand 800
Alternative Low HighSmall facility 200 270Large facility 160 800Do nothing 0 0
PossibleFuture Demand
P small = 0.4
P large = 0.6
Example A.8
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Perfect Information
Alternative Low HighSmall facility 200 270Large facility 160 800Do nothing 0 0
PossibleFuture Demand
P small = 0.4
P large = 0.6
Example A.8
Event Best Payoff
Low demand 200 EV perfect = 200(0.4) + 800(0.6) = 560High demand 800 EV imperfect = 160(0.4) + 800(0.6) = 544
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Perfect Information
Alternative Low HighSmall facility 200 270Large facility 160 800Do nothing 0 0
PossibleFuture Demand
P small = 0.4
P large = 0.6
Example A.8
Event Best Payoff
Low demand 200 EV perfect = 200(0.4) + 800(0.6) = 560High demand 800 EV imperfect = 160(0.4) + 800(0.6) = 544
Value of perfect information = $560,000 - $544,000
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Perfect Information
Alternative Low HighSmall facility 200 270Large facility 160 800Do nothing 0 0
PossibleFuture Demand
P small = 0.4
P large = 0.6
Example A.8
Event Best Payoff
Low demand 200 EV perfect = 200(0.4) + 800(0.6) = 560High demand 800 EV imperfect = 160(0.4) + 800(0.6) = 544
Value of perfect information = $16,000
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Decision Trees
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Decision Trees
= Event node
= Decision node
E i = Event i
P (E i ) = Probability of event i
1stdecision Possible
2nd decision
Payoff 1
Payoff 2
Payoff 3
Alternative 3
Alternative 4
Alternative 5
Payoff 1
Payoff 2
Payoff 3
E 1 [P (E 1)]
E 2 [P (E 2)]
E 3 [P (E 3)]
E 2 [P (E 2)]E 3 [P (E 3)]
Payoff 1
Payoff 2
1 2
Figure A.5
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Decision Trees
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Decision Trees
1
Example A.9
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Decision Trees Low demand [0.4]
Don’t expand
Expand
$200
$223
$270
1
2
Example A.9
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Decision Trees Low demand [0.4]
Don’t expand
Expand
Do nothing
Advertise
$200
$223
$270
$40
$800
Modest response [0.3]
Sizable response [0.7]
$20
$220
High demand [0.6]
1
2
3
Example A.9
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Decision Trees Low demand [0.4]
Don’t expand
Expand
Do nothing
Advertise
$200
$223
$270
$40
$800
Modest response [0.3]
Sizable response [0.7]
$20
$220
High demand [0.6]
1
2
3
Example A.9
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Decision Trees Low demand [0.4]
Don’t expand
Expand
Do nothing
Advertise
$200
$223
$270
$40
$800
Modest response [0.3]
Sizable response [0.7]
$20
$220
High demand [0.6]
1
2
3
0.3(20) + 0.7(220)
Example A.9
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Decision Trees
$160
Low demand [0.4]
Don’t expand
Expand
Do nothing
Advertise
$200
$223
$270
$40
$800
Modest response [0.3]
Sizable response [0.7]
$20
$220
High demand [0.6]
1
2
3
0.3(20) + 0.7(220)
Example A.9
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Decision Trees
$160
$160
Low demand [0.4]
Don’t expand
Expand
Do nothing
Advertise
$200
$223
$270
$40
$800
Modest response [0.3]
Sizable response [0.7]
$20
$220
High demand [0.6]
1
2
3
$270
Example A.9
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Decision Trees
($160)
$270
$160
Low demand [0.4]
Don’t expand
Expand
Do nothing
Advertise
$200
$223
$270
$40
$800
Modest response [0.3]
Sizable response [0.7]
$20
$220
High demand [0.6]
1
2
3
Example A.9
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Decision Trees
($160)
$270
$160
Low demand [0.4]
Don’t expand
Expand
Do nothing
Advertise
$200
$223
$270
$40
$800
Modest response [0.3]
Sizable response [0.7]
$20
$220
High demand [0.6]
1
2
3
0.4(200) + 0.6(270)
0.4(160) + 0.6(800)
Example A.9
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Decision Trees
($160)
$270
$160
$242
$544
Low demand [0.4]
Don’t expand
Expand
Do nothing
Advertise
$200
$223
$270
$40
$800
Modest response [0.3]
Sizable response [0.7]
$20
$220
High demand [0.6]
1
2
3
Example A.9
0.4(200) + 0.6(270)
0.4(160) + 0.6(800)
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Decision Trees
$160
$270
$160
$544
$242
$544
Low demand [0.4]
Don’t expand
Expand
Do nothing
Advertise
$200
$223
$270
$40
$800
Modest response [0.3]
Sizable response [0.7]
$20
$220
High demand [0.6]
1
2
3
Example A.9
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Decision Trees
$160
$270
$160
$544
$242
$544
Low demand [0.4]
Don’t expand
Expand
Do nothing
Advertise
$200
$223
$270
$40
$800
Modest response [0.3]
Sizable response [0.7]
$20
$220
High demand [0.6]
1
2
3
Figure A.6
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Solved Problem 1250 –
200 –
150 –
100 –
50 –
0 –
Figure A.7
Total revenues
Total costs
Units (in thousands)
D o
l l a r s
( i n t h o u s a n
d s
)
| | | | | | | |1 2 3 4 5 6 7 8
Break-evenquantity
3.1
$77.7
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Solved Problem 4
Figure A.8
Bad times [0.3]
Normal times [0.5]
Good times [0.2]
One lift
Two lifts
Bad times [0.3]
Normal times [0.5]
Good times [0.2]
$256.0
$225.3
$256.0
$191
$240
$240
$151
$245
$441