supplement a - decision making

7/30/2019 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.

Supplement A -

Decision Making




Break-Even Analysis




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




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





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




Break-Even Analysis


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


http://slidepdf.com/reader/full/supplement-a-decision-making 8/74To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Sixth Edition © 2002 Prentice Hall, Inc. All rights reserved.


300 –

200 –

100 –

0 –

Figure A.1


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



Sensitivity Analysis 400 –

300 –

200 –

100 –

0 –


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




300 –

200 –

100 –

0 –

Example A.2


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




300 –

200 –

100 –

0 –


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 )




300 –

200 –

100 –

0 –


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



Make-or-Buy Decisions




Figure A.2




Q = F m – F b

c b – c m

Q = 12,000 – 2,4002.0 – 1.5

Example A.3




Example A.3

Q = F m – F b

c b – c m

Q = 19,200 salads



Preference Matrix



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



Preference Matrix


Market potential 30Unit profit margin 20Operations compatibility 20Competitive advantage 15Investment requirement 10

Project risk 5


Example A.4



Preference Matrix


Market potential 30 8Unit profit margin 20 10Operations compatibility 20 6Competitive advantage 15 10Investment requirement 10 2

Project risk 5 4


Example A.4



Preference Matrix


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


Example A.4



Preference Matrix



Project risk 5 4 20

Weighted score = 750


Example A.4



Preference Matrix



Project risk 5 4 20



Example A.4

< 800



Preference Matrix



Project risk 5 4 20



Example A.4




Preference Matrix

Figure A.3




Under Certainty




Under Certainty

Alternative Low HighSmall facility 200 270Large facility 160 800Do nothing 0 0

PossibleFuture Demand

Example A.5




Under Certainty

Example A.5



If future demand will be low —




Under Certainty

Example A.5



If future demand will be low —Choose the small facility.




Under Uncertainty



Example A.6




Under Uncertainty



Maximin —

Example A.6




Under Uncertainty



Maximin —

Best of the worst

Example A.6




Under Uncertainty



Maximin —Small

Best of the worst

Example A.6




Under Uncertainty



Maximin - SmallMaximax -

Example A.6




Under Uncertainty



Maximin —SmallMaximax —

Best of the best

Example A.6




Under Uncertainty



Maximin —SmallMaximax —Large

Best of the best

Example A.6




Under Uncertainty



Maximin —SmallMaximax —LargeLaplace —

Example A.6




Under Uncertainty




Best weighted payoff

Small facility 0.5(200) + 0.5(270) = 235

Example A.6




Under Uncertainty





Small facility 0.5(200) + 0.5(270) = 235Large facility 0.5(160) + 0.5(800) = 480

Example A.6




Under Uncertainty



Maximin —SmallMaximax —LargeLaplace —Large



Example A.6




Under Uncertainty



Maximin —SmallMaximax —LargeLaplace —LargeMinimax Regret —

Example A.6




Under Uncertainty



Example A.6


Best worst regret

RegretLow Demand High Demand

Small facility 200 – 200 = 0 800 – 270 = 530




Under Uncertainty



Example A.6


Best worst regret


Small facility 200 – 200 = 0 800 – 270 = 530Large facility 200 – 160 = 40 800 – 800 = 0




Under Uncertainty



Example A.6

Maximin —SmallMaximax —LargeLaplace —LargeMinimax Regret —Large

Best worst regret


Small facility 200 – 200 = 0 800 – 270 = 530Large facility 200 – 160 = 40 800 – 800 = 0




Under Uncertainty



Maximin —SmallMaximax —LargeLaplace —LargeMinimax Regret —Large

Example A.6




Under Risk



P small = 0.4

P large = 0.6

Example A.7




Under Risk



P small = 0.4

P large = 0.6

Example A.7

Alternative Expected Value

Small facility 0.4(200) + 0.6(270) = 242




Under Risk



P small = 0.4

P large = 0.6

Example A.7






Under Risk

Highest Expected Value



P small = 0.4

P large = 0.6

Example A.7






Under Risk Figure A.4




Perfect Information



P small = 0.4

P large = 0.6

Example A.8




Perfect Information



P small = 0.4

P large = 0.6

Example A.8

Event Best Payoff

Low demand 200High demand 800




Perfect Information

Event Best Payoff

Low demand 200 EV perfect = 200(0.4) + 800(0.6) = 560High demand 800



P small = 0.4

P large = 0.6

Example A.8




Perfect Information



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




Perfect Information



P small = 0.4

P large = 0.6

Example A.8

Event Best Payoff


Value of perfect information = $560,000 - $544,000




Perfect Information



P small = 0.4

P large = 0.6

Example A.8

Event Best Payoff


Value of perfect information = $16,000




Decision Trees




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




Decision Trees




Decision Trees

1

Example A.9




Decision Trees Low demand [0.4]

Don’t expand

Expand

$200

$223

$270

1

2

Example A.9





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





Don’t expand

Expand

Do nothing

Advertise

$200

$223

$270

$40

$800



$20

$220

High demand [0.6]

1

2

3

Example A.9





Don’t expand

Expand

Do nothing

Advertise

$200

$223

$270

$40

$800



$20

$220

High demand [0.6]

1

2

3

0.3(20) + 0.7(220)

Example A.9




Decision Trees

$160

Low demand [0.4]

Don’t expand

Expand

Do nothing

Advertise

$200

$223

$270

$40

$800



$20

$220

High demand [0.6]

1

2

3

0.3(20) + 0.7(220)

Example A.9




Decision Trees

$160

$160

Low demand [0.4]

Don’t expand

Expand

Do nothing

Advertise

$200

$223

$270

$40

$800



$20

$220

High demand [0.6]

1

2

3

$270

Example A.9




Decision Trees

($160)

$270

$160

Low demand [0.4]

Don’t expand

Expand

Do nothing

Advertise

$200

$223

$270

$40

$800



$20

$220

High demand [0.6]

1

2

3

Example A.9




Decision Trees

($160)

$270

$160

Low demand [0.4]

Don’t expand

Expand

Do nothing

Advertise

$200

$223

$270

$40

$800



$20

$220

High demand [0.6]

1

2

3

0.4(200) + 0.6(270)

0.4(160) + 0.6(800)

Example A.9




Decision Trees

($160)

$270

$160

$242

$544

Low demand [0.4]

Don’t expand

Expand

Do nothing

Advertise

$200

$223

$270

$40

$800



$20

$220

High demand [0.6]

1

2

3

Example A.9

0.4(200) + 0.6(270)

0.4(160) + 0.6(800)




Decision Trees

$160

$270

$160

$544

$242

$544

Low demand [0.4]

Don’t expand

Expand

Do nothing

Advertise

$200

$223

$270

$40

$800



$20

$220

High demand [0.6]

1

2

3

Example A.9




Decision Trees

$160

$270

$160

$544

$242

$544

Low demand [0.4]

Don’t expand

Expand

Do nothing

Advertise

$200

$223

$270

$40

$800



$20

$220

High demand [0.6]

1

2

3

Figure A.6




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



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

supplement a - decision making

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