1. Decision Making Supplement A
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2. Break-Even Analysis
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3. Break-Even Analysis Patients (Q) Dollars (in thousands) 400 – 300 –
200 –
100 –
0 –
| | | |
Example A.1
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4. Break-Even Analysis Quantity Total Annual Total Annual
(patients) Cost ($) Revenue ($)
(Q) (100, Q) (200Q)
0 100,000 0
, ,000
400 –
300 –
200 –
100 –
0 –
Patients (Q)
Dollars (in thousands)
| | | |
Example A.1
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5. Break-Even Analysis Quantity Total Annual Total Annual
(patients) Cost ($) Revenue ($)
(Q) (100, Q) (200Q)
0 100,000 0
, ,000
400 –
300 –
200 –
100 –
0 –
Total annual revenues
Patients (Q)
Dollars (in thousands)
| | | |
(2000, 400)
Example A.1
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6. Break-Even Analysis Total annual revenues Total annual costs
Patients (Q)
Dollars (in thousands)
400 –
300 –
200 –
100 –
0 –
| | | |
Fixed costs
(2000, 400)
(2000, 300)
Example A.1
Quantity Total Annual Total Annual
(patients) Cost ($) Revenue ($)
(Q) (100, Q) (200Q)
0 100,000 0
, ,000
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7. Break-Even Analysis Break-even quantity Total annual revenues
Total annual costs
Patients (Q)
Dollars (in thousands)
400 –
300 –
200 –
100 –
0 –
| | | |
Fixed costs
Break-even quantity
(2000, 400)
(2000, 300)
Profits
Loss
Example A.1
Quantity Total Annual Total Annual
(patients) Cost ($) Revenue ($)
(Q) (100, Q) (200Q)
0 100,000 0
, ,000
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8. Break-Even Analysis Break-even quantity 400 – 300 – 200 – 100 – 0 –
Figure A.1
Total annual revenues
Total annual costs
Patients (Q)
Dollars (in thousands)
| | | |
Fixed costs
Break-even quantity
(2000, 400)
(2000, 300)
Profits
Loss
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9. Sensitivity Analysis 400 – 300 – 200 – 100 – 0 – Total annual revenues
Total annual costs
Patients (Q)
Dollars (in thousands)
| | | |
Fixed costs
Profits
Loss
Example A.2
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10. Sensitivity Analysis Forecast = 1,500 400 – 300 – 200 – 100 – 0 –
Example A.2
Total annual revenues
Total annual costs
Patients (Q)
Dollars (in thousands)
| | | |
Fixed costs
Profits
Loss
Forecast = 1,500
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11. Sensitivity Analysis Forecast = 1,500 pQ – (F + cQ) 400 – 300 – 200 –
100 –
0 –
Total annual revenues
Total annual costs
Patients (Q)
Dollars (in thousands)
| | | |
Fixed costs
Profits
Loss
Example A.2
Forecast = 1,500
pQ – (F + cQ)
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12. Sensitivity Analysis Forecast = 1,500 pQ – (F + cQ)
400 –
300 –
200 –
100 –
0 –
Total annual revenues
Total annual costs
Patients (Q)
Dollars (in thousands)
| | | |
Fixed costs
Profits
Loss
Example A.2
Forecast = 1,500
pQ – (F + cQ)
200(1500) – [100, (1500)]
$50,000
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13. Make-or-Buy Decisions
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14. Make-or-Buy Decisions
Figure A.2
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15. Make-or-Buy Decisions
Q =
Fm – Fb
cb – cm
12,000 – 2,400
2.0 – 1.5
Example A.3
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16. Make-or-Buy Decisions
Q =
Fm – Fb
cb – cm
Q = 19,200 salads
Example A.3
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17. Preference Matrix
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18. Preference Matrix Threshold score = 800
Performance Weight Score Weighted Score
Criterion (A) (B) (A x B)
Market potential
Unit profit margin
Operations compatibility
Competitive advantage
Investment requirement
Project risk
Threshold score = 800
Example A.4
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19. Preference Matrix Threshold score = 800
Performance Weight Score Weighted Score
Criterion (A) (B) (A x B)
Market potential 30
Unit profit margin 20
Operations compatibility 20
Competitive advantage 15
Investment requirement 10
Project risk 5
Threshold score = 800
Example A.4
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20. Preference Matrix Threshold score = 800
Performance Weight Score Weighted Score
Criterion (A) (B) (A x B)
Market potential 30 8
Unit profit margin 20 10
Operations compatibility 20 6
Competitive advantage 15 10
Investment requirement 10 2
Project risk 5 4
Threshold score = 800
Example A.4
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21. Preference Matrix Threshold score = 800
Performance Weight Score Weighted Score
Criterion (A) (B) (A x B)
Market potential
Unit profit margin
Operations compatibility
Competitive advantage
Investment requirement
Project risk
Threshold score = 800
Example A.4
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22. Preference Matrix Threshold score = 800
Performance Weight Score Weighted Score
Criterion (A) (B) (A x B)
Market potential
Unit profit margin
Operations compatibility
Competitive advantage
Investment requirement
Project risk
Weighted score = 750
Threshold score = 800
Example A.4
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23. Preference Matrix < 800 Threshold score = 800
Performance Weight Score Weighted Score
Criterion (A) (B) (A x B)
Market potential
Unit profit margin
Operations compatibility
Competitive advantage
Investment requirement
Project risk
Weighted score = 750
Threshold score = 800
Example A.4
< 800
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24. Not At This Time Preference Matrix Threshold score = 800
Performance Weight Score Weighted Score
Criterion (A) (B) (A x B)
Market potential
Unit profit margin
Operations compatibility
Competitive advantage
Investment requirement
Project risk
Weighted score = 750
Threshold score = 800
Example A.4
Not At This Time
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25. Preference Matrix Figure A.3
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26. Under Certainty
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27. Under Certainty Possible Future Demand Alternative Low High
Small facility
Large facility
Do nothing 0 0
Possible
Future Demand
Example A.5
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28. Under Certainty Possible Future Demand Alternative Low High
Small facility
Large facility
Do nothing 0 0
Possible
Future Demand
If future demand will be low –
Example A.5
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29. Under Certainty Possible Future Demand Alternative Low High
Small facility
Large facility
Do nothing 0 0
Possible
Future Demand
If future demand will be low – Choose the small facility.
Example A.5
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30. Under Certainty Possible Future Demand Alternative Low High
Small facility
Large facility
Do nothing 0 0
Possible
Future Demand
If future demand will be low – Choose the small facility.
Example A.5
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31. Under Uncertainty Possible Future Demand Alternative Low High
Small facility
Large facility
Do nothing 0 0
Possible
Future Demand
Example A.6
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32. Under Uncertainty Possible Future Demand Alternative Low High
Small facility
Large facility
Do nothing 0 0
Possible
Future Demand
Maximin –
Example A.6
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33. Under Uncertainty Best of the worst Possible Future Demand
Alternative Low High
Small facility
Large facility
Do nothing 0 0
Possible
Future Demand
Maximin –
Best of the worst
Example A.6
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34. Under Uncertainty Best of the worst Possible Future Demand
Alternative Low High
Small facility
Large facility
Do nothing 0 0
Possible
Future Demand
Maximin – Small
Best of the worst
Example A.6
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35. Under Uncertainty Possible Future Demand Alternative Low High
Small facility
Large facility
Do nothing 0 0
Possible
Future Demand
Maximin – Small
Maximax –
Example A.6
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36. Under Uncertainty Best of the best Possible Future Demand
Alternative Low High
Small facility
Large facility
Do nothing 0 0
Possible
Future Demand
Maximin – Small
Maximax –
Best of the best
Example A.6
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37. Under Uncertainty Best of the best Possible Future Demand
Alternative Low High
Small facility
Large facility
Do nothing 0 0
Possible
Future Demand
Maximin – Small
Maximax – Large
Best of the best
Example A.6
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38. Under Uncertainty Possible Future Demand Alternative Low High
Small facility
Large facility
Do nothing 0 0
Possible
Future Demand
Maximin – Small
Maximax – Large
Laplace –
Example A.6
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39. Under Uncertainty Best weighted payoff Possible Future Demand
Alternative Low High
Small facility
Large facility
Do nothing 0 0
Possible
Future Demand
Maximin – Small
Maximax – Large
Laplace –
Best weighted payoff
Small facility 0.5(200) + 0.5(270) = 235
Example A.6
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40. Under Uncertainty Best weighted payoff Possible Future Demand
Alternative Low High
Small facility
Large facility
Do nothing 0 0
Possible
Future Demand
Maximin – Small
Maximax – Large
Laplace –
Best weighted payoff
Small facility 0.5(200) + 0.5(270) = 235
Large facility 0.5(160) + 0.5(800) = 480
Example A.6
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41. Under Uncertainty Best weighted payoff Possible Future Demand
Alternative Low High
Small facility
Large facility
Do nothing 0 0
Possible
Future Demand
Maximin – Small
Maximax – Large
Laplace – Large
Best weighted payoff
Small facility 0.5(200) + 0.5(270) = 235
Large facility 0.5(160) + 0.5(800) = 480
Example A.6
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42. Under Uncertainty Possible Future Demand Alternative Low High
Small facility
Large facility
Do nothing 0 0
Possible
Future Demand
Maximin – Small
Maximax – Large
Laplace – Large
Minimax Regret –
Example A.6
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43. Under Uncertainty Best worst regret Possible Future Demand
Alternative Low High
Small facility
Large facility
Do nothing 0 0
Possible
Future Demand
Example A.6
Maximin – Small
Maximax – Large
Laplace – Large
Minimax Regret –
Best worst regret
Regret
Low Demand High Demand
Small facility 200 – 200 = – 270 = 530
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44. Under Uncertainty Best worst regret Possible Future Demand
Alternative Low High
Small facility
Large facility
Do nothing 0 0
Possible
Future Demand
Example A.6
Maximin – Small
Maximax – Large
Laplace – Large
Minimax Regret –
Best worst regret
Regret
Low Demand High Demand
Small facility 200 – 200 = – 270 = 530
Large facility 200 – 160 = – 800 = 0
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45. Under Uncertainty Best worst regret Possible Future Demand
Alternative Low High
Small facility
Large facility
Do nothing 0 0
Possible
Future Demand
Example A.6
Maximin – Small
Maximax – Large
Laplace – Large
Minimax Regret – Large
Best worst regret
Regret
Low Demand High Demand
Small facility 200 – 200 = – 270 = 530
Large facility 200 – 160 = – 800 = 0
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46. Under Uncertainty Possible Future Demand Alternative Low High
Small facility
Large facility
Do nothing 0 0
Possible
Future Demand
Maximin – Small
Maximax – Large
Laplace – Large
Minimax Regret – Large
Example A.6
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47. Under Risk Possible Future Demand Alternative Low High
Small facility
Large facility
Do nothing 0 0
Possible
Future Demand
Psmall = 0.4
Plarge = 0.6
Example A.7
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48. Under Risk Possible Future Demand Alternative Low High
Small facility
Large facility
Do nothing 0 0
Possible
Future Demand
Psmall = 0.4
Plarge = 0.6
Example A.7
Alternative Expected Value
Small facility 0.4(200) + 0.6(270) = 242
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49. Under Risk Possible Future Demand Alternative Low High
Small facility
Large facility
Do nothing 0 0
Possible
Future Demand
Psmall = 0.4
Plarge = 0.6
Example A.7
Alternative Expected Value
Small facility 0.4(200) + 0.6(270) = 242
Large facility 0.4(160) + 0.6(800) = 544
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50. Under Risk Highest Expected Value Possible Future Demand
Alternative Low High
Small facility
Large facility
Do nothing 0 0
Possible
Future Demand
Psmall = 0.4
Plarge = 0.6
Example A.7
Alternative Expected Value
Small facility 0.4(200) + 0.6(270) = 242
Large facility 0.4(160) + 0.6(800) = 544
Highest Expected Value
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51. Under Risk
Figure A.4
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52. Perfect Information Possible Future Demand Alternative Low High
Small facility
Large facility
Do nothing 0 0
Possible
Future Demand
Psmall = 0.4
Plarge = 0.6
Example A.8
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53. Perfect Information Possible Future Demand Alternative Low High
Small facility
Large facility
Do nothing 0 0
Possible
Future Demand
Psmall = 0.4
Plarge = 0.6
Example A.8
Event Best Payoff
Low demand 200
High demand 800
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54. Perfect Information Possible Future Demand Alternative Low High
Small facility
Large facility
Do nothing 0 0
Possible
Future Demand
Psmall = 0.4
Plarge = 0.6
Example A.8
Event Best Payoff
Low demand 200 EVperfect = 200(0.4) + 800(0.6) = 560
High demand 800
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55. Perfect Information Possible Future Demand Alternative Low High
Small facility
Large facility
Do nothing 0 0
Possible
Future Demand
Psmall = 0.4
Plarge = 0.6
Example A.8
Event Best Payoff
Low demand 200 EVperfect = 200(0.4) + 800(0.6) = 560
High demand 800 EVimperfect = 160(0.4) + 800(0.6) = 544
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56. Perfect Information Possible Future Demand Alternative Low High
Small facility
Large facility
Do nothing 0 0
Possible
Future Demand
Psmall = 0.4
Plarge = 0.6
Example A.8
Event Best Payoff
Low demand 200 EVperfect = 200(0.4) + 800(0.6) = 560
High demand 800 EVimperfect = 160(0.4) + 800(0.6) = 544
Value of perfect information = $560,000 - $544,000
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57. Perfect Information Possible Future Demand Alternative Low High
Small facility
Large facility
Do nothing 0 0
Possible
Future Demand
Psmall = 0.4
Plarge = 0.6
Example A.8
Event Best Payoff
Low demand 200 EVperfect = 200(0.4) + 800(0.6) = 560
High demand 800 EVimperfect = 160(0.4) + 800(0.6) = 544
Value of perfect information = $16,000
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58. Decision Trees
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59. Decision Trees 1 2 Figure A.5 = Event node = Decision node
Ei = Event i
P(Ei) = Probability of event i
1st
decision
Possible
2nd decision
Payoff 1
Payoff 2
Payoff 3
Alternative 3
Alternative 4
Alternative 5
E1 [P(E1)]
E2 [P(E2)]
E3 [P(E3)]
Alternative 1
Alternative 2 1 2
Figure A.5
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60. Decision Trees
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61. Decision Trees 1 Example A.9 Small facility Large facility
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62. Decision Trees 2 1 Example A.9 Low demand [0.4] $200 $223 Don’t expand
Small facility
Large facility
Low demand [0.4]
Don’t expand
Expand
$200
$223
$270
High demand [0.6] 1 2
Example A.9
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63. Decision Trees 2 1 3 Example A.9 Low demand [0.4] $200 $223
Small facility
Large facility
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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64. Decision Trees 2 1 3 Example A.9 Low demand [0.4] $200 $223
Small facility
Large facility
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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65. Decision Trees 0.3(20) + 0.7(220) 2 1 3 Example A.9 Low demand [0.4]
Small facility
Large facility
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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66. Decision Trees 0.3(20) + 0.7(220) 2 1 3 $160 Example A.9
Low demand
[0.4]
Small facility
Large facility
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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67. Decision Trees 2 1 3 $160 Example A.9 Low demand [0.4] $200 $223
Small facility
Large facility
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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68. Decision Trees 2 $270 1 3 $160 ($160) Example A.9 Low demand [0.4]
Small facility
Large facility
Low demand [0.4]
Don’t expand
Expand
Do nothing
Advertise
$200
$223
$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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69. Decision Trees 0.4(200) + 0.6(270) 0.4(160) + 0.6(800) 2 $270 1 3 $160
($160)
Low demand
[0.4]
$270
$160
Small facility
Large facility
Low demand [0.4]
Don’t expand
Expand
Do nothing
Advertise
$200
$223
$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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70. Decision Trees 0.4(200) + 0.6(270) 0.4(160) + 0.6(800) $242 2 $270 1 3
($160)
Low demand
[0.4]
$270
$160
Small facility
Large facility
$242
$544
Low demand [0.4]
Don’t expand
Expand
Do nothing
Advertise
$200
$223
$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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71. Decision Trees $242 2 $270 1 $544 3 $160 Example A.9 Low demand [0.4]
Small facility
Large facility
$544
$242
Low demand [0.4]
Don’t expand
Expand
Do nothing
Advertise
$200
$223
$40
$800
Modest response [0.3]
Sizable response [0.7]
$20
$220
High demand [0.6] 1 2 3
Example A.9
To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved.

72. Decision Trees $242 2 $270 1 $544 3 $160 Figure A.6 Low demand [0.4]
Small facility
Large facility
$544
$242
Low demand [0.4]
Don’t expand
Expand
Do nothing
Advertise
$200
$223
$40
$800
Modest response [0.3]
Sizable response [0.7]
$20
$220
High demand [0.6] 1 2 3
Figure A.6
To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved.

73. Solved Problem 1 Break-even quantity Total revenues $77.7 Total costs
250 –
200 –
150 –
100 –
50 –
0 –
Figure A.7
Total revenues
Total costs
Units (in thousands)
Dollars (in thousands)
| | | | | | | |
Break-even
quantity
3.1
$77.7
To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved.

74. Solved Problem 4 Bad times [0.3] $191 Normal times [0.5] $240 One lift
Figure A.8
Bad times [0.3]
Normal times [0.5]
Good times [0.2]
One lift
Two lifts
$256.0
$225.3
$191
$240
$151
$245
$441
To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved.