A – 1 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall. Decision Making A For Operations Management, 9e by Krajewski/Ritzman/Malhotra

A – 1

Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.

Decision MakingDecision MakingA

For For Operations Management, 9eOperations Management, 9e by by Krajewski/Ritzman/Malhotra Krajewski/Ritzman/Malhotra © 2010 Pearson Education© 2010 Pearson Education

PowerPoint Slides PowerPoint Slides by Jeff Heylby Jeff Heyl

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Break-Even AnalysisBreak-Even Analysis

Evaluating Services or Products Is the predicted sales volume of the service or

product sufficient to break even (neither earning a profit nor sustaining a loss)?

How low must the variable cost per unit be to break even, based on current prices and sales forecasts?

How low must the fixed cost be to break even? How do price levels affect the break-even

quantity?

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Break-even analysis is based on the assumption that all costs related to the production of a specific service or product can be divided into two categories: variable costs and fixed costs

Variable cost, c, is the portion of the total cost that varies directly with volume of output

If Q = the number of customers served or units produced per year, total variable cost = cQ

Fixed cost, F, is the portion of the total cost that remains constant regardless of changes in levels of output

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By setting revenue equal to total cost

pQ = F + cQ

Q =F

p - c

So

Total cost = F + cQ

Total revenue = pQ

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Finding the Break-Even QuantityFinding the Break-Even Quantity

EXAMPLE A.1

A hospital is considering a new procedure to be offered at $200 per patient. The fixed cost per year would be $100,000, with total variable costs of $100 per patient. What is the break-even quantity for this service? Use both algebraic and graphic approaches to get the answer.

SOLUTION

The formula for the break-even quantity yields

Q =F

p - c = 1,000 patients=100,000

200 – 100

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To solve graphically we plot two lines: one for costs and one for revenues

Begin by calculating costs and revenues for two different output levels

The following table shows the results for Q = 0 and Q = 2,000

Quantity (patients) (Q)

Total Annual Cost ($) (100,000 + 100Q)

Total Annual Revenue ($) (200Q)

0 100,000 0

2,000 300,000 400,000

Draw the cost line through points (0, 100,000) and (2,000, 300,000)

Draw the revenue line through (0, 0) and (2,000, 400,000)

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Total annual costs

Fixed costs

Break-even quantity

Profits

Loss

Patients (Q)

Do

llar

s (

in t

ho

usa

nd

s)

400 –

300 –

200 –

100 –

0 –| | | |

500 1000 1500 2000

(2000, 300)

Total annual revenues

The two lines intersect at 1,000 patients, the break-even quantity

FIGURE A.1 – Graphic Approach to Break-Even Analysis

(2000, 400)

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Application A.1Application A.1

The Denver Zoo must decide whether to move twin polar bears to Sea World or build a special exhibit for them and the zoo. The expected increase in attendance is 200,000 patrons. The data are:

Revenues per Patron for ExhibitGate receipts $4Concessions $5Licensed apparel $15

Estimated Fixed CostsExhibit construction $2,400,000Salaries $220,000Food $30,000

Estimated Variable Costs per PersonConcessions $2Licensed apparel $9

Is the predicted increase in attendance sufficient to break even?

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Q TR = pQ TC = F + cQ

0 $0 $2,650,000

250,000 $6,000,000 $5,400,000

7 –

6 –

5 –

4 –

3 –

2 –

1 –

0 –| | | | | |

50 100 150 200 250

Co

st a

nd

rev

enu

e (m

illi

on

s o

f d

oll

ars)

Q (thousands of patrons)

Total Cost

Total Revenue

Wherep = 4 + 5 + 15 = $24F= 2,400,000 + 220,000 + 30,000

= $2,650,000c = 2 + 9 = $11

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Q TR = pQ TC = F + cQ

0 $0 $2,650,000

250,000 $6,000,000 $5,400,000

Wherep = 4 + 5 + 15 = $24F= 2,400,000 + 220,000 + 30,000

= $2,650,000c = 2 + 9 = $11

Algebraic solution of Denver Zoo problempQ = F + cQ

24Q = 2,650,000 + 11Q

13Q = 2,650,000Q = 203,846

A – 11


Sensitivity Analysis

EXAMPLE A.2

If the most pessimistic sales forecast for the proposed service in Figure A.1 were 1,500 patients, what would be the procedure’s total contribution to profit and overhead per year?

SOLUTION

The graph shows that even the pessimistic forecast lies above the break-even volume, which is encouraging. The product’s total contribution, found by subtracting total costs from total revenues, is

200(1,500) – [100,000 + 100(1,500)]

pQ – (F + cQ) =

= $50,000

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Evaluating ProcessesEvaluating Processes

Choices must be made between two processes or between an internal process and buying services or materials on the outside

We assume that the decision does not affect revenues

The analyst finds the quantity for which the total costs for two alternatives are equal

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Evaluating ProcessesEvaluating Processes

Let Fb equal the fixed cost (per year) of the buy option, Fm equal the fixed cost of the make option, cb equal the variable cost (per unit) of the buy option, and cm equal the variable cost of the make option

The total cost to buy is Fb + cbQ and the total cost to make is Fm + cmQ

To find the break-even quantity, we set the two cost functions equal and solve for Q:

Fb + cbQ = Fm + cmQ

Q =Fm – Fb

cb – cm

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Break-Even Analysis for Break-Even Analysis for Make-or-Buy DecisionsMake-or-Buy Decisions

A fast-food restaurant featuring hamburgers is adding salads to the menu

The price to the customer will be the same

Fixed costs are estimated at $12,000 and variable costs totaling $1.50 per salad

Preassembled salads could be purchased from a local supplier at $2.00 per salad

Preassembled salads would require additional refrigeration with an annual fixed cost of $2,400

Expected demand is 25,000 salads per year

What is the break-even quantity?

EXAMPLE A.3

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SOLUTION

The formula for the break-even quantity yields the following:

Q =Fm – Fb

cb – cm

= 19,200 salads=12,000 – 2,400

2.0 – 1.5

Figure A.2 shows the solution from OM Explorer’s Break-Even Analysis Solver. The break-even quantity is 19,200 salads. As the 25,000-salad sales forecast exceeds this amount, the make option is preferred. Only if the restaurant expected to sell fewer than 19,200 salads would the buy option be better.

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FIGURE A.2 – Break-Even Analysis Solver of OM Explorer for Example A.3

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Fm – Fb cb –

cm

Q = = $150,000

At what volume should the Denver Zoo be indifferent between buying special sweatshirts from a supplier or have zoo employees make them?

=$300,000 – $0

$9 – $7

Buy Make

Fixed costs $0 $300,000

Variable costs $9 $7

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Preference MatrixPreference Matrix

A Preference Matrix is a table that allows you to rate an alternative according to several performance criteria

The criteria can be scored on any scale as long as the same scale is applied to all the alternatives being compared

Each score is weighted according to its perceived importance, with the total weights typically equaling 100

The total score is the sum of the weighted scores (weight × score) for all the criteria and compared against scores for alternatives

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EXAMPLE A.4

The following table shows the performance criteria, weights, and scores (1 = worst, 10 = best) for a new thermal storage air conditioner. If management wants to introduce just one new product and the highest total score of any of the other product ideas is 800, should the firm pursue making the air conditioner?

Evaluating an AlternativeEvaluating an Alternative

Performance Criterion Weight (A) Score (B) Weighted Score (A B)

Market potential 30 8 240

Unit profit margin 20 10 200

Operations compatibility 20 6 120

Competitive advantage 15 10 150

Investment requirements 10 2 20

Project risk 5 4 20

Weighted score = 750

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SOLUTION

Because the sum of the weighted scores is 750, it falls short of the score of 800 for another product. This result is confirmed by the output from OM Explorer’s Preference Matrix Solver in Figure A.3.

Evaluating an AlternativeEvaluating an Alternative

FIGURE A.3 – Preference Matrix Solver for Example A.4

Total 750

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Performance Criterion Weight Score Weighted Score

Market potential 10 5

Unit profit margin 30 8

Operations compatibility 20 10

Competitive advantage 25 7

Investment requirements 10 3

Project risk 5 4

Total weighted score =

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Repeat this process for each alternative – pick the one with the largest weighted score

50

200

240

175

30

20

715

Performance Criterion Weight Score Weighted Score

Market potential 10 5

Unit profit margin 30 8

Operations compatibility 20 10

Competitive advantage 25 7

Investment requirements 10 3

Project risk 5 4

Total weighted score =

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CriticismsCriticisms

Requires the manager to state criterion weights before examining alternatives, but they may not know in advance what is important and what is not

Allows one very low score to be overridden by high scores on other factors

May cause managers to ignore important signals

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Decision theory is a general approach to decision making when the outcomes associated with alternatives are in doubt

A manager makes choices using the following process:

Decision TheoryDecision Theory

1. List a reasonable number of feasible alternatives

2. List the events (states of nature)

3. Calculate the payoff table showing the payoff for each alternative in each event

4. Estimate the probability of occurrence for each event

5. Select the decision rule to evaluate the alternatives

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Decision Making Under CertaintyDecision Making Under Certainty

The simplest solution is when the manager knows which event will occur

Here the decision rule is to pick the alternative with the best payoff for the known event

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Decisions Under CertaintyDecisions Under Certainty

EXAMPLE A.5

A manager is deciding whether to build a small or a large facility

Much depends on the future demand

Demand may be small or large

Payoffs for each alternative are known with certainty

What is the best choice if future demand will be low?

Possible Future Demand

Alternative Low High

Small facility 200 270

Large facility 160 800

Do nothing 0 0

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Decisions Under CertaintyDecisions Under Certainty

SOLUTION

The best choice is the one with the highest payoff

For low future demand, the company should build a small facility and enjoy a payoff of $200,000

Under these conditions, the larger facility has a payoff of only $160,000


Alternative Low High



Do nothing 0 0

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Can list the possible events but can not estimate the probabilities

Decision Making Under UncertaintyDecision Making Under Uncertainty

1. Maximin: The best of the worst, a pessimistic approach

2. Maximax: The best of the best, an optimistic approach

3. Laplace: The alternative with the best weighted payoff assuming equal probabilities

4. Minimax Regret: Minimizing your regret (also pessimistic)

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Decisions Under UncertaintyDecisions Under Uncertainty

EXAMPLE A.6

Reconsider the payoff matrix in Example A.5. What is the best alternative for each decision rule?

SOLUTION

a. Maximin. An alternative’s worst payoff is the lowest number in its row of the payoff matrix, because the payoffs are profits. The worst payoffs ($000) are

Alternative Worst Payoff

Small facility 200

Large facility 160

The best of these worst numbers is $200,000, so the pessimist would build a small facility

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b. Maximax. An alternative’s best payoff ($000) is the highest number in its row of the payoff matrix, or

Alternative Best Payoff

Small facility 270

Large facility 800

The best of these best numbers is $800,000, so the optimist would build a large facility

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c. Laplace. With two events, we assign each a probability of 0.5. Thus, the weighted payoffs ($000) are

The best of these weighted payoffs is $480,000, so the realist would build a large facility

0.5(200) + 0.5(270) = 235

0.5(160) + 0.5(800) = 480

Alternative Weighted Payoff

Small facility

Large facility

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d. Minimax Regret. If demand turns out to be low, the best alternative is a small facility and its regret is 0 (or 200 – 200). If a large facility is built when demand turns out to be low, the regret is 40 (or 200 – 160).

Regret

Alternative Low Demand High Demand Maximum Regret

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

Large facility 200 – 160 = 40 800 – 800 = 0 40

The column on the right shows the worst regret for each alternative. To minimize the maximum regret, pick a large facility. The biggest regret is associated with having only a small facility and high demand.

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Fletcher (a realist), Cooper (a pessimist), and Wainwright (an optimist) are joint owners in a company. They must decide whether to make Arrows, Barrels, or Wagons. The government is about to issue a policy and recommendation on pioneer travel that depends on whether certain treaties are obtained. The policy is expected to affect demand for the products; however it is impossible at this time to assess the probability of these policy “events.” The following data are available:

Payoffs (Profits)

AlternativeLand Routes

No treatyLand Routes

TreatySea Routes

Only

Arrows $840,000 $440,000 $190,000

Barrels $370,000 $220,000 $670,000

Wagons $25,000 $1,150,000 ($25,000)

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Fletcher (a realist), Cooper (a pessimist), and Wainwright (an optimist) are joint owners in a company. They must decide whether to make Arrows, Barrels, or Wagons. The government is about to issue a policy and recommendation on pioneer travel that depends on whether certain treaties are obtained. The policy is expected to affect demand for the products; however it is impossible at this time to assess the probability of these policy “events.” The following data are available:

Payoffs (Profits)



TreatySea Routes

Only

Arrows $840,000 $440,000 $190,000

Barrels $370,000 $220,000 $670,000

Wagons $25,000 $1,150,000 ($25,000)

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Which product would be favored by Fletcher (realist)?

Which product would be favored by Cooper (pessimist)?

Which product would be favored by Wainwright (optimist)?

What is the minimax regret solution?

The short answers are:

Payoffs (Profits)



TreatySea Routes

Only

Arrows $840,000 $440,000 $190,000

Barrels $370,000 $220,000 $670,000

Wagons $25,000 $1,150,000 ($25,000)

Fletcher (realist – Laplace) would choose arrows

Cooper (pessimist – Maximin) would choose barrels

Wainwright (optimist – Maximax) would choose wagons

The Minimax Regret solution is arrows

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Decisions Under RiskDecisions Under Risk

The manager can list the possible events and estimate their probabilities

The manager has less information than decision making under certainty, but more information than with decision making under uncertainty

The expected value rule is widely used

This rule is similar to the Laplace decision rule, except that the events are no longer assumed to be equally likely

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Decisions Under RiskDecisions Under Risk

EXAMPLE A.7

Reconsider the payoff matrix in Example A.5. For the expected value decision rule, which is the best alternative if the probability of small demand is estimated to be 0.4 and the probability of large demand is estimated to be 0.6?

SOLUTION

The expected value for each alternative is as follows:


Alternative Small Large



0.4(200) + 0.6(270) = 242

0.4(160) + 0.6(800) = 544

Alternative Expected Value

Small facility

Large facility

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For Fletcher, Cooper, and Wainwright, find the best decision using the expected value rule. The probabilities for the events are given below.

What alternative has the best expected results?

Payoffs (Profits)



TreatySea Routes

Only

Arrows $840,000 $440,000 $190,000

Barrels $370,000 $220,000 $670,000

Wagons $25,000 $1,150,000 ($25,000)

(0.50)(840,000) + (0.30)(440,000) +(0.20)

(190,000)= 590,000

(0.50)(370,000) + (0.30)(220,000) + (0.20)(670,000) = 385,000

(0.50)(25,000) + (0.30)(1,150,000) + (0.20)(-25,000) = 352,500

AlternativeLand routes,

No Treaty(0.50)

Land Routes, Treaty Only

(0.30)

Sea routes, Only (0.20)

Expected Value

Arrows

Barrels

Wagons

A – 39


Are schematic models of available alternatives and possible consequences

Are useful with probabilistic events and sequential decisions

Decision TreesDecision Trees

Square nodes represent decisions

Circular nodes represent events

Events leaving a chance node are collectively exhaustive

A – 40


Conditional payoffs for each possible alternative-event combination shown at the end of each combination

Draw the decision tree from left to right

Calculate expected payoff to solve the decision tree from right to left


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Payoff 1

Payoff 2

Payoff 3

Alternative 3

Alternative 4

Alternative 5

Payoff 1

Payoff 2

Payoff 3

E1 & Probability

E2 & Probability

E3 & Probability

Altern

ativ

e 1

Alternative 2

E2 & Probability

E3 & Probability

E 1 &

Pro

babili

ty

Payoff 1

Payoff 2

1stdecision

1

Possible2nd decision

2


= Event node

= Decision node

Ei = Event i

P(Ei) = Probability of event iFIGURE A.4 – A Decision Tree Model

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After drawing a decision tree, we solve it by working from right to left, calculating the expected payoff for each of its possible paths

1. For an event node, we multiply the payoff of each event branch by the event’s probability and add these products to get the event node’s expected payoff

2. For a decision node, we pick the alternative that has the best expected payoff

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Various software is available for drawing and/or analyzing decision trees, including PowerPoint can be used to draw decision

trees, but does not have the capability to analyze the decision tree

POM with Windows SmartDraw PrecisionTree decision analysis from Palisade

Corporation

TreePlan

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Analyzing a Decision TreeAnalyzing a Decision Tree

EXAMPLE A.8

A retailer will build a small or a large facility at a new location

Demand can be either small or large, with probabilities estimated to be 0.4 and 0.6, respectively

For a small facility and high demand, not expanding will have a payoff of $223,000 and a payoff of $270,000 with expansion

For a small facility and low demand the payoff is $200,000

For a large facility and low demand, doing nothing has a payoff of $40,000

The response to advertising may be either modest or sizable, with their probabilities estimated to be 0.3 and 0.7, respectively

For a modest response the payoff is $20,000 and $220,000 if the response is sizable

For a large facility and high demand the payoff is $800,000

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SOLUTION

The decision tree in Figure A.5 shows the event probability and the payoff for each of the seven alternative-event combinations. The first decision is whether to build a small or a large facility. Its node is shown first, to the left, because it is the decision the retailer must make now. The second decision node is reached only if a small facility is built and demand turns out to be high. Finally, the third decision point is reached only if the retailer builds a large facility and demand turns out to be low.

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$200

$223

$270

$40

$800

$20

$220

Don’t expand

Expand

Low demand [0.4]

High demand

[0.6]

2

FIGURE A.5 – Decision Tree for Retailer (in $000)

Low dem

and

[0.4]

High demand [0.6]

3

Do nothing

Advertise

Modest response [0.3]

Sizable response [0.7]

Smal

l fac

ility

Large facility

1

A – 47



$200

$223

$270

$40

$800

$20

$220

Don’t expand

Expand

Low demand [0.4]

High demand

[0.6]

2


Low dem

and

[0.4]

High demand [0.6]

3

Do nothing

Advertise



Smal

l fac

ility

Large facility

1 0.3 x $20 = $6

0.7 x $220 = $154

$6 + $154 = $160

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$200

$223

$270

$40

$800

$20

$220

Don’t expand

Expand

Low demand [0.4]

High demand

[0.6]

2


Low dem

and

[0.4]

High demand [0.6]

3

Do nothing

Advertise



Smal

l fac

ility

Large facility

1

$160$160

A – 49



$200

$223

$270

$40

$800

$20

$220

Don’t expand

Expand

Low demand [0.4]

High demand

[0.6]

2


Low dem

and

[0.4]

High demand [0.6]

3

Do nothing

Advertise



Smal

l fac

ility

Large facility

1

$160$160

$270

A – 50



$200

$223

$270

$40

$800

$20

$220

Don’t expand

Expand

Low demand [0.4]

High demand

[0.6]

2


Low dem

and

[0.4]

High demand [0.6]

3

Do nothing

Advertise



Smal

l fac

ility

Large facility

1

$160$160

$270

x 0.4 = $80

x 0.6 = $162

$80 + $162 = $242

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$200

$223

$270

$40

$800

$20

$220

Don’t expand

Expand

Low demand [0.4]

High demand

[0.6]

2


Low dem

and

[0.4]

High demand [0.6]

3

Do nothing

Advertise



Smal

l fac

ility

Large facility

1

$160$160

$270

$242

x 0.6 = $480

0.4 x $160 = $64

$544

A – 52



$200

$223

$270

$40

$800

$20

$220

Don’t expand

Expand

Low demand [0.4]

High demand

[0.6]

2


Low dem

and

[0.4]

High demand [0.6]

3

Do nothing

Advertise



Smal

l fac

ility

Large facility

1

$160$160

$270

$242

$544

$544

A – 53



$200

$223

$270

$40

$800

$20

$220

Don’t expand

Expand

Low demand [0.4]

High demand

[0.6]

2


Low dem

and

[0.4]

High demand [0.6]

3

Do nothing

Advertise



Smal

l fac

ility

Large facility

1

$160$160

$270

$242

$544

$544

A – 54



a. Draw the decision tree for the Fletcher, Cooper, and Wainwright Application A.5 problem

b. What is the expected payoff for the best alternative in the decision tree below?

AlternativeLand routes,

No Treaty(0.50)

Land Routes, Treaty Only

(0.30)

Sea routes, Only (0.20)

Arrows 840,000 440,000 190,000

Barrels 370,000 220,000 670,000

Wagons 25,000 1,150,000 -25,000

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A – 56


Solved Problem 1Solved Problem 1

A small manufacturing business has patented a new device for washing dishes and cleaning dirty kitchen sinks

The owner wants reasonable assurance of success

Variable costs are estimated at $7 per unit produced and sold

Fixed costs are about $56,000 per year

a. If the selling price is set at $25, how many units must be produced and sold to break even? Use both algebraic and graphic approaches.

b. Forecasted sales for the first year are 10,000 units if the price is reduced to $15. With this pricing strategy, what would be the product’s total contribution to profits in the first year?

A – 57



SOLUTION

a. Beginning with the algebraic approach, we get

Q =F

p – c

= 3,111 units

=56,00025 – 7

Using the graphic approach, shown in Figure A.6, we first draw two lines:

The two lines intersect at Q = 3,111 units, the break-even quantity

Total revenue =

Total cost =

25Q

56,000 + 7Q

A – 58



b. Total profit contribution = Total revenue – Total cost

= pQ – (F + cQ)

= 15(10,000) – [56,000 + 7(10,000)]

= $24,000

A – 59


Total costs

Break-evenquantity

250 –

200 –

150 –

100 –

50 –

0 –

Units (in thousands)

Do

llars

(in

th

ou

san

ds)

| | | | | | | |

1 2 3 4 5 6 7 8

Total revenues

3.1

$77.7


FIGURE A.6

A – 60



Binford Tool Company is screening three new product idea: A, B, and C. Resource constraints allow only one of them to be commercialized. The performance criteria and ratings, on a scale of 1 (worst) to 10 (best), are shown in the following table. The Binford managers give equal weights to the performance criteria. Which is the best alternative, as indicated by the preference matrix method?

Rating

Performance Criteria Product A Product B Product C

1. Demand uncertainty and project risk 3 9 2

2. Similarity to present products 7 8 6

3. Expected return on investment (ROI) 10 4 8

4. Compatibility with current manufacturing process

4 7 6

5. Competitive Strategy 4 6 5

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SOLUTION

Each of the five criteria receives a weight of 1/5 or 0.20

Rating

Performance Criteria Product A Product B Product C

1. Uncertainty and risk 3 9 2

2. Similarity 7 8 6

3. ROI 10 4 8

4. Compatibility 4 7 6

5. Strategy 4 6 5

The best choice is product B as Products A and C are well behind in terms of total weighted score

(0.20 × 3) + (0.20 × 7) + (0.20 × 10) + (0.20 × 4) + (0.20 × 4)

= 5.6

(0.20 × 9) + (0.20 × 8) + (0.20 × 4) + (0.20 × 7) + (0.20 × 6) = 6.8

(0.20 × 2) + (0.20 × 6) + (0.20 × 8) + (0.20 × 6) + (0.20 × 5) = 5.4

Product Calculation Total Score

A

B

C

A – 62



Adele Weiss manages the campus flower shop. Flowers must be ordered three days in advance from her supplier in Mexico. Although Valentine’s Day is fast approaching, sales are almost entirely last-minute, impulse purchases. Advance sales are so small that Weiss has no way to estimate the probability of low (25 dozen), medium (60 dozen), or high (130 dozen) demand for red roses on the big day. She buys roses for $15 per dozen and sells them for $40 per dozen. Construct a payoff table. Which decision is indicated by each of the following decision criteria?

a. Maximin

b. Maximax

c. Laplace

d. Minimax regret

A – 63



SOLUTION

The payoff table for this problem is

Demand for Red Roses

Alternative Low(25 dozen)

Medium(60 dozen)

High(130 dozen)

Order 25 dozen $625 $625 $625

Order 60 dozen $100 $1,500 $1,500

Order 130 dozen ($950) $450 $3,250

Do nothing $0 $0 $0

A – 64



a. Under the maximin criteria, Weiss should order 25 dozen, because if demand is low, Weiss’s profits are $625, the best of the worst payoffs.

b. Under the maximax criteria, Weiss should order 130 dozen. The greatest possible payoff, $3,250, is associated with the largest order.

c. Under the Laplace criteria, Weiss should order 60 dozen. Equally weighted payoffs for ordering 25, 60, and 130 dozen are about $625, $1,033, and $917, respectively.

d. Under the minimax regret criteria, Weiss should order 130 dozen. The maximum regret of ordering 25 dozen occurs if demand is high: $3,250 – $625 = $2,625. The maximum regret of ordering 60 dozen occurs if demand is high: $3,250 – $1,500 = $1,750. The maximum regret of ordering 130 dozen occurs if demand is low: $625 – (–$950) = $1,575.

A – 65



White Valley Ski Resort is planning the ski lift operation for its new ski resort and wants to determine if one or two lifts will be necessary

Each lift can accommodate 250 people per day and skiing occurs 7 days per week in the 14-week season and lift tickets cost $20 per customer per day

The table below shows all the costs and probabilities for each alternative and condition

Should the resort purchase one lift or two?

Alternatives Conditions Utilization Installation Operation

One lift Bad times (0.3) 0.9 $50,000 $200,000

Normal times (0.5) 1.0 $50,000 $200,000

Good times (0.2) 1.0 $50,000 $200,000

Two lifts Bad times (0.3) 0.9 $90,000 $200,000

Normal times (0.5) 1.0 $90,000 $400,000

Good times (0.2) 1.0 $90,000 $400,000

A – 66



SOLUTION

The decision tree is shown in Figure A.7. The payoff ($000) for each alternative-event branch is shown in the following table. The total revenues from one lift operating at 100 percent capacity are $490,000 (or 250 customers × 98 days × $20/customer-day).

0.9(490) – (50 + 200) = 191

1.0(490) – (50 + 200) = 240

1.0(490) – (50 + 200) = 240

0.9(490) – (90 + 200) = 151

1.5(490) – (90 + 400) = 245

1.9(490) – (90 + 400) = 441

Alternatives Economic Conditions Payoff Calculation (Revenue – Cost)

One lift Bad times

Normal times

Good times

Two lifts Bad times

Normal times

Good times

A – 67


Bad times [0.3]

Normal times [0.5]

Good times [0.2]

$191

$240

$240

Bad times [0.3]

Normal times [0.5]

Good times [0.2]

$151

$245

$441

One lift

Two lifts

$256.0

$225.3

$256.0


0.3(191) + 0.5(240) + 0.2(240) = 225.3

0.3(151) + 0.5(245) + 0.2(441) = 256.0

FIGURE A.7

A – 68


Documents

A – 1 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall. Decision Making A For Operations Management, 9e by Krajewski/Ritzman/Malhotra