978-0134237473 Module QUANTITATIVE MODULE

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Quantitative Module – Quantitative Decision-Making Aids

QUANTITATIVE

MODULE

QUANTITATIVE

DECISION-MAKING

AIDS

In the study of this module, the authors introduce payoff matrices, decision trees,

break-even analysis, ratio analysis, linear programming, queuing theory, and economic

order quantity. The purpose of each method is to provide managers with a tool to assist

in the decision making process and to provide more complete information to make

better-informed decisions.

Payoff Matrices

1. Uncertainty is a situation in which the decision maker is not certain and cannot

even make reasonable probability estimates concerning the outcomes of

alternatives.

2. The choice of alternative is influenced by the limited amount of information

available to the decision maker.

3. It’s also influenced by the psychological orientation of the decision maker.

4. Example of Visa International

a. Four possible strategies and the resulting profit. (See Exhibit QM-1.)

b. An optimistic manager will follow a maximax choice, maximizing the

maximum possible payoff.

c. A pessimistic manager will pursue a maximin choice, maximizing the

minimum possible payoff.

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Quantitative Module – Quantitative Decision-Making Aids

d. The manager who desires to minimize the maximum “regret” will opt for a

minimax choice. (See Exhibit QM-2.)

Decision Trees

1. Decision trees are a useful way to analyze hiring, marketing, investment,

equipment purchases, pricing, and similar decisions that involve a progression of

decisions.

2. They’re called decision trees because, when diagrammed, they look a lot like a

tree with branches.

3. Example of Harrington, a site selection supervisor of a bookstore (See Exhibit

QM-3).

a. The lease on the company’s store in Winter Park, Florida is expiring, and

the property owner has decided not to renew it.

Following the approach used for Decision Point 1, she could calculate the

profit potential by extending the branches on the tree and calculating

expected values for the various options.

Break-even Analysis

1. Break-even analysis is a technique for identifying the point at which total

revenue is just sufficient to cover total costs. (See Exhibit QM-4.)

2. To compute the break-even point (BE), the manager needs to know the unit price

of the product being sold (P), the variable cost per unit (VC), and the total fixed

costs (TFC).

3. An organization breaks even when its total revenue is just enough to equal its

total costs.

4. Total cost has two parts: a fixed component and a variable component.

5. The break-even point can be computed graphically or by using the following

formula: BE = [TFC/(P-VC)]

6. As a planning tool, break-even analysis could help management set sales

objectives.

7. Break-even analysis can also tell management how much volume has to increase

in order to break-even if the company is currently operating at a loss, or how

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Quantitative Module – Quantitative Decision-Making Aids

much volume they can afford to lose and still break-even if the company is

currently operating profitably.

Ratio Analysis

1. Ratio analysis involves the use of significant figures from the financial

statements and expresses them as a percentage or ratio.

2. This practice allows managers to compare current financial performance with

that of previous periods and other organizations in the same industry.

3. Some of the more useful ratios evaluate liquidity, leverage, operations, and

profitability.

4. These ratios are summarized in Exhibit QM-5.

a. Liquidity is a measure of the organization’s ability to convert assets into cash

in order to meet its debt obligations.

b. Leverage ratios refer to the use of borrowed funds to operate and expand an

organization.

c. Operating ratios describe how efficiently management is using the

organization’s resources.

d. Profitability ratios measure an organizations effectiveness and efficiency.

Linear Programming

1. Linear programming is a mathematical technique that solves resource

allocation problems.

2. Linear programming cannot be applied to all resource allocation situations.

3. Besides requiring limited resources and the objective of optimization, it requires

that there be alternative ways of combining resources to produce a number of

output mixes.

4. A linear relationship between variables is also necessary, which means that a

change in one variable will be accompanied by an exactly proportional change in

the other.

5. An example is given in Exhibit QM-6.

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Quantitative Module – Quantitative Decision-Making Aids

6. Many different types of problems can be solved with linear programming. For

example:

a. Selecting transportation routes that minimize shipping costs.

b. Allocating a limited advertising budget among various product brands.

c. Making the optimum assignment of personnel among projects.

d. Determining how much of each product to make with a limited number of

resources.

Queuing Theory

1. Queuing theory helps solve problems that involve balancing the cost of having

a waiting line against the cost of service to maintain that line.

2. These types of common situations include determining how many gas pumps are

needed at gas stations, tellers at bank windows, toll takers at toll booths, or

check-in lines at airline ticket counters.

3. Management wants to have as few stations open to minimize costs without

testing the patience of its customers.

Economic Order Quantity Model

1. A fixed-point reordering system is a method for a system to “flag” the need to

reorder inventory at some pre-established point in the process.

2. The objective is to minimize inventory carrying costs while at the same time

limiting the probability of stocking out of the inventory item.

3. The Economic Order Quantity Model seeks to balance the costs involved in

ordering and carrying inventory, thus minimizing total costs associated with

carrying and ordering costs. (See Exhibit QM-8.)

4. The EOQ model assumes that demand and lead times are known and constant. If

these conditions can’t be met, the model shouldn’t be used.

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