SOLUTION MANUAL FOR
QUANTITATIVE ANALYSIS FOR MANAGEMENT, 14TH EDITION
RENDER
CHAPTER 1-15
CHAPTER 1
Introduction to Quantitative Analysis
TEACHING SUGGESTIONS
Teaching Suggestion 1.1: Importance of Qualitative Factors.
Section 1.1 gives students an overview of quantitative analysis. In this section, a number of
qualitative factors, including federal legislation and new technology, are discussed. Students can
be asked to discuss other qualitative factors that could have an impact on quantitative analysis.
Waiting lines and project planning can be used as examples.
Teaching Suggestion 1.2: Discussing Other Quantitative Analysis Problems.
Section 1.2 covers an application of the quantitative analysis approach. Students can be asked to
describe other problems or areas that could benefit from quantitative analysis.
Teaching Suggestion 1.3: Discussing Conflicting Viewpoints.
Possible problems in the QA approach are presented in this chapter. A discussion of conflicting
viewpoints within the organization can help students understand this problem. For example, how
many people should staff a registration desk at a university? Students will want more staff to
reduce waiting time, while university administrators will want less staff to save money. A
discussion of these types of conflicting viewpoints will help students understand some of the
problems of using quantitative analysis.
Teaching Suggestion 1.4: Difficulty of Getting Input Data.
A major problem in quantitative analysis is getting proper input data. Students can be asked to
explain how they would get the information they need to determine inventory ordering or
carrying costs. Role-playing with students assuming the parts of the analyst who needs inventory
costs and the instructor playing the part of a veteran inventory manager can be fun and
interesting. Students quickly learn that getting good data can be the most difficult part of using
quantitative analysis.
Teaching Suggestion 1.5: Dealing with Resistance to Change.
11-1
Copyright © 2024 Pearson Education, Inc.
,Resistance to change is discussed in this chapter. Students can be asked to explain how they
would introduce a new system or change within the organization. People resisting new
approaches can be a major stumbling block to the successful implementation of quantitative
analysis. Students can be asked why some people may be afraid of a new inventory control or
forecasting system.
SOLUTIONS TO DISCUSSION QUESTIONS AND PROBLEMS
1-1. Quantitative analysis involves the use of mathematical equations or relationships in
analyzing a particular problem. In most cases, the results of quantitative analysis will be one or
more numbers that can be used by managers and decision makers in making better decisions.
Calculating rates of return, financial ratios from a balance sheet and profit and loss statement,
determining the number of units that must be produced in order to break even, and many similar
techniques are examples of quantitative analysis. Qualitative analysis involves the investigation
of factors in a decision-making problem that cannot be quantified or stated in mathematical
terms. The state of the economy, current or pending legislation, perceptions about a potential
client, and similar situations reveal the use of qualitative analysis. In most decision-making
problems, both quantitative and qualitative analysis are used. In this book, however, we
emphasize the techniques and approaches of quantitative analysis.
1-2. Quantitative analysis is the scientific approach to managerial decision making. This type of
analysis is a logical and rational approach to making decisions. Emotions, guesswork, and whim
are not part of the quantitative analysis approach. A number of organizations support the use of
the scientific approach: the Institute for Operation Research and Management Science
(INFORMS), Decision Sciences Institute, and Academy of Management.
1-3. The three categories of business analytics are descriptive, predictive, and prescriptive.
Descriptive analytics provides an indication of how things were performed in the past. Predictive
analytics uses past data to forecast what will happen in the future. Prescriptive analytics uses
optimization and other models to present better ways for a company to operate to reach goals and
objectives.
1-4. Quantitative analysis is a step-by-step process that allows decision makers to investigate
problems using quantitative techniques. The steps of the quantitative analysis process include
defining the problem, developing a model, acquiring input data, developing a solution, testing
the solution, analyzing the results, and implementing the results. In every case, the analysis
begins with defining the problem. The problem could be too many stockouts, too many bad
debts, or determining the products to produce that will result in the maximum profit for the
organization. After the problems have been defined, the next step is to develop one or more
models. These models could be inventory control models, models that describe the debt situation
in the organization, and so on. Once the models have been developed, the next step is to acquire
input data. In the inventory problem, for example, such factors as the annual demand, the
ordering cost, and the carrying cost would be input data that are used by the model developed in
the preceding step. In determining the products to produce in order to maximize profits, the input
data could be such things as the profitability for all the different products, the amount of time
that is available at the various production departments that produce the products, and the amount
of time it takes for each product to be produced in each production department. The next step is
developing the solution. This requires manipulation of the model in order to determine the best
11-2
Copyright © 2024 Pearson Education, Inc
nm nm nm nm nm
.
,solution. Next, the results are tested, analyzed, and implemented. In the inventory control prob
nm nm nm nm nm nm nm nm nm nm nm nm nm
lem, this might result in determining and implementing a policy to order a certain amount of i
nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm
nventory at specified intervals. For the problem of determining the best products to produce, thi
nm nm nm nm nm nm nm nm nm nm nm nm nm nm
s might mean testing, analyzing, and implementing a decision to produce a certain quantity of
nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm
given products. nm
1-
5. Although the formal study of quantitative analysis and the refinement of the tools and techn
nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm
iques of the scientific method have occurred only in the recent past, quantitative approaches to
nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm
decision making have been in existence since the beginning of time. In the early 1900s, Freder
nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm
ick W. Taylor developed the principles of the scientific approach. During World War II, quanti
nm nm nm nm nm nm nm nm nm nm nm nm nm nm
tative analysis was intensified and used by the military. Because of the success of these techni
nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm
ques during World War II, interest continued after the war.
nm nm nm nm nm nm nm nm nm
1-
6. Model types include the scale model, physical model, and schematic model (which is a pict
nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm
ure or drawing of reality). In this book, mathematical models are used to describe mathematica
nm nm nm nm nm nm nm nm nm nm nm nm nm nm
l relationships in solving quantitative problems.
nm nm nm nm nm
In this question, the student is asked to develop two mathematical models. The student mig
nm nm nm nm nm nm nm nm nm nm nm nm nm nm
ht develop a number of models that relate to finance, marketing, accounting, statistics, or other
nm nm nm nm nm nm nm nm nm nm nm nm nm nm n
fields. The purpose of this part of the question is to have the student develop a mathematical r
m nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm
elationship between variables with which the student is familiar.
nm nm nm nm nm nm nm nm
1-
7. Input data can come from company reports and documents, interviews with employees and
n m nm nm nm nm nm nm nm nm nm nm nm nm nm
other personnel, direct measurement, and sampling procedures. For many problems, a number
nm nm nm nm nm nm nm nm nm nm nm nm
of different sources are required to obtain data, and in some cases it is necessary to obtain the
nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm
same data from different sources in order to check the accuracy and consistency of the input d
nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm
ata. If the input data are not accurate, the results can be misleading and very costly to the org
nm n m nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm
anization. This concept is called ―garbage in, garbage out.‖
nm nm nm nm nm nm nm nm
1-
8. Implementation is the process of taking the solution and incorporating it into the company
n m nm nm nm nm nm nm nm nm nm nm nm nm nm n
m or organization. This is the final step in the quantitative analysis approach, and if a good job i
nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm
s not done with implementation, all of the effort expended on the previous steps can be wasted
nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm
.
1-
9. Sensitivity analysis and post optimality analysis allow the decision maker to determine how
n m nm nm nm nm nm nm nm nm nm nm nm nm
the final solution to the problem will change when the input data or the model change. This t
nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm
ype of analysis is very important when the input data or model has not been specified properly
nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm
. A sensitive solution is one in which the results of the solution to the problem will change dr
nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm n m
astically or by a large amount with small changes in the data or in the model. When the model
nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm n
mis not sensitive, the results or solutions to the model will not change significantly with changes
nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm
in the input data or in the model. Models that are very sensitive require that the input data an
nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm
d the model itself be thoroughly tested to make sure that both are very accurate and consistent
nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm
with the problem statement.
nm nm nm
1-
10. There are a large number of quantitative terms that may not be understood by managers.
n m nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm
11-3
Copyright © 2024 Pearson Education, Inc nm nm nm nm nm
.
, Examples include PERT, CPM, simulation, the Monte Carlo method, mathematical programmi
nm nm nm nm nm nm nm nm nm nm
ng, EOQ, and so on. The student should explain each of the four terms selected in his or her o
nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm
wn words.
nm
1-
11. Many quantitative analysts enjoy building mathematical models and solving them to find t
n m nm nm nm nm nm nm nm nm nm nm nm nm
he optimal solution to a problem. Others enjoy dealing with other technical aspects, for exam
nm nm nm nm nm nm nm nm nm nm nm nm nm n m
ple, data analysis and collection, computer programming, or computations.
n m n m n m n m n m n m n m n m n m n m n m n m n m n m n m n m n m n
mThe
11-4
Copyright © 2024 Pearson Education, Inc
nm nm nm nm nm
.
QUANTITATIVE ANALYSIS FOR MANAGEMENT, 14TH EDITION
RENDER
CHAPTER 1-15
CHAPTER 1
Introduction to Quantitative Analysis
TEACHING SUGGESTIONS
Teaching Suggestion 1.1: Importance of Qualitative Factors.
Section 1.1 gives students an overview of quantitative analysis. In this section, a number of
qualitative factors, including federal legislation and new technology, are discussed. Students can
be asked to discuss other qualitative factors that could have an impact on quantitative analysis.
Waiting lines and project planning can be used as examples.
Teaching Suggestion 1.2: Discussing Other Quantitative Analysis Problems.
Section 1.2 covers an application of the quantitative analysis approach. Students can be asked to
describe other problems or areas that could benefit from quantitative analysis.
Teaching Suggestion 1.3: Discussing Conflicting Viewpoints.
Possible problems in the QA approach are presented in this chapter. A discussion of conflicting
viewpoints within the organization can help students understand this problem. For example, how
many people should staff a registration desk at a university? Students will want more staff to
reduce waiting time, while university administrators will want less staff to save money. A
discussion of these types of conflicting viewpoints will help students understand some of the
problems of using quantitative analysis.
Teaching Suggestion 1.4: Difficulty of Getting Input Data.
A major problem in quantitative analysis is getting proper input data. Students can be asked to
explain how they would get the information they need to determine inventory ordering or
carrying costs. Role-playing with students assuming the parts of the analyst who needs inventory
costs and the instructor playing the part of a veteran inventory manager can be fun and
interesting. Students quickly learn that getting good data can be the most difficult part of using
quantitative analysis.
Teaching Suggestion 1.5: Dealing with Resistance to Change.
11-1
Copyright © 2024 Pearson Education, Inc.
,Resistance to change is discussed in this chapter. Students can be asked to explain how they
would introduce a new system or change within the organization. People resisting new
approaches can be a major stumbling block to the successful implementation of quantitative
analysis. Students can be asked why some people may be afraid of a new inventory control or
forecasting system.
SOLUTIONS TO DISCUSSION QUESTIONS AND PROBLEMS
1-1. Quantitative analysis involves the use of mathematical equations or relationships in
analyzing a particular problem. In most cases, the results of quantitative analysis will be one or
more numbers that can be used by managers and decision makers in making better decisions.
Calculating rates of return, financial ratios from a balance sheet and profit and loss statement,
determining the number of units that must be produced in order to break even, and many similar
techniques are examples of quantitative analysis. Qualitative analysis involves the investigation
of factors in a decision-making problem that cannot be quantified or stated in mathematical
terms. The state of the economy, current or pending legislation, perceptions about a potential
client, and similar situations reveal the use of qualitative analysis. In most decision-making
problems, both quantitative and qualitative analysis are used. In this book, however, we
emphasize the techniques and approaches of quantitative analysis.
1-2. Quantitative analysis is the scientific approach to managerial decision making. This type of
analysis is a logical and rational approach to making decisions. Emotions, guesswork, and whim
are not part of the quantitative analysis approach. A number of organizations support the use of
the scientific approach: the Institute for Operation Research and Management Science
(INFORMS), Decision Sciences Institute, and Academy of Management.
1-3. The three categories of business analytics are descriptive, predictive, and prescriptive.
Descriptive analytics provides an indication of how things were performed in the past. Predictive
analytics uses past data to forecast what will happen in the future. Prescriptive analytics uses
optimization and other models to present better ways for a company to operate to reach goals and
objectives.
1-4. Quantitative analysis is a step-by-step process that allows decision makers to investigate
problems using quantitative techniques. The steps of the quantitative analysis process include
defining the problem, developing a model, acquiring input data, developing a solution, testing
the solution, analyzing the results, and implementing the results. In every case, the analysis
begins with defining the problem. The problem could be too many stockouts, too many bad
debts, or determining the products to produce that will result in the maximum profit for the
organization. After the problems have been defined, the next step is to develop one or more
models. These models could be inventory control models, models that describe the debt situation
in the organization, and so on. Once the models have been developed, the next step is to acquire
input data. In the inventory problem, for example, such factors as the annual demand, the
ordering cost, and the carrying cost would be input data that are used by the model developed in
the preceding step. In determining the products to produce in order to maximize profits, the input
data could be such things as the profitability for all the different products, the amount of time
that is available at the various production departments that produce the products, and the amount
of time it takes for each product to be produced in each production department. The next step is
developing the solution. This requires manipulation of the model in order to determine the best
11-2
Copyright © 2024 Pearson Education, Inc
nm nm nm nm nm
.
,solution. Next, the results are tested, analyzed, and implemented. In the inventory control prob
nm nm nm nm nm nm nm nm nm nm nm nm nm
lem, this might result in determining and implementing a policy to order a certain amount of i
nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm
nventory at specified intervals. For the problem of determining the best products to produce, thi
nm nm nm nm nm nm nm nm nm nm nm nm nm nm
s might mean testing, analyzing, and implementing a decision to produce a certain quantity of
nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm
given products. nm
1-
5. Although the formal study of quantitative analysis and the refinement of the tools and techn
nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm
iques of the scientific method have occurred only in the recent past, quantitative approaches to
nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm
decision making have been in existence since the beginning of time. In the early 1900s, Freder
nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm
ick W. Taylor developed the principles of the scientific approach. During World War II, quanti
nm nm nm nm nm nm nm nm nm nm nm nm nm nm
tative analysis was intensified and used by the military. Because of the success of these techni
nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm
ques during World War II, interest continued after the war.
nm nm nm nm nm nm nm nm nm
1-
6. Model types include the scale model, physical model, and schematic model (which is a pict
nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm
ure or drawing of reality). In this book, mathematical models are used to describe mathematica
nm nm nm nm nm nm nm nm nm nm nm nm nm nm
l relationships in solving quantitative problems.
nm nm nm nm nm
In this question, the student is asked to develop two mathematical models. The student mig
nm nm nm nm nm nm nm nm nm nm nm nm nm nm
ht develop a number of models that relate to finance, marketing, accounting, statistics, or other
nm nm nm nm nm nm nm nm nm nm nm nm nm nm n
fields. The purpose of this part of the question is to have the student develop a mathematical r
m nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm
elationship between variables with which the student is familiar.
nm nm nm nm nm nm nm nm
1-
7. Input data can come from company reports and documents, interviews with employees and
n m nm nm nm nm nm nm nm nm nm nm nm nm nm
other personnel, direct measurement, and sampling procedures. For many problems, a number
nm nm nm nm nm nm nm nm nm nm nm nm
of different sources are required to obtain data, and in some cases it is necessary to obtain the
nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm
same data from different sources in order to check the accuracy and consistency of the input d
nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm
ata. If the input data are not accurate, the results can be misleading and very costly to the org
nm n m nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm
anization. This concept is called ―garbage in, garbage out.‖
nm nm nm nm nm nm nm nm
1-
8. Implementation is the process of taking the solution and incorporating it into the company
n m nm nm nm nm nm nm nm nm nm nm nm nm nm n
m or organization. This is the final step in the quantitative analysis approach, and if a good job i
nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm
s not done with implementation, all of the effort expended on the previous steps can be wasted
nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm
.
1-
9. Sensitivity analysis and post optimality analysis allow the decision maker to determine how
n m nm nm nm nm nm nm nm nm nm nm nm nm
the final solution to the problem will change when the input data or the model change. This t
nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm
ype of analysis is very important when the input data or model has not been specified properly
nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm
. A sensitive solution is one in which the results of the solution to the problem will change dr
nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm n m
astically or by a large amount with small changes in the data or in the model. When the model
nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm n
mis not sensitive, the results or solutions to the model will not change significantly with changes
nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm
in the input data or in the model. Models that are very sensitive require that the input data an
nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm
d the model itself be thoroughly tested to make sure that both are very accurate and consistent
nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm
with the problem statement.
nm nm nm
1-
10. There are a large number of quantitative terms that may not be understood by managers.
n m nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm
11-3
Copyright © 2024 Pearson Education, Inc nm nm nm nm nm
.
, Examples include PERT, CPM, simulation, the Monte Carlo method, mathematical programmi
nm nm nm nm nm nm nm nm nm nm
ng, EOQ, and so on. The student should explain each of the four terms selected in his or her o
nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm nm
wn words.
nm
1-
11. Many quantitative analysts enjoy building mathematical models and solving them to find t
n m nm nm nm nm nm nm nm nm nm nm nm nm
he optimal solution to a problem. Others enjoy dealing with other technical aspects, for exam
nm nm nm nm nm nm nm nm nm nm nm nm nm n m
ple, data analysis and collection, computer programming, or computations.
n m n m n m n m n m n m n m n m n m n m n m n m n m n m n m n m n m n
mThe
11-4
Copyright © 2024 Pearson Education, Inc
nm nm nm nm nm
.
