11-1
Copyright © 2024 Pearson Education, Inc.
,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.
11-2
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
11-3
Copyright © 2024 Pearson Education, Inc.
, solution. Next, the results are tested, analyzed, and implemented. In the inventory control
problem, this might result in determining and implementing a policy to order a certain amount of
inventory at specified intervals. For the problem of determining the best products to produce, this
might mean testing, analyzing, and implementing a decision to produce a certain quantity of
given products.
1-5.
Althoughqtheqformalqstudyqofqquantitativeqanalysisqandqtheqrefinementqofqtheqtoolsqandqtechniquesqo
fqtheqscientificqmethodqhaveqoccurredqonlyqinqtheqrecentqpast,qquantitativeqapproachesqtoqdecisionqm
akingqhaveqbeenqinqexistenceqsinceqtheqbeginningqofqtime.qInqtheqearlyq1900s,qFrederickqW.qTaylorq
developedqtheqprinciplesqofqtheqscientificqapproach.qDuringqWorldqWarqII,qquantitativeqanalysisqwa
sqintensifiedqandqusedqbyqtheqmilitary.qBecauseqofqtheqsuccessqofqtheseqtechniquesqduringqWorldqWa
rqII,qinterestqcontinuedqafterqtheqwar.
1-
6.qModelqtypesqincludeqtheqscaleqmodel,qphysicalqmodel,qandqschematicqmodelq(whichq isqaqpictureqo
rqdrawingqofqreality).qInqthisqbook,qmathematicalq modelsqarequsedqtoqdescribeqmathematicalqrelation
shipsq inqsolvingqquantitativeqproblems.
Inqthisqquestion,qtheqstudentqisqaskedqtoqdevelopqtwoqmathematicalqmodels.qTheqstudentqmightqd
evelopqaqnumberqofqmodelsqthatqrelateqtoqfinance,qmarketing,qaccounting,q statistics,qorqotherqfields.q
Theqpurposeqofqthisqpartqofqtheqquestionqisqtoqhaveqtheqstudentqdevelopqaqmathematicalqrelationshipq
betweenqvariablesqwithqwhichqtheqstudentqisqfamiliar.
1-
7.q Inputqdataqcanqcomeqfromqcompanyqreportsqandqdocuments,qinterviewsqwithqemployeesqandqothe
rqpersonnel,qdirectqmeasurement,qandqsamplingqprocedures.qForqmanyqproblems,qaqnumberqofqdiffer
entqsourcesqareqrequiredqtoqobtainqdata,qandqinqsomeqcasesqitqisqnecessaryqtoqobtainqtheqsameqdataqfro
mqdifferentqsourcesqinqorderqtoqcheckqtheqaccuracyqandqconsistencyqofqtheqinputqdata.qIfq theqinputqda
taqareqnotqaccurate,qtheqresultsqcanqbeqmisleadingqandqveryqcostlyqtoqtheqorganization.q Thisqconceptqi
sqcalledq―garbageqin,q garbageqout.‖
1-
8.q Implementationqisqtheqprocessqofqtakingqtheqsolutionqandq incorporatingq itqintoqtheqcompanyq orqor
ganization.q Thisqisqtheqfinalqstepqinqtheqquantitativeqanalysisqapproach,qandqifqaqgoodq jobqisqnotqdone
qwithqimplementation, qallqofqthe qeffort qexpendedqonqthe qpreviousqstepsqcanqbe qwasted.
1-
9.q Sensitivityqanalysisqandqpostqoptimalityqanalysisqallowqtheqdecisionq makerqtoqdetermineqhowqtheq
finalqsolutionqtoqtheqproblemq willqchangeqwhenqtheqinputqdataqorqtheqmodelqchange.qThisqtypeqofqana
lysisq isqveryqimportantqwhenqtheqinputqdataqorqmodelqhasqnotqbeenqspecifiedqproperly.qAqsensitiveqso
lutionq isqoneqinq whichqtheqresultsqofqtheqsolutionqtoqtheqproblemqwillqchangeq drasticallyqorqbyqaqlarg
eqamountqwithqsmallqchangesqinqtheqdataqorqinqtheqmodel.qWhenqtheqmodelqisqnotqsensitive,qtheqresul
tsqorqsolutionsqtoqtheqmodelqwillqnotqchangeqsignificantlyqwithqchangesqinqtheqinputqdataqorqinqtheqmo
del.qModelsqthatqareqveryqsensitiveqrequireqthatqtheqinputqdataqandqtheqmodelqitselfqbeqthoroughlyqtes
tedqtoqmakeqsureqthatqbothqareqveryqaccurateqandqconsistentqwithqtheqproblemqstatement.
1-
10.q Thereqareqaqlargeqnumberqofqquantitativeqtermsqthatqmayqnotqbequnderstoodqbyqmanagers.qExam
plesqincludeqPERT,qCPM,qsimulation,qtheqMonteqCarloqmethod,qmathematicalqprogramming,qEOQ
,qandqsoqon.qTheqstudentqshouldqexplainqeachqofqtheqfourqtermsqselectedqinqhisqorqherqownqwords.
11-4
Copyright © 2024 Pearson Education, Inc.
Copyright © 2024 Pearson Education, Inc.
,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.
11-2
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
11-3
Copyright © 2024 Pearson Education, Inc.
, solution. Next, the results are tested, analyzed, and implemented. In the inventory control
problem, this might result in determining and implementing a policy to order a certain amount of
inventory at specified intervals. For the problem of determining the best products to produce, this
might mean testing, analyzing, and implementing a decision to produce a certain quantity of
given products.
1-5.
Althoughqtheqformalqstudyqofqquantitativeqanalysisqandqtheqrefinementqofqtheqtoolsqandqtechniquesqo
fqtheqscientificqmethodqhaveqoccurredqonlyqinqtheqrecentqpast,qquantitativeqapproachesqtoqdecisionqm
akingqhaveqbeenqinqexistenceqsinceqtheqbeginningqofqtime.qInqtheqearlyq1900s,qFrederickqW.qTaylorq
developedqtheqprinciplesqofqtheqscientificqapproach.qDuringqWorldqWarqII,qquantitativeqanalysisqwa
sqintensifiedqandqusedqbyqtheqmilitary.qBecauseqofqtheqsuccessqofqtheseqtechniquesqduringqWorldqWa
rqII,qinterestqcontinuedqafterqtheqwar.
1-
6.qModelqtypesqincludeqtheqscaleqmodel,qphysicalqmodel,qandqschematicqmodelq(whichq isqaqpictureqo
rqdrawingqofqreality).qInqthisqbook,qmathematicalq modelsqarequsedqtoqdescribeqmathematicalqrelation
shipsq inqsolvingqquantitativeqproblems.
Inqthisqquestion,qtheqstudentqisqaskedqtoqdevelopqtwoqmathematicalqmodels.qTheqstudentqmightqd
evelopqaqnumberqofqmodelsqthatqrelateqtoqfinance,qmarketing,qaccounting,q statistics,qorqotherqfields.q
Theqpurposeqofqthisqpartqofqtheqquestionqisqtoqhaveqtheqstudentqdevelopqaqmathematicalqrelationshipq
betweenqvariablesqwithqwhichqtheqstudentqisqfamiliar.
1-
7.q Inputqdataqcanqcomeqfromqcompanyqreportsqandqdocuments,qinterviewsqwithqemployeesqandqothe
rqpersonnel,qdirectqmeasurement,qandqsamplingqprocedures.qForqmanyqproblems,qaqnumberqofqdiffer
entqsourcesqareqrequiredqtoqobtainqdata,qandqinqsomeqcasesqitqisqnecessaryqtoqobtainqtheqsameqdataqfro
mqdifferentqsourcesqinqorderqtoqcheckqtheqaccuracyqandqconsistencyqofqtheqinputqdata.qIfq theqinputqda
taqareqnotqaccurate,qtheqresultsqcanqbeqmisleadingqandqveryqcostlyqtoqtheqorganization.q Thisqconceptqi
sqcalledq―garbageqin,q garbageqout.‖
1-
8.q Implementationqisqtheqprocessqofqtakingqtheqsolutionqandq incorporatingq itqintoqtheqcompanyq orqor
ganization.q Thisqisqtheqfinalqstepqinqtheqquantitativeqanalysisqapproach,qandqifqaqgoodq jobqisqnotqdone
qwithqimplementation, qallqofqthe qeffort qexpendedqonqthe qpreviousqstepsqcanqbe qwasted.
1-
9.q Sensitivityqanalysisqandqpostqoptimalityqanalysisqallowqtheqdecisionq makerqtoqdetermineqhowqtheq
finalqsolutionqtoqtheqproblemq willqchangeqwhenqtheqinputqdataqorqtheqmodelqchange.qThisqtypeqofqana
lysisq isqveryqimportantqwhenqtheqinputqdataqorqmodelqhasqnotqbeenqspecifiedqproperly.qAqsensitiveqso
lutionq isqoneqinq whichqtheqresultsqofqtheqsolutionqtoqtheqproblemqwillqchangeq drasticallyqorqbyqaqlarg
eqamountqwithqsmallqchangesqinqtheqdataqorqinqtheqmodel.qWhenqtheqmodelqisqnotqsensitive,qtheqresul
tsqorqsolutionsqtoqtheqmodelqwillqnotqchangeqsignificantlyqwithqchangesqinqtheqinputqdataqorqinqtheqmo
del.qModelsqthatqareqveryqsensitiveqrequireqthatqtheqinputqdataqandqtheqmodelqitselfqbeqthoroughlyqtes
tedqtoqmakeqsureqthatqbothqareqveryqaccurateqandqconsistentqwithqtheqproblemqstatement.
1-
10.q Thereqareqaqlargeqnumberqofqquantitativeqtermsqthatqmayqnotqbequnderstoodqbyqmanagers.qExam
plesqincludeqPERT,qCPM,qsimulation,qtheqMonteqCarloqmethod,qmathematicalqprogramming,qEOQ
,qandqsoqon.qTheqstudentqshouldqexplainqeachqofqtheqfourqtermsqselectedqinqhisqorqherqownqwords.
11-4
Copyright © 2024 Pearson Education, Inc.
