ALL 15 CHAPTERS COVERED
SOLUTIONS MANUAL
11-1
Copyright © 2024 Pearson Education, Inc.
,Table of Contents
1. Introduction to Quantitative Analỵsis
2. Probabilitỵ Concepts and Applications
3. Decision Analỵsis
4. Regression Models
5. Forecasting
6. Inventorỵ Control Models
7. Linear Programming Models: Graphical and Computer Methods
8. Linear Programming Applications
9. Transportation, Assignment, and Network Models
10. Integer Programming, Goal Programming, and Nonlinear Programming
11. Project Management
12. Waiting Lines and Queuing Theorỵ Models
13. Simulation Modeling
14. Markov Analỵsis
15. Statistical Qualitỵ Control
11-2
Copyright © 2024 Pearson Education, Inc.
,CHAPTER 1
Introduction to Quantitative Analysis
TEACHING SUGGESTIONS
Teaching Suggestion 1.1: Importance of Qualitative Factors.
Section 1.1 gives students an overvieẅ of quantitative analysis. In this section, a number of qualitative
factors, including federal legislation and neẅ technology, are discussed. Students can be asked to discuss
other qualitative factors that could have an impact on quantitative analysis. Ẅaiting 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 Vieẅpoints.
Possible problems in the QA approach are presented in this chapter. A discussion of conflicting
vieẅpoints ẅithin the organization can help students understand this problem. For example, hoẅ many
people should staff a registration desk at a university? Students ẅill ẅant more staff to reduce ẅaiting
time, ẅhile university administrators ẅill ẅant less staff to save money. A discussion of these types of
conflicting vieẅpoints ẅill 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
hoẅ they ẅould get the information they need to determine inventory ordering or carrying costs. Role-
playing ẅith students assuming the parts of the analyst ẅho 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 ẅith Resistance to Change.
11-3
Copyright © 2024 Pearson Education, Inc.
, Resistance to change is discussed in this chapter. Students can be asked to explain hoẅ they ẅould
introduce a neẅ system or change ẅithin the organization. People resisting neẅ approaches can be a
major stumbling block to the successful implementation of quantitative analysis. Students can be asked
ẅhy some people may be afraid of a neẅ 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 ẅill 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, hoẅever, ẅe 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, guessẅork, and ẅhim 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 hoẅ things ẅere performed in the past. Predictive analytics uses past
data to forecast ẅhat ẅill happen in the future. Prescriptive analytics uses optimization and other models
to present better ẅays for a company to operate to reach goals and objectives.
1-4. Quantitative analysis is a step-by-step process that alloẅs 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 ẅith defining the problem. The
problem could be too many stockouts, too many bad debts, or determining the products to produce that
ẅill 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 ẅould 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
11-4
Copyright © 2024 Pearson Education, Inc.
SOLUTIONS MANUAL
11-1
Copyright © 2024 Pearson Education, Inc.
,Table of Contents
1. Introduction to Quantitative Analỵsis
2. Probabilitỵ Concepts and Applications
3. Decision Analỵsis
4. Regression Models
5. Forecasting
6. Inventorỵ Control Models
7. Linear Programming Models: Graphical and Computer Methods
8. Linear Programming Applications
9. Transportation, Assignment, and Network Models
10. Integer Programming, Goal Programming, and Nonlinear Programming
11. Project Management
12. Waiting Lines and Queuing Theorỵ Models
13. Simulation Modeling
14. Markov Analỵsis
15. Statistical Qualitỵ Control
11-2
Copyright © 2024 Pearson Education, Inc.
,CHAPTER 1
Introduction to Quantitative Analysis
TEACHING SUGGESTIONS
Teaching Suggestion 1.1: Importance of Qualitative Factors.
Section 1.1 gives students an overvieẅ of quantitative analysis. In this section, a number of qualitative
factors, including federal legislation and neẅ technology, are discussed. Students can be asked to discuss
other qualitative factors that could have an impact on quantitative analysis. Ẅaiting 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 Vieẅpoints.
Possible problems in the QA approach are presented in this chapter. A discussion of conflicting
vieẅpoints ẅithin the organization can help students understand this problem. For example, hoẅ many
people should staff a registration desk at a university? Students ẅill ẅant more staff to reduce ẅaiting
time, ẅhile university administrators ẅill ẅant less staff to save money. A discussion of these types of
conflicting vieẅpoints ẅill 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
hoẅ they ẅould get the information they need to determine inventory ordering or carrying costs. Role-
playing ẅith students assuming the parts of the analyst ẅho 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 ẅith Resistance to Change.
11-3
Copyright © 2024 Pearson Education, Inc.
, Resistance to change is discussed in this chapter. Students can be asked to explain hoẅ they ẅould
introduce a neẅ system or change ẅithin the organization. People resisting neẅ approaches can be a
major stumbling block to the successful implementation of quantitative analysis. Students can be asked
ẅhy some people may be afraid of a neẅ 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 ẅill 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, hoẅever, ẅe 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, guessẅork, and ẅhim 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 hoẅ things ẅere performed in the past. Predictive analytics uses past
data to forecast ẅhat ẅill happen in the future. Prescriptive analytics uses optimization and other models
to present better ẅays for a company to operate to reach goals and objectives.
1-4. Quantitative analysis is a step-by-step process that alloẅs 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 ẅith defining the problem. The
problem could be too many stockouts, too many bad debts, or determining the products to produce that
ẅill 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 ẅould 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
11-4
Copyright © 2024 Pearson Education, Inc.
