,Table of Contents
i. Introduction to Quantitative Anal.sis
z. Proaaailit. Concepts and Applications
3. Decision Anal.sis
4. Regression Models
s. Forecasting
6. Inventor. Control Models
7. Linear Programming Models: Graphical and Computer Methods
8. Linear Programming Applications
9. Transportation, Assignment, and Netyrork Models
iO. Integer Programming, Goal Programming, and Nonlinear Programming
ii. Project Management
iz. tÿaiting Lines and Queuing Theeor. Models
i3. Simulation Modeling
i4. Markov Anal.sis
is. Statistical Qualit. Control
11-2
Copy.right Ò 2024 Pea.rson Education, Inc.
,CHAPTER 1
Introduction to Quantitative Anal.sis
TEACHING SUGGESTIONS
Teachinq Suqqestion 1.1: łmportance ofQualitative Factors.
Section 1.1 gives students an overview of quantitative anal.sis. In this section, a number of
qualitative factors, including federal legislation and new technolog., are discussed.
Students can be asked to discuss other qualitative factors that could have an impact on
quantitative anal.sis. Waiting lines and project planning can be used as examples.
Teachinq Suqqestion 1.z: Discussing Other Quantitative Analy.sis Problems.
Section 1.z covers an application of the quantitative anal.sis approach. Students can be
asked to describe other problems or areas that could benefit from quantitative anal.sis.
Teachinq Suqqestion 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 man. people should staff a registration desk at a universit.? Students
will want more staff to reduce waiting time, while universit. administrators will want less
staff to save mone.. A discussion of these t.pes of conflicting viewpoints will help students
understand some of the problems of using quantitative anal.sis.
Teachinq Suqqestion 1.4: Difficulty. ofGetting łnput Data.
A major problem in quantitative anal.sis is getting proper input data. Students can be
asked to explain how the. would get the information the. need to determine inventor.
ordering or carr.ing costs. Roleppla.ing with students assuming the parts of the anal.st
who needs inventor. costs and the instructor pla.ing the part of a veteran inventor.
manager can be fun and interesting. Students quickl. learn that getting good data can be
the most difficult part of using quantitative anal.sis.
Teachinq Suqqestion 1.s: Dealing with Resistance to Change.
11-3
Copy.right Ò 2024 Pea.rson Education, Inc.
, Resistance to change is discussed in this chapter. Students can be asked to explain how the.
would introduce a new s.stem or change within the organization. People resisting new
approaches can be a major stumbling block to the successful implementation of
quantitative anal.sis. Students can be asked wh. some people ma. be afraid of a new
inventor. control or forecasting s.stem.
SOLUTIONS TO DISCUSSION QUESTIONS AND PROBLEMS
i-i. Quantitative anal.sis involves the use of mathematical equations or relationships in
anal.zing a particular problem. In most cases, the results of quantitative anal.sis will be
one or more numbers that can be used b. 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 man. similar techniques are examples of quantitative anal.sis. Qualitative
anal.sis involves the investigation of factors in a decision-making problem that cannot be
quantified or stated in mathematical terms. The state of the econom., current or pending
legislation, perceptions about a potential client, and similar situations reveal the use of
qualitative anal.sis. In most decision-making problems, both quantitative and qualitative
anal.sis are used. In this book, however, we emphasize the techniques and approaches of
quantitative anal.sis.
i-z. Quantitative anaI.sis is the scientific approach to managerial decision making. This
t.pe of anal.sis is a logical and rational approach to making decisions. Emotions,
guesswork, and whim are not part of the quantitative anal.sis 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 Academ. of
Management.
i-3. The three categories of business anal.tics are descriptive, predictive, and prescriptive.
Descriptive anal.tics provides an indication of how things were performed in the past.
Predictive anal.tics uses past data to forecast what will happen in the future. Prescriptive
anal.tics uses optimization and other models to present better wa.s for a compan. to
operate to reach goals and objectives.
i-4. Quantitative anal.sis is a step-b.-step process that allows decision makers to
investigate problems using quantitative techniques. The steps of the quantitative anal.sis
process include defining the problem, developing a model, acquiring input data, developing
a solution, testing the solution, anal.zing the results, and implementing the results. In
ever. case, the anal.sis begins with defining the problem. The problem could be too man.
stockouts, too man. 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 inventor. 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 inventor. problem, for
example, such factors as the annual demand, the ordering cost, and the carr.ing cost would
be input data that are used b. 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 profitabilit. 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
Copy.right Ò 2024 Pea.rson Education, Inc.
i. Introduction to Quantitative Anal.sis
z. Proaaailit. Concepts and Applications
3. Decision Anal.sis
4. Regression Models
s. Forecasting
6. Inventor. Control Models
7. Linear Programming Models: Graphical and Computer Methods
8. Linear Programming Applications
9. Transportation, Assignment, and Netyrork Models
iO. Integer Programming, Goal Programming, and Nonlinear Programming
ii. Project Management
iz. tÿaiting Lines and Queuing Theeor. Models
i3. Simulation Modeling
i4. Markov Anal.sis
is. Statistical Qualit. Control
11-2
Copy.right Ò 2024 Pea.rson Education, Inc.
,CHAPTER 1
Introduction to Quantitative Anal.sis
TEACHING SUGGESTIONS
Teachinq Suqqestion 1.1: łmportance ofQualitative Factors.
Section 1.1 gives students an overview of quantitative anal.sis. In this section, a number of
qualitative factors, including federal legislation and new technolog., are discussed.
Students can be asked to discuss other qualitative factors that could have an impact on
quantitative anal.sis. Waiting lines and project planning can be used as examples.
Teachinq Suqqestion 1.z: Discussing Other Quantitative Analy.sis Problems.
Section 1.z covers an application of the quantitative anal.sis approach. Students can be
asked to describe other problems or areas that could benefit from quantitative anal.sis.
Teachinq Suqqestion 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 man. people should staff a registration desk at a universit.? Students
will want more staff to reduce waiting time, while universit. administrators will want less
staff to save mone.. A discussion of these t.pes of conflicting viewpoints will help students
understand some of the problems of using quantitative anal.sis.
Teachinq Suqqestion 1.4: Difficulty. ofGetting łnput Data.
A major problem in quantitative anal.sis is getting proper input data. Students can be
asked to explain how the. would get the information the. need to determine inventor.
ordering or carr.ing costs. Roleppla.ing with students assuming the parts of the anal.st
who needs inventor. costs and the instructor pla.ing the part of a veteran inventor.
manager can be fun and interesting. Students quickl. learn that getting good data can be
the most difficult part of using quantitative anal.sis.
Teachinq Suqqestion 1.s: Dealing with Resistance to Change.
11-3
Copy.right Ò 2024 Pea.rson Education, Inc.
, Resistance to change is discussed in this chapter. Students can be asked to explain how the.
would introduce a new s.stem or change within the organization. People resisting new
approaches can be a major stumbling block to the successful implementation of
quantitative anal.sis. Students can be asked wh. some people ma. be afraid of a new
inventor. control or forecasting s.stem.
SOLUTIONS TO DISCUSSION QUESTIONS AND PROBLEMS
i-i. Quantitative anal.sis involves the use of mathematical equations or relationships in
anal.zing a particular problem. In most cases, the results of quantitative anal.sis will be
one or more numbers that can be used b. 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 man. similar techniques are examples of quantitative anal.sis. Qualitative
anal.sis involves the investigation of factors in a decision-making problem that cannot be
quantified or stated in mathematical terms. The state of the econom., current or pending
legislation, perceptions about a potential client, and similar situations reveal the use of
qualitative anal.sis. In most decision-making problems, both quantitative and qualitative
anal.sis are used. In this book, however, we emphasize the techniques and approaches of
quantitative anal.sis.
i-z. Quantitative anaI.sis is the scientific approach to managerial decision making. This
t.pe of anal.sis is a logical and rational approach to making decisions. Emotions,
guesswork, and whim are not part of the quantitative anal.sis 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 Academ. of
Management.
i-3. The three categories of business anal.tics are descriptive, predictive, and prescriptive.
Descriptive anal.tics provides an indication of how things were performed in the past.
Predictive anal.tics uses past data to forecast what will happen in the future. Prescriptive
anal.tics uses optimization and other models to present better wa.s for a compan. to
operate to reach goals and objectives.
i-4. Quantitative anal.sis is a step-b.-step process that allows decision makers to
investigate problems using quantitative techniques. The steps of the quantitative anal.sis
process include defining the problem, developing a model, acquiring input data, developing
a solution, testing the solution, anal.zing the results, and implementing the results. In
ever. case, the anal.sis begins with defining the problem. The problem could be too man.
stockouts, too man. 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 inventor. 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 inventor. problem, for
example, such factors as the annual demand, the ordering cost, and the carr.ing cost would
be input data that are used b. 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 profitabilit. 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
Copy.right Ò 2024 Pea.rson Education, Inc.
