Lesson 1: Introduction
Lesson Overview
Introduction
Decision making is a critical managerial function. The environment in which managers make
decisions is often complex; they must deal with limited resources, laws and regulations,
uncertainty, and competition.
To make good decisions in such complex environments, managers need to apply a scientific
approach to the decision-making process.
Management science provides managers with a number of quantitative techniques that they
can use to make sound decisions. It is an interdisciplinary field comprising elements of
mathematics, economics, computer science, and engineering. Its specific content and
application has expanded enormously with the use of computer technologies.
This lesson introduces you to management science and the tools used to support the
decision-making process.
Reading Assignment and Learning Objectives
Textbook, Chapter 1, Sections 1.1 to 1.5, pages 2–19
Learning Objectives
After completing Lesson 1, you should be able to
1. describe the management science/operations research approach to decision making.
2. differentiate between decision making and problem solving.
3. briefly describe the quantitative techniques of management science and their frequency of
use in practice.
4. recognize that managerial problem situations have both quantitative and qualitative
considerations that are important in the decision-making process.
5. know the step-by-step procedure used in many quantitative approaches to decision
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making.
6. explain why models are used to solve problems.
7. identify the various types of models available for dealing with real-life situations.
8. differentiate between single-criterion decision problems and multicriteria decision
problems.
Lesson Notes
Note 1: Overview of Management Science
Management science and operations research focus on applying scientific and rational
methodologies and information technologies to the managerial decision-making process.
Emphasis is placed on the analysis of the problem, the decision environment, the objectives
of the organization, and the judgment of the decision makers. Throughout its history,
management science has proven to be effective in helping managers to make
purpose-oriented, information- and analysis-based decisions. It has also helped many
organizations to solve complex problems, suggest ways of making optimal use of resources,
perform systematic contingency planning, facilitate group decision making and
multidisciplinary approaches to problem solving, and make decisions involving multiple
objectives.
The use of computer-based management systems and analytical approaches to
management decision making has become common in both the public and private sectors
and in product-oriented as well as service industries. As organizations continue to develop
and implement sophisticated mathematical modelling and decision-aiding systems for all
aspects of their businesses, the demand for managers who can understand and use this
management technology has increased. Today, terms such as modelling, simulation, linear
programming, and project management are standard vocabulary. Management science
concepts are known not only to business people and management scientists but also to
government planners, military analysts, space scientists, regional planners, health care
administrators, and many other professionals.
Management science is an internationally recognized profession.
Note 2: Classification of Decision Making Models
A model is a simplified version of reality. The success of a model depends heavily on how
accurately it represents the real-life situation. This course examines a wide variety of
decision-making models. Two classifications of the basic decision-making models are
discussed below.
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Descriptive versus Normative Models
A descriptive model is one that shows a relationship between system components but does
not suggest a course of actions. Such models help managers to understand and predict the
behaviour of systems. The waiting line model (discussed in Lesson 8) is an ideal example of
a descriptive model.
A normative model is usually a mathematical model that describes a functional relationship
and prescribes a course of actions for achieving defined objectives. Normative models are,
therefore, prescriptive. Linear programming (Lessons 2, 3, and 4), transportation and
assignment (Lesson 5), integer programming (Lesson 6), and network models (Lesson 7)
are examples of normative decision-making models.
Deterministic versus Stochastic Models
A deterministic model is one in which all parameters are known with certainty. The
production model illustrated in Figure 1.5 of your textbook is an example of a deterministic
model. Linear programming, transportation and assignment, integer programming, and
network models are also examples of deterministic decision-making models.
A stochastic (or probabilistic) model is one in which the model parameters are not known
with certainty. Instead, model parameters have a known probability distribution. A
stochastic model may have some functional relationships that are both deterministic and
stochastic, or all relationships may be stochastic. The waiting line model is an example of a
stochastic model.
Lesson Review
Practice Questions
The questions on this page are for review purposes only.
Multiple-Choice and True/False Questions
Lesson 1 Quiz will test your knowledge of the material covered in this lesson. You can check
your answers within the quiz. You may repeat this quiz as many times as you like.
Short-Answer Questions and Problems
Answer the following short-answer practice questions. When you are done, compare your
responses to the suggested answers at the end of this lesson. If you have difficulty
completing any of these practice questions, contact the Student Support Centre.
1. Define management science (or operations research ).
2. Solve problem 4 on page 20 of your textbook.
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3. Provide one example of each of the following models:
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
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iconic
analog
mathematical
4. Solve problem 8 on pages 20–21 of your textbook.
Practice Activity
Complete the practice activities as directed below. After you have tried solving them on
your own, click the icon to the right of each question to view a suggested solution. If you
have any difficulty completing these practice activities, contact the Student Support Centre.
Read the article “Timber Harvesting Model at MeadWestvaco Corporation” on page 29 in
your textbook, then answer the questions that follow.
1. Which techniques listed in Section 1.5 are being used in the four
management science applications described at the MeadWestvaco
Corporation (Mead)?
 
2. Which of the Mead applications use a deterministic model and which use a
stochastic model? What conditions in the applications indicate a stochastic
model is necessary?
 
3. Discuss how quantitative analysis described in Section 1.3 occurs in Mead's
inventory analysis application.
 
4. Discuss the benefits associated with the management science applications at
Mead.
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