Lecture 03 decision making

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IS - 332

DECISION SUPPORT SYSTEMS

Lecture 03

Dr. Abdul Rauf Baig

Second Semester 2010-2011

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Topic 02: Decision Making


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DECISION MAKING

Summary of previous lectures:

1. From databases to DSS

2. Computerized support for decision making: its benefits

3. Framework for computerized decision support

4. Framework for business intelligence


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Decision Making: Introduction

DECISION MAKING


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What is decision making?

Decision making is a process of choosing among two or more

alternate courses of action for the purpose of attaining a goal

(or goals)

Managerial decision making is a complex task in today’s

business environment.

DECISION MAKING


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Phases of Decision Making Process

DECISION MAKING


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Decision Making Phases

Systematic decision making involves three major phases

followed by the implementation phase:

• Intelligence or Information gathering,

• Design,

• Choice,

• Implementation

Decision making process starts with the intelligence or

information gathering phase, where reality is examined and

the problem is identified.

DECISION MAKING: Four Phases


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Decision Making Phases

In the design phase, a model that represents the system is

constructed.

The choice phase includes selection of a proposed solution to

the model.

Once the proposed solution seems to be reasonable, we are

ready for the last phase: implementation.

DECISION MAKING: Four Phases


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Decision Making Phases:

Intelligence & Information Gathering

DECISION MAKING: Intelligence Phase


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Decision Making: Intelligence Phase

Intelligence or information gathering includes several

activities aimed at identifying problem situations or

opportunities.

1. Problem or opportunity identification

2. Problem classification

3. Programmed versus non-programmed problems

4. Problem decomposition

5. Problem ownership



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Problem or Opportunity Identification:

The intelligence or information gathering phase begins with

the identification of organizational goals and objectives

related to an issue of concern (e.g. inventory management,

job selection).

Problems occur because of dissatisfaction with the status

quo. Dissatisfaction is the result of a difference between what

we desire (or expect) and what is occurring.



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In the first phase, one attempts to determine whether a

problem exists, identify its symptoms, determine its

magnitude, and explicitly define it.

The existence of a problem can be determined by monitoring

and analyzing the organization’s productivity level. This is

based on real data.



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- Data may not be available

- Obtaining data may be expensive

- Data may not be accurate or precise

- Data estimation is often subjective

- Important data that influence the

results may be qualitative

- There may be too much data

(information overload)

- Outcomes or results may occur

over an extended period of time. As

a result, revenues, expenses, and

profits will be recorded at different

points in time.

- It is assumed that future data will

be similar to historic data.





Some issues that may arise during data collection and

estimation are:

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Problem Classification:

Problem classification is the placement of a problem in a

definable category.

This leads to a standard solution approach.

An important classification is according to the degree of

structuredness evident in the problem. This ranges from

totally structured to totally unstructured



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Problem Decomposition:

Many complex problems can be divided into sub-problems.

Solving the simpler sub-problems may help in solving the

complex problem.

Some unstructured problems may have some highly

structure sub-problems

Decomposition also facilitates communication among

decision makers



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Problem Ownership:

A problem exists in an organization only if someone or some

groups takes on the responsibility of attacking it and if the

organization has the ability to solve it.



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Decision Making:

Design Phase

DECISION MAKING: Design Phase


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Decision Making: Design Phase

The design phase involves finding (or developing) and

analyzing possible courses of action.

These include understanding the problem and testing

solutions for feasibility.

A model of the decision making problem is constructed,

tested, and validated



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Modelling involves abstracting the problem to quantitative

and/or qualitative forms.

For a mathematical model, the variables are identified and

the relationships among them are established.



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Components of Quantitative Models:

All models are made up of three basic components: decision

variables, uncontrollable variables, and result (outcome)

variables

Mathematical relationships link these components together

In a non-quantitative model, the relationships are symbolic

or qualitative.



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Components of Quantitative Models: Result Variables

Result variables are outputs.

They reflect the level of effectiveness of the system.

These are dependent variables.



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Components of Quantitative Models: Decision Variables

Decision variables describe alternative courses of action.

Example: For an investment problem, the amount to invest

in bonds is a decision variable.

In a scheduling problem, the decision variables are people,

times, and schedules.



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Components of Quantitative Models: Uncontrollable Variables

In any decision making situation, there are factors that affect

the result variables but are not under the control of the

decision maker.

Either these factors are fixed (called parameters) or they can

vary.

Examples: Prime interest rate, a city’s building code, tax

regulations



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Components of Quantitative Models: Intermediate Results

Variables

Intermediate result variables reflect intermediate outcomes.

Example: Employee’s salaries is a decision variable,

It determines employee’s satisfaction

(intermediate variable),

which determines productivity level (final

outcome)



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Structure of Quantitative Models:

The components (i.e. decision variables, result variables, etc.)

of a quantitative model are linked together by mathematical

expressions.

Example: Profit = Revenue - Cost



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Selection of a Principle of Choice:

A principle of choice is a criterion that describes the

acceptability of a solution approach.

Two types: normative and descriptive

Normative models: Normative implies that the chosen

alternative is demonstrably the best of all possible

alternatives. To find it, one should examine al alternatives

and prove that the one selected is indeed the best. The

process is basically optimization



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Selection of a Principle of Choice:

Descriptive models: They investigate alternate courses of

action under different configurations of inputs and processes.

Al the alternatives are not checked, only a given set of

alternatives are checked.



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Decision Making:

Choice Phase


DECISION MAKING: Choice Phase

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Decision Making: Choice Phase

Choice is the critical act of decision making.

The choice phase is the one in which the actual decision is

made and where the commitment to follow a certain course

of action is made.

The choice phase includes search for, evaluation of, and

recommendation of an appropriate solution to the model

(problem).

The boundary between the design and choice phases is often

unclear Second Semester 2010-2011

DECISION MAKING: Choice Phase

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Decision Making:

Implementation Phase


DECISION MAKING: Implementation Phase

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Decision Making: Implementation Phase

Implementation means putting a recommended solution to

work.

It does not stop at implementing a computer system. There

are many issues involved, such as user expectations,

resistance to change, and user training


DECISION MAKING: Implementation Phase

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Decision Making: How decisions are supported

DECISION MAKING


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Support for Intelligence Phase

The primary requirement of decision support for the

intelligence phase is the ability to scan external and internal

information sources for opportunities and problems and to

interpret what the scanning discovers

DECISION MAKING: How decisions are supported


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Support for Design Phase

The design phase involves generating alternate courses of

actions, discussing the criteria for choices and their relative

importance, and forecasting the future consequences of using

various alternatives

Several of these activities can use standard models provided

by a DSS (e.g. financial and forecasting models)



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Support for Choice Phase

In addition to providing models that rapidly identify a best

or good-enough alternative, a DSS can support the choice

phase through what-if and goal seeking analyses

Different scenarios can be tested for the selected option to

reinforce the final decision



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Support for Implementation Phase

The DSS benefits implementation phase through the

vividness and detail of analyses and reports

This improves the communication, explanation, and

justification of decisions



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Reference

Chapter 2:

Sections 2.1 to 2.9 (except 2.3)


DECISION MAKING

Lecture 03 decision making

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