McLeod_CH11.ppt 2468KB Jan 28 2009 12:06:50 AM

Management
Information Systems,
Raymond McLeod
10/eand George
Schell

© 2007 by Prentice Hall

Management Information Systems, 10/e R
aymond McLeod and George Schell

1

Chapter 11
Decision Support Systems

© 2007 by Prentice Hall

Management Information Systems, 10/e R
aymond McLeod and George Schell

2

Learning Objectives
► Understand

the fundamentals of
decision making & problem solving.
► Know how the decision support system
(DSS) concept originated.
► Know the fundamentals of
mathematical modeling.
► Know how to use an electronic
spreadsheet as a mathematical model.
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Learning Objectives (Cont’d)
► Be

familiar with how artificial
intelligence emerged as a computer
application & know its main areas.
► Know the four basic parts of an expert
system.
► Know what a group decision support
system (GDSS) is & the different
environmental settings that can be used.
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Problem-Solving & Decision
Making Review
► Problem

solving consists of response to
things going well & also to things going
badly.
► Problem is a condition or event that is
harmful or potentially harmful to a firm or
that is beneficial or potentially beneficial.
► Decision making is the act of selecting
from alternative problem solutions.
► Decision is a selected course of action.
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Problem-Solving Phases
► Herbert

A. Simon’s four basic phases:

 Intelligence phase – Searching the
environment for conditions calling for a
solution.
 Design activity – inventing, developing,
& analyzing possible course of actions.
 Choice activity – Selecting a particular
course of action from those available.
 Review activity – Assessing past choices.
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Frameworks & Systems
Approach
► Problem-solving

frameworks

 General systems model of the firm.
 Eight-element environmental model.
► Systems

approach to problem-solving,
involves a series of steps grouped into
three phases – preparation effort,
definition effort, & solution effort.

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Importance of Systems View
►

Systems view which regards business operations as
systems embedded within a larger environmental setting;
abstract way of thinking; potential value to the manager.
 Prevents the manager from getting lost in the
complexity of the organizational structure & details of
the job.
 Recognizes the necessity of having good objectives.
 Emphasizes the importance of all of the parts of the
organization working together.
 Acknowledges the interconnections of the organization
with its environment.
 Places a high value on feedback information that can

only be achieved by means of a closed-loop system.

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Building on the Concepts
►

►

►

Elements of a problem-solving phase.
 Desired state – what the system should achieve.
 Current state – what the system is now achieving.

 Solution criterion – difference between the current
state & the desired state.
Constraints.
 Internal take the form of limited resources that exist
within the firm.
 Environmental take the form of pressures from
various environmental elements that restrict the flow of
resources into & out of the firm.
When all of these elements exist & the manager
understands them, a solution to the problem is possible!
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Figure 11.1 Elements of the
Problem-Solving Process

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Selecting the Best Solution
► Henry

Mintzberg, management theorist,
has identified three approaches:
► Analysis – a systematic evaluation of
options.
► Judgment – the mental process of a
single manager.
► Bargaining – negotiations between
several managers.

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Problem vs. Symptoms
► Symptom

is a condition produced by the problem.
► Structured problem consists of elements &
relationships between elements, all of which are
understood by the problem solver.
► Unstructured problem is one that contains no
elements or relationships between elements that
are understood by the problem solver.
► Semistructured problem is one that contains
some elements or relationships that are

understood by the problem solver & some that are
not.
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Types of Decisions
►

Programmed decisions are “repetitive & routine,

to the extent that a definite procedure has been
worked out for handling them so that they don’t
have to be treated de novo (as new) each time
they occur.
► Nonprogrammed decisions are “novel,
unstructured, & unusually consequential.
There’s no cut-and-dried method for handling
the problem because its precise nature &
structure are elusive or complex, because it is so
important that it deserves a custom-tailored
treatment”.
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Decision Support Systems
►

►

►

►

Gorry & Scott Morton (1971) argued that an
information system that focused on single problems
faced by single managers would provide better
support.
Central to their concept was a table, called the GorryScott Morton grid (Figure 11.2) that classifies problems
in terms of problem structure & management level.
The top level is called the strategic planning level, the
middle level - the management control level, & the
lower level - the operational control level.
Gorry & Scott Morton also used the term decision
support system (DSS) to describe the systems that
could provide the needed support.

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Figure 11.2 The Gorry & ScottMorton Grid

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A DSS Model
►

►
►
►

►

Originally the DSS was conceived to produce periodic
& special reports (responses to database queries), &
outputs from mathematical models.
An ability was added to permit problem solvers to work
in groups.
The addition of groupware enabled the system to
function as a group decision support system (GDSS).
Figure 11.3 is a model of a DSS. The arrow at the
bottom indicates how the configuration has expanded
over time.
More recently, artificial intelligence (AI) capability has
been added, along with an ability to engage in online
analytical programming (OLAP).

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Figure 11.3 DSS Model that
Incorporates GDSS, OLAP, & AI

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Mathematical Modeling
►
►

Model is an abstraction of something. It represents
some object or activity, which is called an entity.
There are four basic types of models:
 Physical model is a three-dimensional
representation of its entity.
 Narrative model, which describes its entity with
spoken or written words.
 Graphic model represents its entity with an
abstraction of lines, symbols, or shapes (Figure
11.4).
► Economic

order quantity (EOQ) is the optimum
quantity of replenishment stock to order from a supplier.

 Mathematical model is any mathematical
formula or equation.
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Formula to Compute Economic
Order Quantity (EOQ)

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Figure 11.4 Graphical Model of
EOQ

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Uses of Models
►

►

►

►

Facilitate Understanding: Once a simple model is
understood, it can gradually be made more complex
so as to more accurately represent its entity.   
Facilitate Communication: All four types of models
can communicate information quickly and accurately.
  
Predict the Future:  The mathematical model can
predict what might happen in the future but a
manager must use judgment & intuition in evaluating
the output.
A mathematical model can be classified in terms of
three dimensions: the influence of time, the degree of
certainty, & the ability to achieve optimization.

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Classes of Mathematical
Models
►
►
►

Static model doesn’t include time as a variable
but deals only with a particular point in time.
Dynamic model includes time as a variable; it
represents the behavior of the entity over time.
Probabilistic model includes probabilities.
Otherwise, it is a deterministic model.
 Probability is the chance that something will happen.

►
►

Optimizing model is one that selects the best
solution among the alternatives.
Suboptimizing model (satisficing model) does
not identify the decisions that will produce the best
outcome but leaves that task to the manager.

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Simulation
►

►

►

►
►

The act of using a model is called simulation while the
term scenario is used to describe the conditions that
influence a simulation.
For example, if you are simulating an inventory system,
as shown in Figure 11.5, the scenario specifies the
beginning balance & the daily sales units.
Models can be designed so that the scenario data
elements are variables, thus enabling different values
to be assigned.
The input values the manager enters to gauge their
impact on the entity are known as decision variables.
Figure 11.5 gives an example of decision variables such
as order quantity, reorder point, & lead time.

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Figure 11.5 Scenario Data &
Decision Variables from a
Simulation

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Simulation Technique & Format
of Simulation Output
► The

manager usually executes an optimizing
model only a single time.
► Suboptimizing models, however, are run over &
over, in a search for the combination of
decision variables that produces a satisfying
outcome (known as playing the what-if game).
► Each time the model is run, only one decision
variable should be changed, so its influence can
be seen.
► This way, the problem solver systematically
discovers the combination of decisions leading
to a desirable solution.
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A Modeling Example
►

►

►

A firm’s executives may use a math model to assist
in making key decisions & to simulate the effect of:
1.Price of the product;
2.Amount of plant investment;
3.Amount to be invested in marketing activity;
4.Amount to be invested in R & D.
Furthermore, executives want to simulate 4 quarters
of activity & produce 2 reports: an operating
statement & an income statement.
Figures 11.6 and 11.7 shows the input screen used to
enter the scenario data elements for the prior
quarter & next quarter, respectively.

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Figure 11.6 Model Input Screen
for Entering Scenario Data for
Prior

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Figure 11.7 Model Input Screen
for Entering Scenario Data for
Next

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Model Output
► The

next quarter’s activity (Quarter 1) is
simulated, & the after-tax profit is displayed on
the screen.
► The executives then study the figure & decide on
the set of decisions to be used in Quarter 2.
These decisions are entered & the simulation is
repeated.
► This process continues until all four quarters
have been simulated. At this point the screen
has the appearance shown in Figure 11.8.
► The operating statement in Figure 11.9 & the
income statement in Figure 11.10 are displayed
on separate screens.
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Figure 11.8 Summary Output
from the Model

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Figure 11.9 Operating
Statement Shows Nonmonetary
Results

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Figure 11.10 Income Statement
Shows Nonmonetary Results

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Modeling Advantages &
Disadvantages
►

Advantages:
 The modeling process is a learning experience.
 The speed of the simulation process enables the
consideration of a larger number of alternatives.
 Models provide a predictive power - a look into the future that no other information-producing method offers.
 Models are less expensive than the trial-and-error method.

►

Disadvantages:
 The difficulty of modeling a business system will produce a
model that does not capture all of the influences on the
entity.
 A high degree of mathematical skill is required to develop
& properly interpret the output of complex models.

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Mathematical Modeling Using
Electronic Spreadsheets
►

►

►

►

The technological breakthrough that enabled problem
solvers to develop their own math models was the electronic
spreadsheet.
Static model: Figure 11.11 shows an operating budget in
column form. The columns are for: the budgeted expenses,
actual expenses, & variance, while rows are used for the
various expense items.
A spreadsheet is especially well-suited for use as a dynamic
model. The columns are excellent for the time periods, as
illustrated in Figure 11.12.
A spreadsheet also lends itself to playing the “what-if”
game, where the problem solver manipulates 1 or more
variables to see the effect on the outcome of the simulation.
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Figure 11.11 Spreadsheet Rows
& Columns Provide Format for
Columnar Report

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Figure 11.12 Spreadsheet
Columns are Excellent for Time
Periods in Dynamic Model

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Spreadsheet Model Interface
►

►

►

►

When using a spreadsheet as a mathematical model,
the user can enter data or make changes directly to
the spreadsheet cells, or by using a GUI
The pricing model described earlier in Figures 11.611.10 could have been developed using a
spreadsheet, and had the graphical user interface
added
The interface could be created using a programming
language such as Visual Basic and would likely require
an information specialist to develop
A development approach would be for the user to
develop the spreadsheet and then have the interface
added by an information specialist.

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Artificial Intelligence
►

►

►

►

Artificial intelligence (AI) is the activity of
providing such machines as computers with the
ability to display behavior that would be regarded as
intelligent if it were observed in humans.
AI is being applied in business in knowledgebased systems, which use human knowledge to
solve problems.
The most popular type of knowledge-based system
are expert systems, which are computer programs
that try to represent the knowledge of human
experts in the form of heuristics.
These heuristics allow an expert system to consult
on how to solve a problem: called a consultation the user consults the expert system for advice.

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Areas of AI
► Expert

system is a computer program that
attempts to represent the knowledge of
human experts in the form of heuristics.
► Heuristic is a rule of thumb or a rule of
good guessing.
► Consultation is the act of using an expert
system.
► Knowledge engineer has special expertise
in artificial intelligence; adept in obtaining
knowledge from the expert.
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Areas of AI (Cont’d)
► Neural

networks mimic the
physiology of the human brain.
► Genetic algorithms apply the
“survival of the fittest” process to
enable problem solvers to produce
increasingly better problem solutions.
► Intelligent agents are used to
perform repetitive computer-related
tasks; i.e. data mining.
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Expert System Configuration
► User

interface enables the manager to
enter instructions & information into the
expert system & to receive information from
it.
► Knowledge base contains both facts that
describe the problem area & knowledge
representation techniques that describe how
the facts fit together in a logical manner.
► Problem domain is used to describe the
problem area.
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Expert System Configuration
(Cont’d)
► Rule

specifies what to do in a given
situation & consists of two parts:
 A condition that may or may not be true, and
 An action to be taken when the condition is true.

► Inference

engine is the portion of the expert
system that performs reasoning by using the
contents of the knowledge base in a
particular sequence.
► Goal variable is assigning a value to the
problem solution.
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Expert System Configuration
(Cont’d)
► Expert

system shell is a ready-made
processor that can be tailored to a specific
problem domain through the addition of the
appropriate knowledge base.
► Case-based reasoning (CBR) uses
historical data as the basis for identifying
problems & recommending solutions.
► Decision tree is a network-like structure
that enables the user to progress from the
root through the network of branches by
answering questions relating to the problem.
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Figure 11.13 Expert System
Model

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Group Decision Support
System
► Group

decision support system (GDSS) is “a
computer-based system that supports groups of
people engaged in a common task (or goal) & that
provides an interface to a shared environment”.
► Aliases group support system (GSS), computersupported cooperative work (CSCW),
computerized collaborative work support, &
electronic meeting system (EMS).
► Groupware the software used in these settings.
► Improved communications make possible improved
decisions.

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GDSS Environmental Settings
►
►
►
►
►
►

Synchronous exchange when members meet at
the same time.
Asynchronous exchange when members meet at
different times.
Decision room is the setting for small groups of
people meeting face-to-face.
Facilitator is the person whose chief task is to
keep the discussion on track.
Parallel communication is when all participants
enter comments at the same time,&
Anonymity is when nobody is able to tell who
entered a particular comment; participants say
what they REALLY think without fear.

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Figure 11.14 Group Size &
Location Determine DSS
Environmental Settings

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GDSS Environmental Settings
(Cont’d)
►

►

Local area decision network when it is impossible
for small groups of people to meet face-to-face, the
members can interact by means of a local area
network, or LAN.
Legislative session when the group is too large for a
decision room.
 Imposes certain constraints on communications such as equal
participation by each member is removed or less time is
available.

►

Computer-mediated conference several virtual
office applications permit communication between
large groups with geographically dispersed members.
 Teleconferencing applications include computer conferencing,
audio conferencing, & videoconferencing.

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