SIM - Mc leod ch11

© 2007 by Prentice Hall Management Information Systems, 10/e
Raymond McLeod and George Schell
1
Management
Information
Systems, 10/e

2
Chapter 11
Decision Support Systems

3
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.

4
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.

5
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.

6
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 Gorry-Scott
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 & Scott-
Morton 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.

23
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 11.6-11.10
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.

38
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 knowledge-based
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), computer-
supported 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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