- 1. 2007 by Prentice Hall Management Information Systems, 10/e
Raymond McLeod and George Schell 1 Management Information Systems,
10/e Raymond McLeod and George Schell
2. 2007 by Prentice Hall Management Information Systems, 10/e
Raymond McLeod and George Schell 2 Chapter 11 Decision Support
Systems 3. 2007 by Prentice Hall Management Information Systems,
10/e Raymond McLeod and George Schell 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.
2007 by Prentice Hall Management Information Systems, 10/e Raymond
McLeod and George Schell 4 Learning Objectives (Contd) 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. 2007 by
Prentice Hall Management Information Systems, 10/e Raymond McLeod
and George Schell 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. 2007 by Prentice Hall Management Information Systems,
10/e Raymond McLeod and George Schell 6 Problem-Solving Phases
Herbert A. Simons 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. 7.
2007 by Prentice Hall Management Information Systems, 10/e Raymond
McLeod and George Schell 7 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. 8.
2007 by Prentice Hall Management Information Systems, 10/e Raymond
McLeod and George Schell 8 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. 9. 2007
by Prentice Hall Management Information Systems, 10/e Raymond
McLeod and George Schell 9 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! 10. 2007 by Prentice Hall Management
Information Systems, 10/e Raymond McLeod and George Schell 10
Figure 11.1 Elements of the Problem-Solving Process 11. 2007 by
Prentice Hall Management Information Systems, 10/e Raymond McLeod
and George Schell 11 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.
12. 2007 by Prentice Hall Management Information Systems, 10/e
Raymond McLeod and George Schell 12 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. 13. 2007 by Prentice
Hall Management Information Systems, 10/e Raymond McLeod and George
Schell 13 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 dont have to be treated
de novo (as new) each time they occur. Nonprogrammed decisions are
novel, unstructured, & unusually consequential. Theres 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. 14. 2007 by
Prentice Hall Management Information Systems, 10/e Raymond McLeod
and George Schell 14 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. 15. 2007 by Prentice Hall Management Information
Systems, 10/e Raymond McLeod and George Schell 15 Figure 11.2 The
Gorry & Scott- Morton Grid 16. 2007 by Prentice Hall Management
Information Systems, 10/e Raymond McLeod and George Schell 16 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). 17. 2007 by
Prentice Hall Management Information Systems, 10/e Raymond McLeod
and George Schell 17 Figure 11.3 DSS Model that Incorporates GDSS,
OLAP, & AI 18. 2007 by Prentice Hall Management Information
Systems, 10/e Raymond McLeod and George Schell 18 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. 19. 2007 by Prentice Hall
Management Information Systems, 10/e Raymond McLeod and George
Schell 19 Formula to Compute Economic Order Quantity (EOQ) 20. 2007
by Prentice Hall Management Information Systems, 10/e Raymond
McLeod and George Schell 20 Figure 11.4 Graphical Model of EOQ 21.
2007 by Prentice Hall Management Information Systems, 10/e Raymond
McLeod and George Schell 21 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. 22. 2007 by Prentice Hall
Management Information Systems, 10/e Raymond McLeod and George
Schell 22 Classes of Mathematical Models Static model doesnt
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. 2007 by Prentice Hall Management Information
Systems, 10/e Raymond McLeod and George Schell 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. 24. 2007 by Prentice Hall Management
Information Systems, 10/e Raymond McLeod and George Schell 24
Figure 11.5 Scenario Data & Decision Variables from a
Simulation 25. 2007 by Prentice Hall Management Information
Systems, 10/e Raymond McLeod and George Schell 25 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. 26.
2007 by Prentice Hall Management Information Systems, 10/e Raymond
McLeod and George Schell 26 A Modeling Example A firms 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.
27. 2007 by Prentice Hall Management Information Systems, 10/e
Raymond McLeod and George Schell 27 Figure 11.6 Model Input Screen
for Entering Scenario Data for Prior 28. 2007 by Prentice Hall
Management Information Systems, 10/e Raymond McLeod and George
Schell 28 Figure 11.7 Model Input Screen for Entering Scenario Data
for Next 29. 2007 by Prentice Hall Management Information Systems,
10/e Raymond McLeod and George Schell 29 Model Output The next
quarters 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. 30. 2007 by
Prentice Hall Management Information Systems, 10/e Raymond McLeod
and George Schell 30 Figure 11.8 Summary Output from the Model 31.
2007 by Prentice Hall Management Information Systems, 10/e Raymond
McLeod and George Schell 31 Figure 11.9 Operating Statement Shows
Nonmonetary Results 32. 2007 by Prentice Hall Management
Information Systems, 10/e Raymond McLeod and George Schell 32
Figure 11.10 Income Statement Shows Nonmonetary Results 33. 2007 by
Prentice Hall Management Information Systems, 10/e Raymond McLeod
and George Schell 33 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. 34. 2007 by
Prentice Hall Management Information Systems, 10/e Raymond McLeod
and George Schell 34 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.
35. 2007 by Prentice Hall Management Information Systems, 10/e
Raymond McLeod and George Schell 35 Figure 11.11 Spreadsheet Rows
& Columns Provide Format for Columnar Report 36. 2007 by
Prentice Hall Management Information Systems, 10/e Raymond McLeod
and George Schell 36 Figure 11.12 Spreadsheet Columns are Excellent
for Time Periods in Dynamic Model 37. 2007 by Prentice Hall
Management Information Systems, 10/e Raymond McLeod and George
Schell 37 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. 2007 by Prentice Hall Management
Information Systems, 10/e Raymond McLeod and George Schell 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. 39. 2007 by Prentice Hall Management Information
Systems, 10/e Raymond McLeod and George Schell 39 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. 40. 2007 by Prentice Hall Management Information Systems,
10/e Raymond McLeod and George Schell 40 Areas of AI (Contd) 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. 41. 2007 by Prentice Hall Management
Information Systems, 10/e Raymond McLeod and George Schell 41
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. 42.
2007 by Prentice Hall Management Information Systems, 10/e Raymond
McLeod and George Schell 42 Expert System Configuration (Contd)
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. 43. 2007 by
Prentice Hall Management Information Systems, 10/e Raymond McLeod
and George Schell 43 Expert System Configuration (Contd) 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. 44. 2007 by Prentice Hall
Management Information Systems, 10/e Raymond McLeod and George
Schell 44 Figure 11.13 Expert System Model 45. 2007 by Prentice
Hall Management Information Systems, 10/e Raymond McLeod and George
Schell 45 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. 46. 2007 by
Prentice Hall Management Information Systems, 10/e Raymond McLeod
and George Schell 46 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. 47. 2007 by Prentice Hall Management
Information Systems, 10/e Raymond McLeod and George Schell 47
Figure 11.14 Group Size & Location Determine DSS Environmental
Settings 48. 2007 by Prentice Hall Management Information Systems,
10/e Raymond McLeod and George Schell 48 GDSS Environmental
Settings (Contd) 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.