Transcript Power Point Slides of Chapter 8

Chapter 8
Decision Support Systems
Managers and Decision
Making
A decision refers to a choice made between alternatives.
 Why Managers Need the Support of Information
Technology. It is very difficult to make good
decisions without valid, timely and relevant
information.
 Number of alternatives to be considered is increasing
 Many decisions are made under time pressure.
 Due to uncertainty in the decision environment, it is
frequently necessary to conduct a sophisticated analysis.
 It is often necessary to rapidly access remote information.
Can we make better decisions?
Decision Process
Decision makers go through a fairly systematic process.
Define
the
“Process or Problem”
Develop
Alternative
Courses of Action
Select
The “Best”
One
Review It
Act on it
The nature of decisions
 Information systems can support
decision-making levels. These include
the three levels of management
activity.
 Strategic management, tactical
management, and operational
management.
Strategic management
 A board of directors and an executive
committee of the CEO develop longrange planning.
 Decisions made at the strategic level
tend to be unstructured.
Tactical management
 Mid-level mangers deal with middle
level management activities such as
short-term planning, medium range
plans and control.
 Decisions made at the tactical
management level tend to be semistructured.
Operational management
 Operating managers deal with day-today operations of an organization,
such as assigning employees to tasks,
or placing or purchase an order.
 Decisions made at the operational
management level tend to be more
structured.
Structured decisions
 Structured decisions are repetitive
and routine problems for which
standard solutions exist.
 Ex: finding an appropriate inventory
level, finding an optimal investment
strategy.
 MIS primarily analyzes structured
problems.
Semi-structured decisions
 Semi-structured problems fall between
structured and unstructured problems.
 Only some of the phases are structured in
semi-structured problems.
 It requires a combination of standard
procedures and individual judgment.
 Ex: annual evaluation of employees, trading
bonds, setting marketing budgets for
consumer products.
Unstructured decisions
 Unstructured problems are novel and nonroutine, complex.
 For unstructured problems we cannot
specify some procedures to make a
decision.
 Ex: expanding the business, moving
operations to foreign countries.
 IS must provide a wide range of
information products to support these types
of decisions at all levels of the organization.
Decision Complexity
Decision making ranges from simple to very complex decisions that
fall along a continuum that ranges from structured to unstructured.
Structured processes refer to routine & repetitive problems with
standard solutions. While Unstructured are "fuzzy," complex problems
with no clear-cut solutions.
Obj ect i ve
St r at egi c
Pr obl em
Co mpl ex
Impo r t an t
Semi
st r uct ur ed
Tact i cal
Oper at i on al
Un st r uct ur ed
In f or mat i on
Repet i t i ve
St r uct ur ed
Oper at i on
Mul t i
Di men si on al
OLAP
It s b een
d o n e b ef o r e
Day t o Day
Repor t
Management Support Systems
 Management Information Systems
 Decision Support Systems
 Executive Information Systems
Management Information
Systems (MIS)
 MIS primarily provides information on
the firm’s performance to help
managers in monitoring and
controlling the business.
MIS
 Information provided by MIS is used by
decision makers at the operational and
tactical levels of organization.
 They face with more structured and semistructured types of decision situations.
 A typical MIS report might show a
summary of monthly sales for each of the
major sales territories of a company.
MIS
 MIS typically produces fixed, regularly
scheduled reports based on data extracted
and summarized from the organization's
underlying transaction processing systems.
 Sometimes MIS produces exception
reports, highlighting only exceptional
conditions, such as when the sales quotas for
a specific territory fall below an anticipated
level or employees who have exceeded their
spending limit in a dental care plan.
 Traditional MIS produced primarily hard copy
reports.
 Today, these reports might be available
online through an intranet, and more MIS
reports can be generated on demand.
Decision Support Systems
(DSS)
 DSS is a part of special category of information systems
that are designed to enhance managerial decisionmaking.
 Decision support system (DSS) is a computer-based
information system that combines models and data in
an attempt to solve semi-structured and unstructured
problems with user involvement.
 They help managers make more effective decisions by
answering complex questions such as;
 Should a newer, more powerful machine replace two
older pieces of equipment?
 Should your company sell directly to the retail market,
continue to sell through distributors, or both?
 Should your company order parts more frequently and in
smaller lots?
DSS
 DSSs help managers make decisions
that are unique, rapidly changing and
not easily specified in advance.
 Although DSS uses internal
information from TPS and MIS, it also
uses external sources, such as
current stock prices or product prices
of competitors.
DSS
 DSSs combine data and sophisticated
analytical models to support semistructured and unstructured decision
making.
 DSSs help managers better use their
knowledge and help create new
knowledge.
 They are essential components of
knowledge management systems.
DSS Components
 DSS relies on model bases and databases.
 A model (in decision making) is a simplified
representation of reality. Simplified because reality is
too complex to copy exactly and much of the
processes complexity is irrelevant to a specific
problem.
 A DSS model base is a software component that
contains all the models used to develop applications
to run the system.
 DSS uses models to manipulate data.
 Ex: If you have some historic sales data, you can use
many different types of models to create a forecast of
future sales.
DSS Components
 DSS software is a collection of
software tools that are used for data
analysis or a collection of
mathematical and analytical models.
 There can be 3 different types of
modeling software for DSSs:
 statistical models,
 optimization models,
 forecasting models.
Statistical modeling
 Statistical modeling software can be
used to help establish relationships
such as relating product sales to
differences in age, income or other
factors between communities.
 Ex: SPSS.
Optimization models
 Optimization models often using
Linear Programming (LP) determine
the proper mix of products within a
given market to maximize profit.
Forecasting models
 The user of this type of model might
supply a range of historical data to
project future conditions and sales
that might result from those
conditions.
 Companies often use this software to
predict the action of competitors.
Capabilities of DSS
 Using a DSS involves 4 basic types of
analytical modeling activities:
 What-if analysis
 Sensitivity analysis
 Goal-seeking analysis
 Optimization analysis
What-if analysis
 An end user makes predictions and assumptions
regarding the input data, many of which are based
on the assessment of uncertain futures.
 When the model is solved, the results depend on
these assumptions.
 What-if analysis attempts to check the impact of a
change in the assumptions on the proposed
solution.
 Ex: What will happen to the total inventory cost if
the originally assumed cost of carrying inventories
is not 10 percent but 12 percent? Or, what will be
the market share if the initially assumed
advertising budget is overspent by 5 percent?
 In a well designed DSS, managers themselves can
interactively ask the computer these types of
questions as many times as needed.
Sensitivity Analysis
 Investigation of the effect that
changes in one or more parts of a
model have on other parts of the
model.
 Usually we check the impact that
changes in input variables on output
variables.
 It is a special case of what-if
analysis.
Goal-seeking analysis
 Attempts to find the value of the inputs
necessary to achieve a desired level of
outputs.
 Ex: let us say that a DSS solution yielded a
profit of $ 2 million. Management wants to
know that what sales volume and additional
advertising would be necessary to generate
a profit of $2.7 million. This is a goalseeking problem.
Optimization analysis
 Often uses Linear Programming.
 Determines optimal resource
allocation to max or minimize
specified variable such as cost, profit,
revenue, or risk.
 A classic use of optimization analysis
is to determine the proper mix
products within a given market to
maximize profits.
The benefits of DSSs
 Improved decision making through better
understanding of the businesses
 An increased number of decision
alternatives examined
 The ability to implement ad hoc analysis
 Faster response to expended situations
 Improved communication
 More effective teamwork
 Better control
 Time and costs savings
DSS-MIS
DSS
MIS
 Uses data from TPS, MIS, and
external data
Uses data from TPS.
Interactive
Not interactive
 Emphasis on models.
Assumptions, display graphics.
Not fixed format reports.
 Pre-specified, fixed format
reports
 Supports semi-structured and  Addresses structured and
unstructured problems
semi-structured problems
Data Visualization
• Data visualization refers to presentation of
data by technologies such as digital
images, geographical information systems,
graphical user interfaces, multidimensional
tables and graphs, virtual reality, threedimensional presentations, videos and
animation.
 By presenting data in graphical form helps
users see patterns.
 Visualization software packages offer users
capabilities for self-guided exploration and
visual analysis of large amounts of data.
Geographical Information System
(GIS)
 A geographical information system (GIS) is a computerbased system for capturing, storing, checking,
integrating, manipulating, and displaying data using
digitized maps. Every record or digital object has an
identified geographical location. It employs spatially
oriented databases.
 GIS software uses geographic information tying data to
points, lines and areas on a map.
 GIS software simplifies the analysis and visualization of
information about entities whose physical location is
important.
 GIS can be used to support decisions that require
knowledge about the geographic distribution of people or
other resources in scientific research and resource
management.
GIS (Cont’d)
 GIS might be used to help state and
local governments calculate
emergency response times to natural
disasters or to help banks identify the
best locations for installing new
branches or ATM terminals.
 GIS tools have become affordable
even for small businesses and some
can be used on the Web.
GIS Applications
Company
What the application does
PepsiCo, Inc.
Helps select new locations for Taco Bell and Pizza
Hut restaurants by combining demographic data
and traffic patterns.
Sears
Supports planning of truck routes
CellularOne Corp.
Maps company’s entire cellular network to identify
clusters of call disconnects and to dispatch
technicians accordingly.
Toyota, GM
Direct drivers to destinations
Sun Microsystems
Manages leased property in dozens of places
worldwide
Wilkening & Co.
(consulting
services)
Designs optimal sales territories and routes for
clients, reducing travel costs by 15 percent
Executive Information Systems
(EIS)
• An executive information system (EIS), also
known as an executive support system
(ESS), is a technology designed in response
to the specific needs of top-level managers
and executives.
 EIS help managers with unstructured
problems, focusing on the information
needs of Senior management.
 EIS helps senior executives monitor
organizational performance, track activities
of competitors, spot problems, identify
opportunities, and forecast trends.
EIS (cont’d)
 EIS:
 very user friendly
 supported by graphics
 provides the capabilities of exception
reporting (reporting only the results that
deviate from a set standard)
 provides drill down (investigating
information in increasing detail).
EIS (cont’d)
 Contemporary EIS can bring together data
from all parts of the organization and allow
managers to select, access them as needed
using easy-to-use desktop analytical tools
and online data displays.
 It also helps managers to determine the
critical success factors which are critical to
accomplishing an organization's objective.
 Today’s systems try to avoid the problem of
data overload because data can be filtered
and viewed in graphic format.
EIS (cont’d)
 EIS has the ability to drill down,
moving from a piece of summary data
to lower and lower levels of detail.
 Drill down capability provides details
behind any given information.
EIS (cont’d)
 External data including data from Web are
now easily available in many EISs.
 Executives need a variety of external data
from current stock market news to
competitor information, industry trends.
 Through their EIS, many managers have
access to news services, financial market
databases and economic information.
EIS (cont’d)
 EIS includes tools for modeling and analysis.
 With only a min. experience, most managers
can use these tools to create graphic
comparisons of data by time, region,
product, price, and so on.
 EIS provides for ad hoc analysis capabilities,
with which executives can make specific
requests for data analysis. Instead of merely
having access to existing reports, the
executives can do creative analysis on their
own.
EIS (cont’d)
 BENEFITS:
 FLEXIBILITY
 ABILITY TO ANALYZE, COMPARE,
HIGHLIGHT TRENDS
 GRAPHICS HELP EXPLORE SITUATION
 MONITOR PERFORMANCE
 TIMELINESS, AVAILABILITY OF DATA
ALLOWS PROMPT ACTION
Data mining for Decision Support
• Data mining is a tool for analyzing
large amounts of data.
• It derives its name from the
similarities between searching for
valuable business information in a
large database, and mining a
mountain for a vein of valuable ore.
Data mining
 Data mining helps organize information by
analyzing huge quantities of data and
looking for patterns, trends, associations,
exceptions, and changes in data that are
too complicated for normal human
detection.
 Data mining uses a variety of tools, such as
artificial intelligence and statistical and
visualization tools to analyze the data in a
database.
Artificial Intelligence (AI)- (when)
Will computers be smarter than
you?
 Several capabilities are considered to
be the signs of intelligence: learning
and understanding from experience,
responding quickly and successfully to
a new situation, dealing with complex
situations, etc.
Artificial Intelligence
• Artificial intelligence (AI) is concerned
with studying the thought processes of
humans and representing those
processes via machines (computers,
robots, and so on).
• It’s ultimate goal is to build machines
that will mimic human intelligence.
AI
 Why businesses are interested in AI;
 To create a mechanism that is not subject
to human feelings, such as fatigue and
worry.
 To eliminate routine and unsatisfying jobs
held by people.
 To enhance the organization's knowledge
base by generating solutions to specific
problems that are too massive and complex
to be analyzed by human being in a short
period of time.
Major areas of AI
Expert System. It is an attempt to
mimic human experts. It is decisionmaking software that can reach a level
of performance comparable to a human
expert in some specialized and usually
narrow problem area.
E xp e rt S y s te m
N e u ra l N e tw o rk s
R o b o tic s
Artificial neural networks (ANNs)
simulate massive parallel processes
that involve processing elements
interconnected in a network.
N a tu ra l
Language
P ro c e s s in g
C o g n itiv e /L e a rn in g
S c ie n c e
V is u a l & A u d ito ry
P ro c e s s in g
AI
Fuzzy logic deals with uncertainties
by simulating the process of human
reasoning, allowing the computer to
behave less precisely and logically
than conventional computers do.
Major areas of AI
1. Cognitive science: It is based on
research in biology, neurology,
mathematic and many disciplines. It
focuses on researching how the
human brain works and how human
think and learn.
 Its major applications are intelligent
agents, neural networks, and fuzzy
logic.
Intelligent agents
 Intelligent agents represent a new
technology with the potential to become
one of the most important tools of
information technology.
 It is a software surrogate for end user or
process that fulfills a stated need or
activity.
 Wizards is one of its examples. Wizard is a
built-in package capability that watches
users and offers suggestions as they
attempt to perform tasks by themselves.
 Ex: Excel’s or Word’s wizards.
Artificial Neural Networks (ANN)
 It is an architecture that mimics certain data
processing capabilities of human brain.
 It is a computer model which can handle fast retrieval
of large amounts of information and has the ability to
recognize patterns based on experiences.
 It consists of interconnected processing elements,
called neurons.
 It emulates a biological neural network.
 The neurons in an ANN receive information from other
neurons or from external sources, transform the
information, and pass it on to other neurons or as
external outputs.
ANN
 The value of neural network technology
includes its usefulness for pattern
recognition, learning, and the interpretation
of incomplete inputs.
 ANNs have the potential to provide some of
the human characteristics of problem
solving that are difficult to simulate using
the logical, analytical techniques of DSSs.
 ANN can analyze large quantities of data to
discover patterns and characteristics in
situations where the logic or rules are not
known.
ANN
 Ex: Loan applications would be a good example.
 By reviewing many historical cases of applicants’
responses to questionnaires and the granting
decisions (yes or no), the ANN can create “patterns”
or “profiles” of applications that should be approved,
or those that should be denied.
 Let us say, a new application is matched against the
pattern. If it comes close enough, the computer
classifies it as a “yes” or “no”.
 Otherwise it goes to a human to decide.
 Applications can thus be processed more rapidly.
ANN
 Specific business areas that are well-suited to the use of
ANNs are;
 Tax fraud: identifying, enhancing, and finding
irregularities.
 Airline management: seat demand forecasting and crew
scheduling.
 Prediction of consumer behavior on the Internet:
predicting consumer behavior in order to plan ecommerce advertising.
 Stocks, bonds, and commodity selection and trading:
analyzing various investment alternatives, including
pricing of initial public offerings.
 Signature validation: matching against previous
signatures.
 Evaluation of personnel and job candidates: matching
personnel data to job requirements and performance
criteria.
Fuzzy Logic
 It deals with uncertainties by simulating the
process of human reasoning.
 These systems allow computers to behave
less precisely and logically than
conventional computers do.
 The idea behind this approach is that
decision making is not always a matter of
true or false, black and white.
 It often involves gray areas where the
terms approximately, possible, and similar
are more appropriate.
Fuzzy Logic











Ex: The variable “height”.
Most people would agree that if you are above 6 feet, you are
tall.
Similarly, if your height is less than 5 feet, you are short.
But between 6 feet and 5.75 feet, there is less probability that
you will be considered tall.
Similarly, between 5 and 5.25 feet some will consider you short.
Notice that in the area between 5.25 and 5.75 feet you have a
chance for being considered either short or tall.
Fuzzy logic systems can process such data that are ambiguous,
that is fuzzy data, instead of relying only on crisp data, such as
binary (yes/no) choices.
It quickly provides approximate, but acceptable solutions to
problems.
It allows for approximate values and inferences.
Currently there are only a few examples of fuzzy logic
applications in business.
The Japanese trade shares on the Tokyo Stock Exchange using a
stock-trading program based on fuzzy logic rules.
Major Areas of AI (cont’d)
2.Robotics: Robot machines are
electromechanical devices that can be
programmed and reprogrammed to
automate manual tasks.
 In computer aided manufacturing
robotics are used.
Major Areas of AI (cont’d)
3.Natural Interfaces: Virtual reality is an example of natural
interfaces.
 Virtual reality is a computer-simulated reality.
 Human users can experience computer-simulated objects,
spaces, activities as if they actually exist.
 It is interactive, uses computer-generated, threedimensional graphics.
 Ex: CAD.
 Ex: NEC Corporation (Japan) developed a ski simulator,
which is available in amusement centers. It is also used for
training.
Expert Systems
 Expert systems are an attempt to mimic
human experts.
• It is a decision-making software that can
reach a level of performance comparable to
a human expert in some specialized and
usually narrow problem area.
• The idea is simple: expertise is transferred
from an expert or other source of expertise
to the computer.
Expert systems
 It captures the expertise of an expert or group
of experts in a computer-based information
system.
 The necessary expertise is stored electronically
in a knowledge base.
 The computer is programmed so that it can
make inferences.
 During the past few years, the technology of
expert systems have been successfully applied
in thousands of organizations worldwide to
problems ranging from identifying credit card
fraud to medical diagnosis to the analysis of
dust in mines.