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Tonga Institute of Higher Education
IT 245
Management Information Systems
Lecture 10
E-Business Decision Support
E-Business Decision Support
Trends 1 of 2
• New dimensions of competitions added by
E-Business and E-commerce:
– Price, Quality and Features comparison by
customers
– New standards in the speed and quality of
delivery, service and after sales service
• Time available with managers to take
business decisions is shortening.
E-Business Decision Support
Trends 2 of 2
• Downsizing or Flattening of Organizations:
Even an employee at lower level is
required to take important decisions which
were taken at higher levels in the old
economy.
• These factors require an efficient
information support to take business
decisions at all levels.
Levels of Decisions and
Information Characteristics 1 of 3
• Strategic decisions: By Board of Directors,
CEO and top Executives. About Overall
organizational goals, strategies, policies,
objectives of the company. Strategic
Planning & control.
• Characteristics: Unstructured Decisions.
Information required is ad hoc,
summarized, Forward looking, external.
Levels of Decisions and
Information Characteristics 2 of 3
• Tactical Management Decisions: Middlelevel management. Short-and mid-term
plans, budgets. Specifying policies,
procedures. Involves allocation of
resources and fixing of responsibilities for
execution of plans.
• Characteristics:Scheduled, detailed,
historical, internal, narrow focus.
Levels of Decisions and
Information Characteristics 3 of 3
• Operational Management Decisions:
Developing short range plans like
production and delivery schedules, Day-today operations and solving problems and
bottlenecks in daily activities.
• Characteristics: Routine feedback,
detailed, historical, internal with narrow
focus.
Types of Information Systems
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Management Information Systems (MIS)
On Line Analytical Processing (OLAP)
Decision Support Systems (DSS)
Geographic Information Systems (GIS)
and Data Visualization Systems (DVS)
MIS
• Predefined Info Products
for day-to-day decisionmaking.
• For Operational & Tactical
Managers.
• Structured Decisions
• Reports : Periodic,
Exception,Demand, Push
Vs.
DSS
• Interactive Info support
• Initiated, controlled by
decision maker
• tailored to suit personal
decision style
• Rely on Model bases +
databases, LP , Multiple
regression forecasting,
PV Models
Tools and Techniques in DSS
1 of 2
• What if analysis: Changing variables and
observing change on other
values:Mortgage Loans: Amt, Int rate,
period etc.
• Sensitivity Analysis: Value of one variable
is changes repeatedly and change on
other variables is observed.
• Goal-Seeking Analysis : Change in
different variables until a goal is achieved.
Tools and Techniques in DSS
2 of 2
• Optimization Analysis : More complex
extension of goal-seeking analysis.Finding
optimum values for one or more variables
within some given constraints.
• Data Mining : Discovering knowledge from
an ocean of information. Discovering
patterns, trends and correlations hidden in
the data to get strategic business
advantage.
On Line Analytical Processing
(OLAP)
• Provide fast answers to complex business
queries.
• Managers can interactively manipulate large
amounts of detailed data from many
perspectives.
• Involves analyzing complex relationships among
vast data to discover patterns, trends
• Analytical Operations: Consolidation, Drill Down,
Slicing & Dicing
Executive Information Systems
(EIS)
• Combines features of MIS and DSS
• Focus: Meet strategic info needs of top
management
• Info about firm’s critical success factors.
• Report formats are tailored to suit executives’
preferences.
• Extensive use of graphical user interface and
graphic displays.
• Exception reporting, Drilled down info.
Enterprise Info or Knowledge
Portals
• Portals for every one in the company.
• Knowledge sharing
• Critical info support to staff all over the
world ( Refer to Real World Case 1)
• Knowledge Management (KM): Creating,
Sharing and disseminating knowledge in
support of Business Decision Making.
Artificial Intelligence
Technologies in Business
Section II
Artificial Intelligence (AI)
• A field of science and Technology based
on disciplines such as computer science,
biology, psychology, linguistics,
mathematics and engineering.
• Tries to develop computer functions
normally associated with human
intelligence such as reasoning, learning
and problem solving.
Major Application Areas of AI
1 of 3
• Cognitive Science Applications :
– Expert systems
– Learning Systems
– Fuzzy Logic
– Genetic Algorithms
– Neural networks
– Intelligent Agents
Major Application Areas of AI
2 of 3
• Robotics Applications
– Visual perceptions
– Tactility
– Dexterity
– Locomotion
– Navigation
Major Application Areas of AI
3 of 3
• Natural Interface Application
– Natural languages Interface
– Speech recognition
– Multisensory Interfaces
– Virtual Reality
Components of Expert Systems
• Knowledge base
• Software Resources: Inference Engine
and User Interface Programs
• Refer to Fig 6.29 on page 232
Expert Systems Applications
• Loan Portfolio Analysis
• Investment Decisions
• Insurance
Underwriting (Risk
Analysis)
• Medical Diagnosis
• Machine Control
• Inventory Control
• Chemical Testing etc
etc.
Benefits of Expert Systems
• Captures and combines expertise of human
expert/s in a computer-based information
system.
• Faster and more consistent
• Does not suffer from physical fatigue or stress.
• Helps preserve/reproduce knowledge of experts
before they leave or retire or die.
• Improve business efficiency. Competitive
advantage.
Limitations of Expert Systems
•
•
•
•
Limited focus
Inability to learn
Prohibitive development/maintenance Costs
Can solve specific problems in a limited domain
of knowledge.
• Fail miserably in solving problems requiring
broad knowledge base and subjective problem
solving (e.g. Assessment of Political Situation)
Summary
• Decision Support in E-Business
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E-Business Decision Support
MIS, OAP, DSS,
Using Decision Support System
Executive Information System
Enterprise Portals & Decision Support
• Artificial Intelligence Technologies in
Business