e-business decision support -
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E-Business Decision Support
Group members
Anam Ali (03)
Husna Anjum (06)
M Ahsan Nawaz (12)
Abid Hussain (01)
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E-Business Decision Support
Trends 1 of 2 New dimensions of competitions added by
E-Business and E-commerce:
Price, Quality and Features comparison bycustomers
New standards in the speed and quality of
delivery, service and after sales service
Time available with managers to takebusiness decisions is shortening.
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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 whichwere taken at higher levels in the old
economy.
These factors require an efficient
information support to take business
decisions at all levels.
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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.
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Levels of Decisions and
Information Characteristics 2 of 3 Tactical Management Decisions: Middle-
level 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.
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Levels of Decisions and
Information Characteristics 3 of 3 Operational Management Decisions:
Developing short range plans like
production and delivery schedules, Day-to-day operations and solving problems and
bottlenecks in daily activities.
Characteristics: Routine feedback,
detailed, historical, internal with narrowfocus.
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Types of Information Systems
Management Information Systems (MIS)
On Line Analytical Processing (OLAP)
Decision Support Systems (DSS) Geographic Information Systems (GIS)
and Data Visualization Systems (DVS)
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MIS Vs. DSS
Predefined Info Products
for day-to-day decision-
making.
For Operational &
Tactical Managers.
Structured Decisions
Reports : Periodic,
Exception,Demand, Push
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
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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.
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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 inthe data to get strategic business
advantage.
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On Line Analytical Processing
(OLAP)
Provide fast answers to complex business
queries.
Managers can interactively manipulate large
amounts of detailed data from manyperspectives.
Involves analyzing complex relationships among
vast data to discover patterns, trends Analytical Operations: Consolidation, Drill Down,
Slicing & Dicing
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Executive Information Systems
(EIS) Combines features of MIS and DSS
Focus: Meet strategic info needs of top
management
Info about firms critical success factors.
Report formats are tailored to suit executives
preferences.
Extensive use of graphical user interface andgraphic displays.
Exception reporting, Drilled down info.
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Enterprise Info or Knowledge
Portals Portals for every one in the company.
Knowledge sharing
Critical info support to staff all over theworld ( Refer to Real World Case 1)
Knowledge Management (KM): Creating,
Sharing and disseminating knowledge insupport of Business Decision Making.
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Artificial Intelligence
Technologies in Business
Section II
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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, learningand problem solving.
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Major Application Areas of AI
1 of 3 Cognitive Science Applications :
Expert systems
Learning Systems
Fuzzy Logic
Genetic Algorithms
Neural networks
Intelligent Agents
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Major Application Areas of AI
2 of 3 Robotics Applications
Visual perceptions
Tactility Dexterity
Locomotion
Navigation
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Major Application Areas of AI
3 of 3 Natural Interface Application
Natural languages Interface
Speech recognition Multisensory Interfaces
Virtual Reality
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Components of Expert Systems
Knowledge base
Software Resources: Inference Engine
and User Interface Programs Refer to Fig 6.29 on page 232
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Expert Systems Applications Loan Portfolio Analysis
Investment Decisions Insurance
Underwriting (Risk
Analysis)
Medical Diagnosis
Machine Control Inventory Control
Chemical Testing etc
etc.
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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 problemsolving (e.g. Assessment of Political Situation)
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Summary
Decision Support in E-Business E-Business Decision Support
MIS, OAP, DSS,
Using Decision Support System
Executive Information System
Enterprise Portals & Decision Support
Artificial Intelligence Technologies inBusiness
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