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: 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.

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 narrow focus.

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)

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

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• E-Business Decision Support• MIS, OAP, DSS, • Using Decision Support System• Executive Information System• Enterprise Portals & Decision Support

• Artificial Intelligence Technologies in Business