This document discusses different types of information systems used at various levels of management: 1. Transaction processing systems are used at the operational level to record daily transactions like sales and orders. Management information systems are used at the middle level and provide periodic summary reports of transactions. 2. Executive support systems are used by senior managers and focus on long term strategic issues affecting the organization over several years. Decision support systems use analytical models and interactive "what if" analysis to support decision making. 3. Expert systems emulate human expertise in a specific domain through use of knowledge bases and inference engines. They are used to diagnose problems and provide consultative advice.

1. Unit-2

2. Levels of Information System
• Transaction Processing System
• Management Information System
• Executive Support Systems
• Decision Support System
• Expert Systems

3. Transaction Processing System
• Basic systems for operational level managers
• Routine and daily transactions
• They are detailed
• Example Sales- item purchased, quantity purchased,
price, customer who purchased
• Example Order placed to supplier, Supplier name, Product, quantity,
delivery time, etc

4. Transaction Processing System
• They are online and current
• Example Online reservation system
• Example-Sale at retail center can be tracked recorded by
scanner as long as its online

5. Management Information System
• These cater to need of middle level managers
• They are periodic
• Processed in batches (month or quarter)
• They are summary of transactions for a period
• Need not be online or current

6. Management Information System
• They are in the form of reports
• Incentive to salesman
• How much sale has he got-should we give him incentive?
• Comparison between periods of time
• Has sales increased or decrease; analyse why?
• Does this product need promotional push?
• Comparison between sales in regions

7. Executive Support System
• Used by senior level of managers
• CEO, Board of Directors
• Involve long term and strategic issues
• Affect the organization for 5-10 years, long periods of time
• If benefit, reap for long period of time

8. Executive Support System
• They are external oriented
• Example-Air India Divestment on hold
• Match with changes in external environment
• Eg: If I am losing market share, is new competitor in market?
• They are graphical to reflect trends (no details, no summary)

9. Decision Support Systems
• Any system that supports a decision
• DSS is an integrated system
• That combines data, models
• And interactive user-friendly software
• Into a single system under user control
• Custom built for specific occasion /application

10. DSS (cont)
• Uses analytical models
• Undertakes ‘what if analysis’
• They are interactive
• Example Impact of reduction in supplier base on
• Price, delivery time, reliability of supplier
• Supplier-10, Per supplier 100 units, Total-1000 units
• Supplier-2, each get 500 units

11. DSS (cont)
• Example How does change in size of package of box
• Affect other products the company produces
• -In terms of shipping
• -In terms of display shelf in the store

12. DSS (cont)
• What if analysis-
• Seeing how changes to one variable affect other variable
• If cut advt by 10%, what would happen to sales
• Sensitivity analysis-
• How repeated changes to a single variable affect other variables
• Cut advt by $100 repeatedly so see its relationship with sales

13. DSS (cont)
• Goal seeking analysis-
• Making repeated changes to selected variables until a chosen variable reaches a
target value
• Let’s try increase in advt until sales reach $ 1 million
• Optimization analysis-
• Finding an optimum value for selected variables, given certain constraints
• What is best amount of advt, given budget & choice of media

14. Expert System

15. Expert System
• Knowledge-based information system
• It uses knowledge about specific application area
• To act as an expert consultant to end users
• Answer questions in a very specific problem area

16. Expert Systems
• It makes human-like inferences about knowledge
• Contained in a specialized knowledge base
• Explain reasoning process & also conclusions to user
• It is form of advice from an expert consultant in a specific area
• Computer programs that emulate human behavior
• Computer programs that mimic human expertise

17. Uses of Expert Systems
• Various Scientific use
• Oil Drilling
• Geological Survey
• Medical Science

18. Examples
• Dendral: To identify structure of chemical compound
• Prospector: To identify sites for drilling or mining
• Knife: Knowledge and Information Fusion Exchange
• Soldiers in US made right military decisions
• Mycin: Large expert from medical science

19. Expert System- Mycin
• Developed in 1970s by Stanford University
• Consultative advice on bacterial infection in blood
• And Meningitis
• Response of physicians interpreted to diagnose the disease
• Experts Systems helped to diagnose disease

20. History of Development
• Expert system are from research in AI
• Provide information on how to analyze problems
• And develop search strategies for its solution

21. History of Development
• Three stages of development
• Natural Language Processing
• Robotics
• Expert systems

22. Stages of Development
• Natural Language Processing
• Design and development of computer programs
• That understand & respond in languages
• Commonly known by humans-COBOL, FORTRAN

23. Stages of Development
• Robotics:
• Visual and tactile programs
• That note changes in the environment and react
• Dull dirty dangerous; complex boring routine
• Example: Japan restaurant
• Stacking boxes in warehouse

24. Stages of Development
• Expert systems:
• Emulate human expertise
• Domain specific knowledge
• Uses reasoning strategy that humans do

25. Types of Expert System
• Assistant: First Level
• Robots, Physical work
• Colleague: Second Level
• Expert systems, Mental work, Works in consultation
• True Expert: Third Level
• Work independently

26. Components of Expert System
• Knowledge Base: Components of knowledge related to specific domain of
expertise
Fact: statements that associate elements of subject domain with truth values-age,
sex, EPS
Procedural Rules: well defined sequence of actions to events in a specific domain
Heuristics: hunch, thumb rules

27. Components (cont)
• Inference Engine:
• Access the knowledge base
• Uses the knowledge stored therein
• Uses search strategies within itself and arrives at a solution
• IE processes the knowledge related to a specific problem
• It then makes association & inferences
• And recommends a course of action

28. Components (cont)
• User Interface:
• Software program needed
• To communicate with user
• Explanation subsystem within it
• Describes the reasoning strategy

29. Features
• Works with Incomplete Information:
Headache-where, frequency, associated symptoms
• Works on Consultation:
Query is made
• Uses an Inference Strategy:
Sequence of steps, events

30. Benefits
• Captures expertise:
• Captures expertise of an expert or a group of experts
• Outperforms:
• Outperforms a single human expert
• Faster and more consistent:
• Faster and more consistent than human expert

31. Benefits
• Knowledge of several experts
• Can have knowledge of several experts
• Tired or distracted or stressed
• Does not get tired or distracted or stressed
• Preserve and reproduce knowledge
• Can preserve and reproduce knowledge of an expert

32. Limitations
• Limited focus
• Inability to learn on its own (unlike human expert)
• Maintenance problem
• High development costs
• Only solves specific types of problems in a limited domain
• Fail when broad knowledge base & subjective problem solving

33. Development of Expert Systems
• Rule Based Knowledge
• Case Based Reasoning
• Forward Chaining
• Backward Chaining

35. Rule Based Knowledge
• Knowledge is represented in form of rules & statement of facts
• It is a set of ‘If’ (condition) and ‘Then’ (conclusions)
• Its like a decision tree
• Series of questions and answers

36. Case Based Reasoning
• Represents knowledge in the form of cases
• Examples of past performance, occurrences & experience
• Similar cases, problems and solutions
• Example-Maruti had a Union problem, we did this, it worked

37. Forward & Backward Chaining
• Forward Chaining is data driven
• Starts with facts and works towards conclusion
• Backward Chaining is goal driven
• Starts with goals and works backwards supporting facts

38. Participants & Languages
• Domain Expert:
• Person whose knowledge is being captured
• Knowledge Engineering:
• Person trained in design, development, implementation & maintenance of expert system
• Knowledge User: Person or group who benefit

39. Languages Used
Languages Used:
• Lisp-List Processing
• Prologue-Programming in Logic

40. Limited Use in Management
• Skeptical Attitude
• Fear of Replacement
• Wait & See Attitude
• Success Stories
• Frequent Updation

41. • End of Slides