Unit - III

1. Unit - III Decision Support System Models and Software

2. Decision Support in Business • To succeed, companies need information systems that can support the diverse information and decision-making needs of their managers and business professionals.

3. Information, Decisions & Management • The type of information required by decision makers is directly related to the level of management and the amount of structure in the decision situations.

4. Levels of Management Decision Making • Strategic management • Executives develop organizational goals, strategies, policies & objectives • As part of a strategic planning process • Tactical management • Managers & business professionals in self-directed teams • Develop short and medium-range plans, schedules & budgets • Specify the policies, procedures & business objectives for their subunits

5. Levels of Management Decision Making • Operational management • Managers or members of self-directed teams • Develop short-range plans such as weekly production schedules

6. Time Timliness Currency Frequency Time Period Form Clarity Detail Order Presentation Media Content Accuracy Relevance Completeness Conciseness Scope Performance • Information Quality • Timeliness • Provided WHEN it is needed • Up-to-date when it is provided • Provided as often as needed • Provided about past, present, and future time periods as necessary

7. Information Quality (continued) • Content • Free from errors • Should be related to the information needs of a specific recipient for a specific situation • Provide all the information that is needed • Only the information that is needed should be provided • Can have a broad or narrow scope, or an internal or external focus • Can reveal performance

8. Information Quality (continued) • Form • Provided in a form that is easy to understand • Can be provided in detail or summary form • Can be arranged in a predetermined sequence • Can be presented in narrative, numeric, graphic, or other forms • Can be provided in hard copy, video, or other media.

9. Levels of Managerial Decision Making

10. Decision Structure • Structured decisions • Involve situations where the procedures to be followed can be specified in advance • Unstructured decisions • Involve situations where it is not possible to specify most of the decision procedures in advance • Semi structured decisions • Some decision procedures can be specified in advance, but not enough to lead to a definite recommended decision

11. Amount of structure is typically tied to management level • Operational – more structured • Tactical – more semi structured • Strategic – more unstructured

12. Decision Support Trends • The growth of corporate intranets, extranets and the Web has accelerated the development and use of “executive class” information delivery & decision support software tools to virtually every level of the organization.

13. Example of Decisions

15. DSS Components

16. Data is retrieved from corporate databases and staged in an OLAP multi-dimensional database Corporate Databases OLAP Server Client PC Multi- dimensional database • Operational DB • Data Marts • Data Warehouse Web-enabled OLAP Software Online Analytical Processing

17. Online Analytical Processing • OLAP - Enables managers and analysts to interactively examine & manipulate large amounts of detailed and consolidated data from many perspectives • Analyze complex relationships to discover patterns, trends, and exception conditions • Real-time

18. Involves.. • Consolidation • The aggregation of data. • From simple roll-ups to complex groupings of interrelated data • Drill-Down • Display detail data that comprise consolidated data • Slicing and Dicing • The ability to look at the database from different viewpoints. • When performed along a time axis, helps analyze trends and find patterns

21. DSS - Computer-based information systems that provide interactive information support during the decision-making process • DSS’s use • Analytical models • Specialized databases • The decision maker’s insights & judgments • An interactive, computer-based modeling process to support making semi structured and unstructured business decisions

22. Designed to be ad hoc, quick-response systems that are initiated and controlled by the decision maker • DSS Models and Software • Rely on model bases as well as databases • Might include models and analytical techniques used to express complex relationships • Can combine model components to create integrated models in support of specific types of business decisions

23. Using Decision Support Systems • An interactive modeling process • Four types of analytical modeling • What-if analysis • Sensitivity analysis • Goal-seeking analysis • Optimization analysis

24. Using Decision Support Systems (continued) • What-If Analysis • End user makes changes to variables, or relationships among variables, and observes the resulting changes in the values of other variables • Example: What if we cut advertising by 10 percent? What would happen to sales?

26. Using Decision Support Systems (continued) Sensitivity Analysis • A special case of what-if analysis • The value of only one variable is changed repeatedly, and the resulting changes on other variables are observed • Typically used when there is uncertainty about the assumptions made in estimating the value of certain key variables • Example: Lets cut advertising by $100 repeatedly, so we can see its relationship to sales.

27. Using Decision Support Systems (continued) • Goal-Seeking Analysis • Instead of observing how changes in a variable affect other variables, goal-seeking sets a target value (a goal) for a variable, then repeatedly changes other variables until the target value is achieved. • Example: Lets try increasing in advertising until sales reach $1Million.

28. Using Decision Support Systems (continued) • Optimization Analysis • A more complex extension of goal-seeking • The goal is to find the optimum value for one or more target variables, given certain constraints. • Example: What’s the best amount of advertising to have, given our budget and choice of media?

29. Using Decision Support Systems (continued) • Data Mining for Decision Support • Software analyzes vast amounts of data • Attempts to discover patterns, trends, & correlations • May perform regression, decision tree, neural network, cluster detection, or market basket analysis

30. Section II Artificial Intelligence Technologies in Business

31. Business and AI • “Designed to leverage the capabilities of humans rather than replace them,…AI technology enables an extraordinary array of applications that forge new connections among people, computers, knowledge, and the physical world.”

32. Artificial Intelligence

33. Artificial Intelligence • A field of science and technology based on disciplines such as computer science, biology, psychology, linguistics, mathematics, & engineering • Goal is to develop computers that can think, see, hear, walk, talk, and feel • Major thrust – development of computer functions normally associated with human intelligence – reasoning, learning, problem solving

34. Artificial Intelligence (continued) • Domains of AI • Three major areas • Cognitive science • Robotics • Natural interfaces

35. Artificial Intelligence (continued) • Cognitive science • Focuses on researching how the human brain works & how humans think and learn • Applications • Expert systems • Adaptive learning systems • Fuzzy logic systems • Neural networks • Intelligent agents

36. Artificial Intelligence (continued) • Robotics • Produces robot machines with computer intelligence and computer controlled, humanlike physical capabilities • Natural interfaces • Natural language and speech recognition • Talking to a computer and having it understand • Virtual reality

38. Neural Networks

39. Neural Networks • Computing systems modeled after the brain’s meshlike network of interconnected processing elements, called neurons • Goal – the neural network learns from data it processes

41. Fuzzy Logic Systems • A method of reasoning that resembles human reasoning • Allows for approximate values and inferences • Allows for incomplete or ambiguous data • Allows “fuzzy” systems to process incomplete data and provide approximate, but acceptable, solutions to problems

43. Genetic Algorithms

44. Genetic Algorithms • Uses Darwinian, randomizing, & other mathematical functions to simulate an evolutionary process that can yield increasingly better solutions • Especially useful for situations in which thousands of solutions are possible & must be evaluated

45. Virtual Reality • Computer-simulated reality • Relies on multisensory input/output devices • Allows interaction with computer-simulated objects, entities, and environments in three dimensions

46. Intelligent Agents • A “software surrogate” for an end user or a process that fulfills a stated need or activity • Uses built-in and learned knowledge base about a person or process to make decisions and accomplish tasks

47. Expert Systems • A knowledge-based information system that uses its knowledge about a specific, complex application area to act as an expert consultant • Provides answers to questions in a very specific problem area • Must be able to explain reasoning process and conclusions to the user

48. Expert Systems (continued) Components • Knowledge base • Contains • Facts about a specific subject area • Heuristics that express the reasoning procedures of an expert on the subject • Software resources • Contains an inference engine and other programs for refining knowledge and communicating • Inference engine processes the knowledge, and makes associations and inferences • User interface programs, including an explanation program, allows communication with user

49. Developing Expert Systems • Begin with an expert system shell • Add the knowledge base • Built by a “knowledge engineer” • Works with experts to capture their knowledge • Works with domain experts to build the expert system

50. The Value of Expert Systems