1. Decision Support Systems Chapter 9 Copyright © 2010 by the McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin

2. 9-2 Learning Objectives Identify the changes taking place in the form and use of decision support in business Identify the role and reporting alternatives of management information systems Describe how online analytical processing can meet key information needs of managers Explain the decision support system concept and how it differs from traditional management information systems

3. 9-3 Learning Objectives Explain how these information systems can support the information needs of executives, managers, and business professionals –Executive information systems –Enterprise information portals –Knowledge management systems

4. 9-4 Learning Objectives Identify how neural networks, fuzzy logic, genetic algorithms, virtual reality, and intelligent agents can be used in business Give examples of ways expert systems can be used in business decision-making situations

5. 9-5 Decision Support in Business Changing marketing conditions Customer needs Companies invest in data-driven decision support application frameworks to help them respond to Management information Decision support Other information systems Accomplished by several types of

6. 9-6 Levels of Managerial Decision Making

7. 9-7 Information Quality Outdated, inaccurate, or hard to understand information has much less value Information products are made more valuable by their attributes, characteristics, or qualities Content Form Time Information has three dimensions

8. 9-8 Attributes of Information Quality

9. 9-9 Decision Structure The procedures to follow when a decision is needed can be specified in advance It is not possible to specify in advance most of the decision procedures to follow Decision procedures can be pre-specified, but not enough to lead to the correct decision Structured (operational) Unstructured (strategic) Semi-structured (tactical)

10. 9-10 Decision Support Systems Management Information Systems Decision Support Systems Decision support provided Provide information about the performance of the organization Provide information and techniques to analyze specific problems Information form and frequency Periodic, exception, demand, and push reports and responses Interactive inquiries and responses Information format Pre-specified, fixed formatAd hoc, flexible, and adaptable format Information processing methodology Information produced by extraction and manipulation of business data Information produced by analytical modeling of business data

11. 9-11 Decision Support Trends Personalized decision support Modeling Information retrieval Data warehousing What-if scenarios Reporting

12. 9-12 Decision Support Trends

13. 9-13 Business Intelligence Applications

14. 9-14 Decision Support Systems To support the making of semi-structured business decisions, DSS uses –Analytical models –Specialized databases –Decision-maker’s own insights and judgments –Interactive, computer-based modeling process DS systems –Ad hoc, quick-response systems –Initiated and controlled by decision makers

15. 9-15 DSS Components

16. 9-16 DSS Model Base Model Base –A software component –Consists of models used in computational and analytical routines –Mathematically expresses relationships among variables Spreadsheet Examples –Linear programming –Multiple regression forecasting –Capital budgeting present value

17. 9-17 Applications of Statistics and Modeling Supply Chain Simulate & optimize supply chain flows, reduce inventory & stock-outs Pricing Identify the price that maximizes yield or profit Product & Service Quality Detect quality problems early in order to minimize them Research & Development Improve quality, efficacy, and safety of products and services

18. 9-18 Management Information Systems The original type of information system that supported managerial decision making Produces information products that support many day-to-day decision-making needs Produces reports, displays, and responses Satisfies needs of operational and tactical decision makers who face structured decisions

19. 9-19 Management Reporting Alternatives Periodic Scheduled Reports Pre-specified format, issued on a regular basis Exception Reports Reports about exceptional conditions, scheduled or on event Demand Reports & Responses Information is available on demand Push Reporting Information is pushed to a networked computer

20. 9-20 Online Analytical Processing OLAP –Enables managers and analysts to examine and manipulate large amounts of detailed and consolidated data from many perspectives –Done interactively, in real time, with rapid response to queries

21. 9-21 Online Analytical Operations Consolidation Aggregation of data Ex: sales office data, rolled up to the district level Drill-Down Display underlying detail data Ex: sales figures by individual product Slicing and Dicing Viewing database from different viewpoints Often performed along a time axis

22. 9-22 Geographic Information Systems (GIS) DSS uses geographic databases to construct and display maps and other graphic displays Supports decisions affecting the geographic distribution of people and other resources Often used with Global Positioning System (GPS) devices

23. 9-23 Data Visualization Systems (DVS) Represents complex data using interactive, three- dimensional graphical forms (charts, graphs, maps) Helps users interactively sort, subdivide, combine, and organize data while it is in its graphical form

24. 9-24 Using Decision Support Systems Using a decision support system involves an interactive analytical modeling process –Decision makers are not demanding pre-specified information –They are exploring possible alternatives

25. 9-25 Using Decision Support Systems Goal-seeking Analysis What-If Analysis Optimization Analysis Basic analytical modeling activities Sensitivity Analysis

26. 9-26 Data Mining Decision support through knowledge discovery –Analyzes vast stores of historical business data –Looks for patterns, trends, and correlations –Goal is to improve business performance Types of analysis –Regression –Decision tree –Neural network –Cluster detection –Market basket analysis

27. 9-27 Analysis of Customer Demographics

28. 9-28 Market Basket Analysis One of the most common uses for data mining –Determines what products customers purchase together with other products Typical applications of MBA –Cross-selling –Product placement –Affinity promotion –Survey analysis –Fraud detection –Customer behavior identification

29. 9-29 Executive Information Systems (EIS) Provides top executives with immediate, easy access to information Identifies factors critical to accomplishing strategic objectives Combines many features of MIS and DSS So popular it was expanded to managers, analysis, and other knowledge workers

30. 9-30 Features of an EIS Information presented in forms tailored to the preferences of the executives using the system –Customizable graphical user interfaces –Exception reports –Trend analysis –Drill down capability

31. 9-31 Web-Based Executive Information System

32. 9-32 Enterprise Information Portals A Web-based interface and integration of MIS, DSS, EIS, and other technologies –Available to all intranet users and select extranet users –Provides access to a variety of internal and external business applications and services –Typically tailored or personalized to the user or groups of users –Often has a digital dashboard –Also called enterprise knowledge portals

33. 9-33 Enterprise Information Portal Components

34. 9-34 Enterprise Knowledge Portal

35. 9-35 Artificial Intelligence (AI) AI is a field of science and technology based on… Engineering Computer science Mathematics Biology Linguistics Psychology

36. 9-36 Artificial Intelligence (AI) Think Feel Talk Walk Hear See Ultimate goal for computers

37. 9-37 Attributes of Intelligent Behavior Learn or understand from experience Think and reason Use reason to solve problems Deal with complex or perplexing situations Acquire and apply knowledge Exhibit creativity and imagination Handle ambiguous, incomplete, erroneous info Respond quickly and successfully to new situations Recognize relative importance of situation elements

38. 9-38 Domains of Artificial Intelligence

39. 9-39 Expert Systems An Expert System (ES) Knowledge-based information system Contains knowledge about a specific, complex application area Acts as an export consultant to end users

40. 9-40 Components of an Expert System

41. 9-41 Methods of Knowledge Representation Case-based Object-based Frame-based Rule-based

42. 9-42 Expert System Application Categories Diagnostic/Troubleshooting Design/Configuration Decision Management Selection/Classification Process Monitoring/Control

43. 9-43 Benefits of Expert Systems Captures expertise of expert(s) in a computer-based information system Faster and more consistent than an expert Can contain knowledge of multiple experts Does not get tired or distracted Cannot be overworked or stressed Helps preserve and reproduce the knowledge of human experts

44. 9-44 Limitations of Expert Systems Major limitations of expert systems –Limited focus –Inability to learn –Maintenance problems –Development and maintenance costs –Can only solve specific types of problems in a limited domain of knowledge

45. 9-45 Developing Expert Systems Suitability Criteria for Expert Systems Domain The domain or subject area of the problem is small and well- defined Expertise Solutions to the problem require the efforts of an expert Expertise Solutions to the problem require the efforts of an expert Complexity Problem solving is complex, and requires logical inference processing Complexity Problem solving is complex, and requires logical inference processing

46. 9-46 Developing Expert Systems Structure… solution process must be able to cope with ill-structured, uncertain, missing, and conflicting data and a changing problem situation Availability… an expert exists who is articulate, cooperative, and supported by the management and end users involved in the development process Suitability Criteria for Expert Systems

47. 9-47 Development Tool Expert System Shell –The easiest way to develop an expert system –A software package consisting of an expert system without its knowledge base –Has an inference engine and user interface programs

48. 9-48 Knowledge Engineering A knowledge engineer –Works with experts to capture the knowledge (facts and rules of thumb) they possess –Builds the knowledge base, and if necessary, the rest of the expert system –Performs a role similar to that of systems analysts in conventional information systems development

49. 9-49 Neural Networks Computing systems modeled after the brain’s mesh-like network of interconnected processing elements (neurons) –Interconnected processors operate in parallel and interact with each other –Allows the network to learn from the data it processes –Recognizes patterns and relationships in data

50. 9-50 Fuzzy Logic Resembles human reasoning Allows approximate values and inferences, and incomplete or ambiguous data Uses terms like “very high” instead of precise measures Allows processing of incomplete data Results in quick, approximate solutions Used in fuzzy process controllers (subway trains, elevators, cars)

51. 9-51 Example of Fuzzy Logic Rules and Query

52. 9-52 Genetic Algorithms Stimulates an evolutionary process, yielding increasingly better solutions Being used to model a variety of scientific, technical, and business processes Especially useful for situations in which thousands of solutions are possible Uses Darwinian, randomizing, and other mathematical functions Genetic algorithm software

53. 9-53 Virtual Reality (VR) Virtual reality is a computer-simulated reality –Fast-growing area of artificial intelligence –Originated from efforts to build natural, realistic, multi-sensory human-computer interfaces –Relies on multi-sensory input/output devices –Creates a three-dimensional world through sight, sound, and touch –Also called telepresence

54. 9-54 Typical VR Applications Computer-aided design Entertainment Employee training Product demonstrations Flight simulation Scientific experimentation Medical diagnostics and treatment Current applications of virtual reality

55. 9-55 Intelligent Agents Software surrogate for an end user or a process that fulfills a stated need or activity Uses built-in and learned knowledge base to make decisions and accomplish tasks in a way that fulfills the intentions of a user Also called software robots or bots

56. 9-56 User Interface Agents Interface Tutors Observe user computer operations, correct user mistakes, provide hints/advice on efficient software use Presentation Agents Show information in a variety of forms/media based on user preferences Network Navigation Agents Discover paths to information, provide ways to view it based on user preferences Role Playing Play what-if games and other roles to help users understand information and make better decisions

57. 9-57 Information Management Agents Search Agents Help users find files and databases, search for information, and suggest and find new types of information products, media, resources Information Brokers Provide commercial services to discover and develop information resources that fit business or personal needs Information Filters Receive, find, filter, discard, save, forward, and notify users about products received or desired, including e-mail, voice mail, and other information media