Dss es nn fuzzy l vr etc


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Dss es nn fuzzy l vr etc

  1. 1. Decision Support Systems
  2. 2. Decision Support in Business • Companies are investing in data-driven decision support application frameworks to help them respond to – Changing market conditions – Customer needs • This is accomplished by several types of – Management information – Decision support – Other information systems 10-2
  3. 3. Levels of Managerial Decision Making 10-3
  4. 4. Information Quality • Information products made more valuable by their attributes, characteristics, or qualities – Information that is outdated, inaccurate, or hard to understand has much less value • Information has three dimensions – Time – Content – Form 10-4
  5. 5. Attributes of Information Quality 10-5
  6. 6. Decision Structure • Structured (operational) – The procedures to follow when decision is needed can be specified in advance • Unstructured (strategic) – It is not possible to specify in advance most of the decision procedures to follow • Semi-structured (tactical) – Decision procedures can be pre-specified, but not enough to lead to the correct decision 10-6
  7. 7. 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 Prespecified, fixed format Ad hoc, flexible, and adaptable format Information produced by extraction and manipulation of business data Information produced by analytical modeling of business data Information processing methodology 10-7
  8. 8. Decision Support Trends • The emerging class of applications focuses on – Personalized decision support – Modeling – Information retrieval – Data warehousing – What-if scenarios – Reporting 10-8
  9. 9. Business Intelligence Applications 10-9
  10. 10. Decision Support Systems • Decision support systems use the following to support the making of semistructured business decisions – Analytical models – Specialized databases – A decision-maker’s own insights and judgments – An interactive, computer-based modeling process • DSS systems are designed to be ad hoc, quick-response systems that are initiated and controlled by decision makers 10-10
  11. 11. DSS Components 10-11
  12. 12. DSS Model Base • Model Base – A software component that consists of models used in computational and analytical routines that mathematically express relations among variables • Spreadsheet Examples – Linear programming – Multiple regression forecasting – Capital budgeting present value 10-12
  13. 13. Applications of Statistics and Modeling – Supply Chain: simulate and optimize supply chain flows, reduce inventory, reduce stock-outs – Pricing: identify the price that maximizes yield or profit – Product and Service Quality: detect quality problems early in order to minimize them – Research and Development: improve quality, efficacy, and safety of products and services 10-13
  14. 14. 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, display, and responses – Satisfies needs of operational and tactical decision makers who face structured decisions 10-14
  15. 15. Management Reporting Alternatives • Periodic Scheduled Reports – Prespecified format on a regular basis • Exception Reports – Reports about exceptional conditions – May be produced regularly or when an exception occurs • Demand Reports and Responses – Information is available on demand • Push Reporting – Information is pushed to a networked computer 10-15
  16. 16. 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 10-16
  17. 17. Online Analytical Operations • Consolidation – Aggregation of data – Example: data about sales offices rolled up to the district level • Drill-Down – Display underlying detail data – Example: sales figures by individual product • Slicing and Dicing – Viewing database from different viewpoints – Often performed along a time axis 10-17
  18. 18. 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 Systems (GPS) devices 10-18
  19. 19. 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 10-19
  20. 20. 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 • What-If Analysis – Observing how changes to selected variables affect other variables 10-20
  21. 21. Using Decision Support Systems • Sensitivity Analysis – Observing how repeated changes to a single variable affect other variables • Goal-seeking Analysis – Making repeated changes to selected variables until a chosen variable reaches a target value • Optimization Analysis – Finding an optimum value for selected variables, given certain constraints 10-21
  22. 22. Data Mining • Provides 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 10-22
  23. 23. Analysis of Customer Demographics 10-23
  24. 24. Market Basket Analysis • One of the most common uses for data mining – Determines what products customers purchase together with other products • Results affect how companies – – – – – Market products Place merchandise in the store Lay out catalogs and order forms Determine what new products to offer Customize solicitation phone calls 10-24
  25. 25. Executive Information Systems (EIS) – Combines many features of MIS and DSS – Provide top executives with immediate and easy access to information – Identify factors that are critical to accomplishing strategic objectives (critical success factors) – So popular that it has been expanded to managers, analysis, and other knowledge workers 10-25
  26. 26. 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 10-26
  27. 27. Enterprise Information Portals • An EIP is 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 10-27
  28. 28. Enterprise Information Portal Components 10-28
  29. 29. Artificial Intelligence (AI) • AI is a field of science and technology based on – – – – – – Computer science Biology Psychology Linguistics Mathematics Engineering • The goal is to develop computers than can simulate the ability to think – And see, hear, walk, talk, and feel as well 10-29
  30. 30. Attributes of Intelligent Behavior – – – – – – – – Think and reason Use reason to solve problems Learn or understand from experience Acquire and apply knowledge Exhibit creativity and imagination Deal with complex situations Respond quickly and successfully to new situations Recognize the relative importance of elements in a situation – Handle ambiguous, incomplete, or erroneous information 10-30
  31. 31. Domains of Artificial Intelligence 10-31
  32. 32. Cognitive Science • Applications in the cognitive science of AI – – – – – – – Expert systems Knowledge-based systems Adaptive learning systems Fuzzy logic systems Neural networks Genetic algorithm software Intelligent agents • Focuses on how the human brain works and how humans think and learn 10-32
  33. 33. Latest Commercial Applications of AI • Decision Support – Helps capture the why as well as the what of engineered design and decision making • Information Retrieval – Distills tidal waves of information into simple presentations – Natural language technology – Database mining • Virtual Reality – X-ray-like vision enabled by enhanced-reality visualization helps surgeons – Automated animation and haptic interfaces allow users to interact with virtual objects • Robotics – Machine-vision inspections systems – Cutting-edge robotics systems • From micro robots and hands and legs, to cognitive and trainable modular vision systems 10-33
  34. 34. Expert Systems • An Expert System (ES) – A knowledge-based information system – Contain knowledge about a specific, complex application area – Acts as an expert consultant to end users 10-34
  35. 35. Components of an Expert System • Knowledge Base – Facts about a specific subject area – Heuristics that express the reasoning procedures of an expert (rules of thumb) • Software Resources – An inference engine processes the knowledge and recommends a course of action – User interface programs communicate with the end user – Explanation programs explain the reasoning process to the end user 10-35
  36. 36. Components of an Expert System 10-36
  37. 37. Methods of Knowledge Representation • Case-Based – Knowledge organized in the form of cases – Cases are examples of past performance, occurrences, and experiences • Frame-Based – Knowledge organized in a hierarchy or network of frames – A frame is a collection of knowledge about an entity, consisting of a complex package of data values describing its attributes 10-37
  38. 38. Methods of Knowledge Representation • Object-Based – Knowledge represented as a network of objects – An object is a data element that includes both data and the methods or processes that act on those data • Rule-Based – Knowledge represented in the form of rules and statements of fact – Rules are statements that typically take the form of a premise and a conclusion (If, Then) 10-38
  39. 39. Expert System Application Categories • Decision Management – Loan portfolio analysis – Employee performance evaluation – Insurance underwriting • Diagnostic/Troubleshooting – – – – Equipment calibration Help desk operations Medical diagnosis Software debugging • Design/Configuration – Computer option installation – Manufacturability studies – Communications networks 10-39
  40. 40. Expert System Application Categories (cont’d) • Selection/Classification – – – – Material selection Delinquent account identification Information classification Suspect identification • Process Monitoring/Control – – – – Machine control (including robotics) Inventory control Production monitoring Chemical testing 10-40
  41. 41. Benefits of Expert Systems • Captures the expertise of an expert or group of experts 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 10-41
  42. 42. Limitations of Expert Systems • Limited focus • Inability to learn • Maintenance problems • Development cost • Can only solve specific types of problems in a limited domain of knowledge 10-42
  43. 43. Developing Expert Systems • Suitability Criteria for Expert Systems – Domain: the domain or subject area of the problem is small and well-defined – Expertise: a body of knowledge, techniques, and intuition is needed that only a few people possess – Complexity: solving the problem is a complex task that requires logical inference processing – Structure: the 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 10-43
  44. 44. 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 10-44
  45. 45. 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 10-45
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