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Chapter 10

Chapter 10. Decision Support Systems. 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

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Chapter 10

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  1. Chapter10 Decision Support Systems

  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. Learning Objectives • Explain how the following information systems can support the information needs of executives, managers, and business professionals • Executive information systems • Enterprise information portals • Knowledge management systems • Identify how neural networks, fuzzy logic, genetic algorithms, virtual reality, and intelligent agents can be used in business

  4. Learning Objectives • Give examples of several ways expert systems can be used in business decision-making situations

  5. 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

  6. Levels of Managerial Decision Making

  7. 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• Information products made more valuable by their attributes, characteristics, or qualities • Information that is outdated, inaccurate, or hard to understand has much less value • Information has three dimensions • Time • Content • Form

  8. Attributes of Information Quality

  9. 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. Decision Support Systems

  11. Decision Support Trends • The emerging class of applications focuses on • Personalized decision support • Modeling • Information retrieval • Data warehousing • What-if scenarios • Reporting

  12. Business Intelligence Applications

  13. Decision Support Systems • Decision support systems use the following to support the making of semi-structured 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

  14. DSS Components

  15. 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

  16. 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

  17. 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

  18. 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

  19. Example of Push Reporting • Insert Figure 10.10 here

  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. 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

  22. OLAP Configuration • Insert Figure 10.11

  23. 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

  24. 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

  25. DVS Example • Insert Figure 10.14 here

  26. 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

  27. 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

  28. 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

  29. Analysis of Customer Demographics

  30. 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

  31. 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

  32. 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

  33. 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

  34. Dashboard Example

  35. Enterprise Information Portal Components

  36. Enterprise Knowledge Portal

  37. Case 2: Automated Decision Making • Automated decision making has been slow to materialize • Early applications were just solutions looking for problems, contributing little to improved organizational performance • A new generation of AI applications • Easier to create and manage • Decision making triggered without human intervention • Can translate decisions into action quickly, accurately, and efficiently

  38. 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

  39. Attributes of Intelligent Behavior • Some of the 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 or perplexing situations

  40. Attributes of Intelligent Behavior • Attributes of intelligent behavior (continued) • Respond quickly and successfully to new situations • Recognize the relative importance of elements in a situation • Handle ambiguous, incomplete, or erroneous information

  41. Domains of Artificial Intelligence

  42. 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

  43. Robotics • AI, engineering, and physiology are the basic disciplines of robotics • Produces robot machines with computer intelligence and humanlike physical capabilities • This area include applications designed to give robots the powers of • Sight or visual perception • Touch • Dexterity • Locomotion • Navigation

  44. Natural Interfaces • Major thrusts in the area of AI and the development of natural interfaces • Natural languages • Speech recognition • Virtual reality • Involves research and development in • Linguistics • Psychology • Computer science • Other disciplines

  45. 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

  46. Latest Commercial Applications of AI • 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

  47. 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

  48. 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

  49. Components of an Expert System

  50. 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

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