Intro to Information Systems

1,620 views
1,526 views

Published on

Published in: Business, Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
1,620
On SlideShare
0
From Embeds
0
Number of Embeds
2
Actions
Shares
0
Downloads
45
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide
  • Need information systems to support decisions of all types. Decision support systems support the unstructured with unscheduled ad hoc reports Management information systems support the more structured with prespecified reports
  • Example is a Web-enabled marketing DSS. Note the hardware, software, model, data and network resources involved.
  • Unlike an MIS, DSS contains a model base as well as a database
  • Intro to Information Systems

    1. 1. Chapter 9 Decision Support Systems
    2. 2. Learning Objectives <ul><li>Identify the changes taking place in the form and use of decision support in business. </li></ul><ul><li>Identify the role and reporting alternatives of management information systems. </li></ul><ul><li>Describe how online analytical processing can meet key information needs of managers. </li></ul><ul><li>Explain the decision support system concept and how it differs from traditional management information systems. </li></ul>
    3. 3. Learning Objectives <ul><li>Explain how the following information systems can support the information needs of executives, managers, and business professionals: </li></ul><ul><ul><li>Executive information systems </li></ul></ul><ul><ul><li>Enterprise information portals </li></ul></ul><ul><ul><li>Knowledge management systems </li></ul></ul>
    4. 4. Learning Objectives <ul><li>Identify how neural networks, fuzzy logic, genetic algorithms, virtual reality, and intelligent agents can be used in business. </li></ul><ul><li>Give examples of several ways expert systems can be used in business decision-making situations. </li></ul>
    5. 5. Information required at different management levels
    6. 6. Levels of Management Decision Making <ul><li>Strategic management </li></ul><ul><ul><li>Executives develop organizational goals, strategies, policies, and objectives </li></ul></ul><ul><ul><li>As part of a strategic planning process </li></ul></ul><ul><li>Tactical management </li></ul><ul><ul><li>Managers and business professionals in self-directed teams </li></ul></ul><ul><ul><li>Develop short- and medium-range plans, schedules and budgets </li></ul></ul><ul><ul><li>Specify the policies, procedures and business objectives for their subunits </li></ul></ul>
    7. 7. Levels of Management Decision Making <ul><li>Operational management </li></ul><ul><ul><li>Managers or members of self-directed teams </li></ul></ul><ul><ul><li>Develop short-range plans such as weekly production schedules </li></ul></ul>
    8. 8. Decision Structure <ul><li>Structured – situations where the procedures to follow when a decision is needed can be specified in advance </li></ul><ul><li>Unstructured – decision situations where it is not possible to specify in advance most of the decision procedures to follow </li></ul><ul><li>Semistructured - decision procedures that can be prespecified, but not enough to lead to a definite recommended decision </li></ul>
    9. 9. Information Quality <ul><li>Information products whose characteristics, attributes, or qualities make the information more value </li></ul><ul><li>Information has 3 dimensions: </li></ul><ul><ul><li>Time </li></ul></ul><ul><ul><li>Content </li></ul></ul><ul><ul><li>Form </li></ul></ul>
    10. 10. Business Intelligence Applications
    11. 11. Decision Support Systems <ul><li>DSS </li></ul><ul><li>Provide interactive information support to managers and business professionals during the decision-making process </li></ul><ul><li>Use: </li></ul><ul><ul><li>Analytical models </li></ul></ul><ul><ul><li>Specialized databases </li></ul></ul><ul><ul><li>A decision maker’s own insights and judgments </li></ul></ul><ul><ul><li>Interactive computer-based modeling </li></ul></ul><ul><li>To support semistructured business decisions </li></ul>
    12. 12. DSS components
    13. 13. DSS Model base <ul><li>Model base </li></ul><ul><ul><li>A software component that consists of models used in computational and analytical routines that mathematically express relations among variables </li></ul></ul><ul><li>Examples: </li></ul><ul><ul><li>Linear programming models, </li></ul></ul><ul><ul><li>Multiple regression forecasting models </li></ul></ul><ul><ul><li>Capital budgeting present value models </li></ul></ul>
    14. 14. Management Information Systems <ul><li>MIS </li></ul><ul><li>Produces information products that support many of the day-to-day decision-making needs of managers and business professionals </li></ul><ul><li>Prespecified reports, displays and responses </li></ul><ul><li>Support more structured decisions </li></ul>
    15. 15. MIS Reporting Alternatives <ul><li>Periodic Scheduled Reports </li></ul><ul><ul><li>Prespecified format on a regular basis </li></ul></ul><ul><li>Exception Reports </li></ul><ul><ul><li>Reports about exceptional conditions </li></ul></ul><ul><ul><li>May be produced regularly or when exception occurs </li></ul></ul><ul><li>Demand Reports and Responses </li></ul><ul><ul><li>Information available when demanded </li></ul></ul><ul><li>Push Reporting </li></ul><ul><ul><li>Information pushed to manager </li></ul></ul>
    16. 16. Online Analytical Processing <ul><li>OLAP </li></ul><ul><ul><li>Enables mangers and analysts to examine and manipulate large amounts of detailed and consolidated data from many perspectives </li></ul></ul><ul><ul><li>Consolidation </li></ul></ul><ul><ul><ul><li>Aggregation of data </li></ul></ul></ul><ul><ul><li>Drill-down </li></ul></ul><ul><ul><ul><li>Display detail data that comprise consolidated data </li></ul></ul></ul><ul><ul><li>Slicing and Dicing </li></ul></ul><ul><ul><ul><li>Ability to look at the database from different viewpoints </li></ul></ul></ul>
    17. 17. OLAP Technology
    18. 18. Geographic Information Systems <ul><li>GIS </li></ul><ul><ul><li>DSS that uses geographic databases to construct and display maps and other graphics displays </li></ul></ul><ul><ul><li>That support decisions affecting the geographic distribution of people and other resources </li></ul></ul><ul><ul><li>Often used with Global Position Systems (GPS) devices </li></ul></ul>
    19. 19. Data Visualization Systems <ul><li>DVS </li></ul><ul><ul><li>DSS that represents complex data using interactive three-dimensional graphical forms such as charts, graphs, and maps </li></ul></ul><ul><ul><li>DVS tools help users to interactively sort, subdivide, combine, and organize data while it is in its graphical form. </li></ul></ul>
    20. 20. Using DSS <ul><li>What-if Analysis </li></ul><ul><ul><li>End user makes changes to variables, or relationships among variables, and observes the resulting changes in the values of other variables </li></ul></ul><ul><li>Sensitivity Analysis </li></ul><ul><ul><li>Value of only one variable is changed repeatedly and the resulting changes in other variables are observed </li></ul></ul>
    21. 21. Using DSS <ul><li>Goal-Seeking </li></ul><ul><ul><li>Set a target value for a variable and then repeatedly change other variables until the target value is achieved </li></ul></ul><ul><ul><li>How can analysis </li></ul></ul><ul><li>Optimization </li></ul><ul><ul><li>Goal is to find the optimum value for one or more target variables given certain constraints </li></ul></ul><ul><ul><li>One or more other variables are changed repeatedly until the best values for the target variables are discovered </li></ul></ul>
    22. 22. Data Mining <ul><li>Main purpose is to provide decision support to managers and business professionals through knowledge discovery </li></ul><ul><li>Analyzes vast store of historical business data </li></ul><ul><li>Tries to discover patterns, trends, and correlations hidden in the data that can help a company improve its business performance </li></ul><ul><li>Use regression, decision tree, neural network, cluster analysis, or market basket analysis </li></ul>
    23. 23. Market Basket Analysis <ul><li>One of most common data mining for marketing </li></ul><ul><li>The purpose is to determine what products customers purchase together with other products </li></ul>
    24. 24. Executive Information Systems <ul><li>EIS </li></ul><ul><ul><li>Combine many features of MIS and DSS </li></ul></ul><ul><ul><li>Provide top executives with immediate and easy access to information </li></ul></ul><ul><ul><li>About the factors that are critical to accomplishing an organization’s strategic objectives ( Critical success factors ) </li></ul></ul><ul><ul><li>So popular, expanded to managers, analysts and other knowledge workers </li></ul></ul>
    25. 25. Knowledge Management Systems <ul><li>The use of information technology to help gather, organize, and share business knowledge within an organization </li></ul><ul><li>Enterprise Knowledge Portals </li></ul><ul><ul><li>EIPs that are the entry to corporate intranets that serve as knowledge management systems </li></ul></ul>
    26. 26. Case 2: Harrah’s Entertainment, LendingTree, DeepGreen Financial, and Cisco Systems: <ul><li>The promise of AI of automating decision making has been very slow to materialize. </li></ul><ul><li>The new generation AI applications are easier to create and manage, do not require anyone to identify the problems or to initiate the analysis, decision-making capabilities are embedded into the normal flow of work, and are triggered without human intervention. </li></ul><ul><li>They sense online data or conditions, apply codified knowledge or logic and make decisions with minimal human intervention. </li></ul><ul><li>But they rely on experts and managers to create and maintain rules and monitor the results. </li></ul><ul><li>Also, managers in charge of automated decision systems must develop processes for managing exceptions. </li></ul>
    27. 27. Case Study Questions <ul><li>Why did some previous attempts to use artificial intelligence technologies fail? What key differences of the new AI-based applications versus the old cause the authors to declare that automated decision making is finally coming of age? </li></ul><ul><li>What types of decisions are best suited for automated decision making? Provide several examples of successful applications from the companies in this case to illustrate your answer. </li></ul>
    28. 28. Case Study Questions <ul><li>What role do humans play in automated decision making applications? What are some of the challenges faced by managers where automated decision-making systems are being used? What solutions are needed to meet such challenges? </li></ul>
    29. 29. Artificial Intelligence (AI) <ul><li>A field of science and technology based on disciplines such as computer science, biology, psychology, linguistics, mathematics, and engineering </li></ul><ul><li>Goal is to develop computers that can simulate the ability to think, as well as see, hear, walk, talk, and feel </li></ul>
    30. 30. Attributes of Intelligent Behavior <ul><li>Think and reason </li></ul><ul><li>Use reason to solve problems </li></ul><ul><li>Learn or understand from experience </li></ul><ul><li>Acquire and apply knowledge </li></ul><ul><li>Exhibit creativity and imagination </li></ul><ul><li>Deal with complex or perplexing situations </li></ul><ul><li>Respond quickly and successfully to new situations </li></ul><ul><li>Recognize the relative importance of elements in a situation </li></ul><ul><li>Handle ambiguous, incomplete, or erroneous information </li></ul>
    31. 31. Domains of Artificial Intelligence
    32. 32. Cognitive Science <ul><li>Based in biology, neurology, psychology, etc. </li></ul><ul><li>Focuses on researching how the human brain works and how humans think and learn </li></ul>
    33. 33. Robotics <ul><li>Based in AI, engineering and physiology </li></ul><ul><li>Robot machines with computer intelligence and computer controlled, humanlike physical capabilities </li></ul>
    34. 34. Natural Interfaces <ul><li>Based in linguistics, psychology, computer science, etc. </li></ul><ul><li>Includes natural language and speech recognition </li></ul><ul><li>Development of multisensory devices that use a variety of body movements to operate computers </li></ul><ul><li>Virtual reality </li></ul><ul><ul><li>Using multisensory human-computer interfaces that enable human users to experience computer-simulated objects, spaces and “worlds” as if they actually exist </li></ul></ul>
    35. 35. Expert Systems <ul><li>ES </li></ul><ul><li>A knowledge-based information system (KBIS) that uses its knowledge about a specific, complex application to act as an expert consultant to end users </li></ul><ul><li>KBIS is a system that adds a knowledge base to the other components on an IS </li></ul>
    36. 36. Expert System Components <ul><li>Knowledge Base </li></ul><ul><ul><li>Facts about specific subject area </li></ul></ul><ul><ul><li>Heuristics that express the reasoning procedures of an expert (rules of thumb) </li></ul></ul><ul><li>Software Resources </li></ul><ul><ul><li>Inference engine processes the knowledge and makes inferences to make recommend course of action </li></ul></ul><ul><ul><li>User interface programs to communicate with end user </li></ul></ul><ul><ul><li>Explanation programs to explain the reasoning process to end user </li></ul></ul>
    37. 37. <ul><li>Look at www.20q.net , Eliza or http://www.zabaware.com/webhal/hampy.html for examples of artificial intelligence </li></ul>
    38. 38. Expert System Components
    39. 39. Expert System Benefits <ul><li>Faster and more consistent than an expert </li></ul><ul><li>Can have the knowledge of several experts </li></ul><ul><li>Does not get tired or distracted by overwork or stress </li></ul><ul><li>Helps preserve and reproduce the knowledge of experts </li></ul>
    40. 40. Expert System Limitations <ul><li>Limited focus </li></ul><ul><li>Inability to learn </li></ul><ul><li>Maintenance problems </li></ul><ul><li>Developmental costs </li></ul><ul><li>Can only solve specific types of problems in a limited domain of knowledge </li></ul>
    41. 41. Neural Networks <ul><li>Computing systems modeled after the brain’s mesh-like network of interconnected processing elements, called neurons </li></ul><ul><li>Interconnected processors operate in parallel and interact with each other </li></ul><ul><li>Allows network to learn from data it processes </li></ul>
    42. 42. Fuzzy Logic <ul><li>Method of reasoning that resembles human reasoning </li></ul><ul><li>Allows for approximate values and inferences and incomplete or ambiguous data instead of relying only on crisp data </li></ul><ul><li>Uses terms such as “very high” rather than precise measures </li></ul>
    43. 43. Genetic Algorithms <ul><li>Software that uses </li></ul><ul><ul><li>Darwinian (survival of the fittest), randomizing, and other mathematical functions </li></ul></ul><ul><ul><li>To simulate an evolutionary process that can yield increasingly better solutions to a problem </li></ul></ul>
    44. 44. Virtual Reality (VR) <ul><li>Computer-simulated reality </li></ul><ul><li>Relies on multisensory input/output devices such as </li></ul><ul><ul><li>a tracking headset with video goggles and stereo earphones, </li></ul></ul><ul><ul><li>a data glove or jumpsuit with fiber-optic sensors that track your body movements, and </li></ul></ul><ul><ul><li>a walker that monitors the movement of your feet </li></ul></ul>
    45. 45. Intelligent Agents <ul><li>A software surrogate for an end user or a process that fulfills a stated need or activity </li></ul><ul><li>Uses its built-in and learned knowledge base </li></ul><ul><li>To make decisions and accomplish tasks in a way that fulfills the intentions of a user </li></ul><ul><li>Also called software robots or bots </li></ul>

    ×