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Module 6 : Decision Support System

Module 6 : Decision Support System. TKF 2263 Komputer Aplikasi II. wan@udm.edu.my. 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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Module 6 : Decision Support System

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  1. Module 6 : Decision Support System TKF 2263 KomputerAplikasi II

  2. wan@udm.edu.my 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. 2 TKF 2263 KomputerAplikasi II

  3. wan@unisza.edu.my 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 3 TKF 2263 KomputerAplikasi II

  4. wan@unisza.edu.my Learning Objectives • Identify how neural networks, fuzzy logic, genetic algorithms, virtual reality, and intelligent agents can be used in business. • Give examples of several ways expert systems can be used in business decision-making situations. 4 TKF 2263 KomputerAplikasi II

  5. wan@unisza.edu.my Information required at different management levels 5 TKF 2263 KomputerAplikasi II

  6. wan@unisza.edu.my Levels of Management Decision Making • Strategic management • Executives develop organizational goals, strategies, policies, and objectives • As part of a strategic planning process • Tactical management • Managers and business professionals in self-directed teams • Develop short- and medium-range plans, schedules and budgets • Specify the policies, procedures and business objectives for their subunits 6 TKF 2263 KomputerAplikasi II

  7. wan@unisza.edu.my Levels of Management Decision Making • Operational management • Managers or members of self-directed teams • Develop short-range plans such as weekly production schedules 7 TKF 2263 KomputerAplikasi II

  8. wan@unisza.edu.my Information Quality • Information products whose characteristics, attributes, or qualities make the information more value • Information has 3 dimensions: • Time • Content • Form 8 TKF 2263 KomputerAplikasi II

  9. wan@unisza.edu.my Attributes of Information Quality 9 TKF 2263 KomputerAplikasi II

  10. wan@unisza.edu.my Decision Structure • Structured – situations where the procedures to follow when a decision is needed can be specified in advance • Unstructured – decision situations where it is not possible to specify in advance most of the decision procedures to follow • Semistructured - decision procedures that can be prespecified, but not enough to lead to a definite recommended decision 10 TKF 2263 KomputerAplikasi II

  11. wan@unisza.edu.my Information Systems to support decisions 11 TKF 2263 KomputerAplikasi II

  12. wan@unisza.edu.my Decision Support Trends • Personalized proactive decision analytics • Web-Based applications • Decisions at lower levels of management and by teams and individuals • Business intelligence applications 12 TKF 2263 KomputerAplikasi II

  13. wan@unisza.edu.my Business Intelligence Applications 13 TKF 2263 KomputerAplikasi II

  14. wan@unisza.edu.my Decision Support Systems (DSS) • Provide interactive information support to managers and business professionals during the decision-making process • Use: • Analytical models • Specialized databases • A decision maker’s own insights and judgments • Interactive computer-based modeling • To support semistructured business decisions 14 TKF 2263 KomputerAplikasi II

  15. wan@unisza.edu.my DSS components 15 TKF 2263 KomputerAplikasi II

  16. wan@unisza.edu.my DSS Model base • Model base • A software component that consists of models used in computational and analytical routines that mathematically express relations among variables • Examples: • Linear programming models, • Multiple regression forecasting models • Capital budgeting present value models 16 TKF 2263 KomputerAplikasi II

  17. wan@unisza.edu.my Using DSS • What-if Analysis • End user makes changes to variables, or relationships among variables, and observes the resulting changes in the values of other variables • Sensitivity Analysis • Value of only one variable is changed repeatedly and the resulting changes in other variables are observed 17 TKF 2263 KomputerAplikasi II

  18. wan@unisza.edu.my Management Information Systems (MIS) • Produces information products that support many of the day-to-day decision-making needs of managers and business professionals • Prespecified reports, displays and responses • Support more structured decisions 18 TKF 2263 KomputerAplikasi II

  19. wan@unisza.edu.my MIS Reporting Alternatives • Periodic Scheduled Reports • Prespecified format on a regular basis • Exception Reports • Reports about exceptional conditions • May be produced regularly or when exception occurs • Demand Reports and Responses • Information available when demanded • Push Reporting • Information pushed to manager 19 TKF 2263 KomputerAplikasi II

  20. wan@unisza.edu.my Online Analytical Processing (OLAP) • Enables mangers and analysts to examine and manipulate large amounts of detailed and consolidated data from many perspectives • Done interactively in real time with rapid response 20 TKF 2263 KomputerAplikasi II

  21. wan@unisza.edu.my OLAP Analytical Operations • Consolidation • Aggregation of data • Drill-down • Display detail data that comprise consolidated data • Slicing and Dicing • Ability to look at the database from different viewpoints 21 TKF 2263 KomputerAplikasi II

  22. wan@unisza.edu.my OLAP Technology 22 TKF 2263 KomputerAplikasi II

  23. wan@unisza.edu.my Geographic Information Systems(GIS) • That uses geographic databases to construct and display maps and other graphics displays • That support decisions affecting the geographic distribution of people and other resources • Often used with Global Position Systems (GPS) devices 23 TKF 2263 KomputerAplikasi II

  24. wan@unisza.edu.my Data Visualization Systems (DVS) • Represents complex data using interactive three-dimensional graphical forms such as charts, graphs, and maps • DVS tools help users to interactively sort, subdivide, combine, and organize data while it is in its graphical form. 24 TKF 2263 KomputerAplikasi II

  25. wan@unisza.edu.my Data Mining • Main purpose is to provide decision support to managers and business professionals through knowledge discovery • Analyzes vast store of historical business data • Tries to discover patterns, trends, and correlations hidden in the data that can help a company improve its business performance • Use regression, decision tree, neural network, cluster analysis, or market basket analysis 25 TKF 2263 KomputerAplikasi II

  26. wan@unisza.edu.my Market Basket Analysis • One of most common data mining for marketing • The purpose is to determine what products customers purchase together with other products 26 TKF 2263 KomputerAplikasi II

  27. wan@unisza.edu.my Executive Information Systems • EIS • Combine many features of MIS and DSS • Provide top executives with immediate and easy access to information • About the factors that are critical to accomplishing an organization’s strategic objectives (Critical success factors) • So popular, expanded to managers, analysts and other knowledge workers 27 TKF 2263 KomputerAplikasi II

  28. wan@unisza.edu.my Features of an EIS • Information presented in forms tailored to the preferences of the executives using the system • Customizable graphical user interfaces • Exception reporting • Trend analysis • Drill down capability 28 TKF 2263 KomputerAplikasi II

  29. wan@unisza.edu.my Enterprise Interface Portals • EIP • Web-based interface • Integration of MIS, DSS, EIS, and other technologies • Gives all intranet users and selected extranet users access • To a variety of internal and external business applications and services • Typically tailored to the user giving them a personalized digital dashboard 29 TKF 2263 KomputerAplikasi II

  30. wan@unisza.edu.my Enterprise Information Portal Components 30 TKF 2263 KomputerAplikasi II

  31. wan@unisza.edu.my Knowledge Management Systems • The use of information technology to help gather, organize, and share business knowledge within an organization • Enterprise Knowledge Portals • EIPs that are the entry to corporate intranets that serve as knowledge management systems 31 TKF 2263 KomputerAplikasi II

  32. wan@unisza.edu.my Enterprise Knowledge Portals 32 TKF 2263 KomputerAplikasi II

  33. wan@unisza.edu.my Artificial Intelligence (AI) • A field of science and technology based on disciplines such as computer science, biology, psychology, linguistics, mathematics, and engineering • Goal is to develop computers that can simulate the ability to think, as well as see, hear, walk, talk, and feel 33 TKF 2263 KomputerAplikasi II

  34. wan@unisza.edu.my 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 • Respond quickly and successfully to new situations • Recognize the relative importance of elements in a situation • Handle ambiguous, incomplete, or erroneous information 34 TKF 2263 KomputerAplikasi II

  35. wan@unisza.edu.my Domains of Artificial Intelligence 35 TKF 2263 KomputerAplikasi II

  36. wan@unisza.edu.my Cognitive Science • Based in biology, neurology, psychology, etc. • Focuses on researching how the human brain works and how humans think and learn 36 TKF 2263 KomputerAplikasi II

  37. wan@unisza.edu.my Robotics • Based in AI, engineering and physiology. • Robot machines with computer intelligence and computer controlled, humanlike physical capabilities. 37 TKF 2263 KomputerAplikasi II

  38. wan@unisza.edu.my Natural Interfaces • Based in linguistics, psychology, computer science, etc. • Includes natural language and speech recognition • Development of multisensory devices that use a variety of body movements to operate computers • Virtual reality • Using multisensory human-computer interfaces that enable human users to experience computer-simulated objects, spaces and “worlds” as if they actually exist 38 TKF 2263 KomputerAplikasi II

  39. wan@unisza.edu.my Expert Systems • ES • A knowledge-based information system (KBIS) that uses its knowledge about a specific, complex application to act as an expert consultant to end users • KBIS is a system that adds a knowledge base to the other components on an IS 39 TKF 2263 KomputerAplikasi II

  40. wan@unisza.edu.my Expert System Components • Knowledge Base • Facts about specific subject area • Heuristics that express the reasoning procedures of an expert (rules of thumb) • Software Resources • Inference engine processes the knowledge and makes inferences to make recommend course of action • User interface programs to communicate with end user • Explanation programs to explain the reasoning process to end user 40 TKF 2263 KomputerAplikasi II

  41. wan@unisza.edu.my Expert System Components 41 TKF 2263 KomputerAplikasi II

  42. wan@unisza.edu.my Methods of Knowledge Representation • Case-Based – knowledge organized in form of cases • Cases: examples of past performance, occurrences and experiences • Frame-Based – knowledge organized in a hierarchy or network of frames • Frames: entities consisting of a complex package of data values 42 TKF 2263 KomputerAplikasi II

  43. wan@unisza.edu.my Methods of Knowledge Representation • Object-Based – knowledge organized in network of objects • Objects: data elements and the methods or processes that act on those data • Rule-Based – knowledge represented in rules and statements of fact • Rules: statements that typically take the form of a premise and a conclusion • Such as, If (condition) then (conclusion) 43 TKF 2263 KomputerAplikasi II

  44. wan@unisza.edu.my Expert System Benefits • Faster and more consistent than an expert • Can have the knowledge of several experts • Does not get tired or distracted by overwork or stress • Helps preserve and reproduce the knowledge of experts 44 TKF 2263 KomputerAplikasi II

  45. wan@unisza.edu.my Expert System Limitations • Limited focus • Inability to learn • Maintenance problems • Developmental costs • Can only solve specific types of problems in a limited domain of knowledge 45 TKF 2263 KomputerAplikasi II

  46. wan@unisza.edu.my Suitability Criteria for Expert Systems • Domain: subject area relatively small and limited to well-defined area • Expertise: solutions require the efforts of an expert • Complexity: solution of the problem is a complex task that requires logical inference processing (not possible in conventional information processing) • Structure: solution process must be able to cope with ill-structured, uncertain, missing and conflicting data • Availability: an expert exists who is articulate and cooperative 46 TKF 2263 KomputerAplikasi II

  47. wan@unisza.edu.my Development Tool • Expert System Shell • Software package consisting of an expert system without its knowledge base • Has inference engine and user interface programs 47 TKF 2263 KomputerAplikasi II

  48. wan@unisza.edu.my Knowledge Engineer • A professional who works with experts to capture the knowledge they possess. • Builds the knowledge base using an iterative, prototyping process. 48 TKF 2263 KomputerAplikasi II

  49. wan@unisza.edu.my Neural Networks • Computing systems modeled after the brain’s mesh-like network of interconnected processing elements, called neurons • Interconnected processors operate in parallel and interact with each other • Allows network to learn from data it processes 49 TKF 2263 KomputerAplikasi II

  50. wan@unisza.edu.my Fuzzy Logic • Method of reasoning that resembles human reasoning • Allows for approximate values and inferences and incomplete or ambiguous data instead of relying only on crisp data • Uses terms such as “very high” rather than precise measures 50 TKF 2263 KomputerAplikasi II

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