1. 5 Management Information System Decision Support System Judi Prajetno Sugiono jpsugiono@gmail.com (2008)

2. 2 Learning Objectives  Identify the changes taking place in the form and use of decision support in e- business enterprises.  Identify the role and reporting alternatives of management information systems.

3. 3 Learning Objectives (continued)  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.

4. 4 Learning Objectives (continued)  Explain how the following information systems can support the information needs of executives, managers, and business professionals:  Executive information systems  Enterprise information portals  Enterprise knowledge portals

5. 5 Learning Objectives (continued)  Identify how neural networks, fuzzy logic, genetic algorithms, virtual reality, and intelligent agents can be used in business.  How can expert systems be used in business decision-making situations?

6. 6 Section I  Decision Support in Business

7. 7 Business and Decision Support  To succeed, companies need information systems that can support the diverse information and decision-making needs of their managers and business professionals.

8. 8 Business and Decision Support (continued)  Information, Decisions, & Management  The type of information required by decision makers is directly related to the level of management and the amount of structure in the decision situations.

9. 9 Business and Decision Support (continued)

10. 10 Business and Decision Support (continued)  Information Quality  Timeliness  Provided WHEN it is needed  Up-to-date when it is provided  Provided as often as needed  Provided about past, present, and future time periods as necessary

11. 11 Business and Decision Support (continued)  Information Quality (continued)  Content  Free from errors  Should be related to the information needs of a specific recipient for a specific situation  Provide all the information that is needed  Only the information that is needed should be provided  Can have a broad or narrow scope, or an internal or external focus  Can reveal performance

12. 12 Business and Decision Support (continued)  Information Quality (continued)  Form  Provided in a form that is easy to understand  Can be provided in detail or summary form  Can be arranged in a predetermined sequence  Can be presented in narrative, numeric, graphic, or other forms  Can be provided in hard copy, video, or other media.

13. 13 Business and Decision Support (continued)

14. 14 Business and Decision Support (continued)  Decision Structure  Structured decisions  Involve situations where the procedures to be followed can be specified in advance  Unstructured decisions  Involve situations where it is not possible to specify most of the decision procedures in advance

15. 15 Business and Decision Support (continued)  Decision structure (continued)  Semistructured decisions  Some decision procedures can be specified in advance, but not enough to lead to a definite recommended decision

16. 16 Business and Decision Support (continued)  Amount of structure is typically tied to management level  Operational – more structured  Tactical – more semistructured  Strategic – more unstructured

17. 17 Decision Support Trends  The growth of corporate intranets, extranets and the Web has accelerated the development and use of “executive class” information delivery & decision support software tools to virtually every level of the organization.

18. 18 Management Information Systems  The original type of information system  Produces many of the products that support day- to-day decision-making  These information products typically take the following forms:  Periodic scheduled reports  Exception reports  Demand reports and responses  Push reports

19. 19 Management Information Systems (continued)  Management reporting alternatives  Periodic scheduled reports  Prespecified format  Provided on a scheduled basis  Exception reports  Produced only when exceptional conditions occur  Reduces information overload

20. 20 Management Information Systems (continued)  Management reporting alternatives (continued)  Demand reports and responses  Available when demanded.  Ad hoc  Push reports  Information is sent to a networked PC over the corporate intranet.  Not specifically requested by the recipient

21. 21 Online Analytical Processing  Enables managers and analysts to interactively examine & manipulate large amounts of detailed and consolidated data from many perspectives  Analyze complex relationships to discover patterns, trends, and exception conditions  Real-time

22. 22 Online Analytical Processing (continued)  Involves..  Consolidation  The aggregation of data.  From simple roll-ups to complex groupings of interrelated data  Drill-Down  Display detail data that comprise consolidated data

23. 23 Online Analytical Processing (continued)  Slicing and Dicing  The ability to look at the database from different viewpoints.  When performed along a time axis, helps analyze trends and find patterns

24. 24 Decision Support Systems  Computer-based information systems that provide interactive information support during the decision-making process  DSS’s use  Analytical models  Specialized databases  The decision maker’s insights & judgments  An interactive, computer-based modeling process to support making semistructured and unstructured business decisions

25. 25 Decision Support Systems (continued)  Designed to be ad hoc, quick-response systems that are initiated and controlled by the decision maker  DSS Models and Software  Rely on model bases as well as databases  Might include models and analytical techniques used to express complex relationships

26. 26 Decision Support Systems (continued)  DSS models and software (continued)  Can combine model components to create integrated models in support of specific types of business decisions

27. 27 Decision Support Systems (continued)  Geographic Information & Data Visualization Systems  Special categories of DSS that integrate computer graphics with other DSS features  GIS  A DSS that uses geographic databases to construct and display maps and other graphics displays

28. 28 Decision Support Systems (continued)  Geographic information and data visualization systems (continued)  Data visualization systems  Represent complex data using interactive three- dimensional graphic forms  Helps discover patterns, links, and anomalies

29. 29 Using Decision Support Systems  An interactive modeling process  Four types of analytical modeling  What-if analysis  Sensitivity analysis  Goal-seeking analysis  Optimization analysis

30. 30 Using Decision Support Systems (continued)  What-If Analysis  End user makes changes to variables, or relationships among variables, and observes the resulting changes in the values of other variables

31. 31 Using Decision Support Systems (continued)  Sensitivity Analysis  A special case of what-if analysis  The value of only one variable is changed repeatedly, and the resulting changes on other variables are observed  Typically used when there is uncertainty about the assumptions made in estimating the value of certain key variables

32. 32 Using Decision Support Systems (continued)  Goal-Seeking Analysis  Instead of observing how changes in a variable affect other variables, goal-seeking sets a target value (a goal) for a variable, then repeatedly changes other variables until the target value is achieved

33. 33 Using Decision Support Systems (continued)  Optimization Analysis  A more complex extension of goal-seeking  The goal is to find the optimum value for one or more target variables, given certain constraints

34. 34 Contoh: PT. INDAH GELAS  PT Indah Gelas adalah suatu perusahaan yang memproduksi kaca berkualitas tinggi untuk digunakan sebagai jendela dan pintu kaca. Perusahaan ini memiliki tiga buah 'pabrik, yaitu pabrik 1 yang membuat bingkai aluminium, pabrik 2 yang membuat bingkai kayu, dan pabrik 3 yang digunakan untuk memproduksi kaca dan merakit produk keseluruhan. Saat ini perusahaan mendapat pesanan berupa dua macam produk baru yang potensial, yaitu pintu kaca setinggi 8 kaki dengan bingkai aluminium (produk 1), dan jendela berukuran 4 x 6 kaki dengan bingkai kayu (produk 2). Produk Pabrik Kapasitas yang digunakan per unit ukuran produksi Kapasitas maks 12 1104 20212 33218 Keuntungan35

35. 35  Goal:  Max. Profit: z=3x 1 +5x 2  Criterion:  x 1 <=4  2X 2 <=12  3x 1 +5x 2 <=18  x 1 >=0  X 2 >=0

36. 36 Decision Making Under Risk Plant State of Nature Wet P(W)=0.6 Dry P(D)=0.4 Lettuce10025 Tomatoes8050

37. 37 Decision Tree

38. 38 Expected Value of Imperfect Information  Consider an imperfect source of information with a prediction record as shown in the table.  In the past 100 seasons of which 60 wet (W) and 40 dry (D), the source predicted a total of 46 wet seasons (w) of which 42 were wet (W) and 4 were dry (D). Of the 54 prediction of dry (d), 36 were dry (D) and 18 were wet (W). Actual State WD Predictionw42446 d183654 6040

39. 39

40. 40 Example: Investment  Additional Information  P(O|C)=0.8P(O|L)=0.1O=optimistic  P(P|C)=0.2P(P|L)=0.9P=pessimistic Investment Alternatives Prospectus Good P(C)=0.6 Bad P(L)=0.4 A50-10 B1560 C10010

41. 41

42. 42 Using Decision Support Systems (continued)  Data Mining for Decision Support  Software analyzes vast amounts of data  Attempts to discover patterns, trends, & correlations  May perform regression, decision tree, neural network, cluster detection, or market basket analysis

43. 43 Executive Information Systems  EIS’s combine many of the features of MIS and DSS  Originally intended to provide top executives with immediate, easy access to information about the firm’s “critical success factors”  Alternative names  Enterprise information systems  Executive support systems

44. 44 Executive Information Systems (continued)  Features of an EIS  Information presented in forms tailored to the preferences of the users  Most stress use of graphical user interface and graphics displays  May also include exception reporting and trend analysis

45. 45 Enterprise Portals and Decision Support  A Web-based interface and integration of intranet and other technologies that gives all intranet users and selected extranet users access to a variety of internal & external business applications and services

46. 46 Enterprise Portals and Decision Support (continued)  Business benefits  More specific and selective information  Easy access to key corporate intranet website resources  Industry and business news  Access to company data for stakeholders  Less time spent on unproductive surfing

47. 47 Knowledge Management Systems  IT that helps gather, organize, and share business knowledge within an organization  Hypermedia databases that store and disseminate business knowledge. May also be called knowledge bases  Best practices, policies, business solutions  Entered through the enterprise knowledge portal

48. 48 Section II  Artificial Intelligence Technologies in Business

49. 49 Business and AI  “Designed to leverage the capabilities of humans rather than replace them,…AI technology enables an extraordinary array of applications that forge new connections among people, computers, knowledge, and the physical world.”

50. 50 Artificial Intelligence  A field of science and technology based on disciplines such as computer science, biology, psychology, linguistics, mathematics, & engineering  Goal is to develop computers that can think, see, hear, walk, talk, and feel  Major thrust – development of computer functions normally associated with human intelligence – reasoning, learning, problem solving

51. 51 Artificial Intelligence (continued)  Domains of AI  Three major areas  Cognitive science  Robotics  Natural interfaces

52. 52 Artificial Intelligence (continued)  Cognitive science  Focuses on researching how the human brain works & how humans think and learn  Applications  Expert systems  Adaptive learning systems  Fuzzy logic systems  Neural networks  Intelligent agents

53. 53 Artificial Intelligence (continued)  Robotics  Produces robot machines with computer intelligence and computer controlled, humanlike physical capabilities  Natural interfaces  Natural language and speech recognition  Talking to a computer and having it understand  Virtual reality

54. 54 Neural Networks  Computing systems modeled after the brain’s meshlike network of interconnected processing elements, called neurons  Goal – the neural network learns from data it processes

55. 55 Fuzzy Logic Systems  A method of reasoning that resembles human reasoning  Allows for approximate values and inferences  Allows for incomplete or ambiguous data  Allows “fuzzy” systems to process incomplete data and provide approximate, but acceptable, solutions to problems

56. 56 Genetic Algorithms  Uses Darwinian, randomizing, & other mathematical functions to simulate an evolutionary process that can yield increasingly better solutions  Especially useful for situations in which thousands of solutions are possible & must be evaluated

57. 57 Virtual Reality  Computer-simulated reality  Relies on multisensory input/output devices  Allows interaction with computer-simulated objects, entities, and environments in three dimensions

58. 58 Intelligent Agents  A “software surrogate” for an end user or a process that fulfills a stated need or activity  Uses built-in and learned knowledge base about a person or process to make decisions and accomplish tasks

59. 59 Expert Systems  A knowledge-based information system that uses its knowledge about a specific, complex application area to act as an expert consultant  Provides answers to questions in a very specific problem area  Must be able to explain reasoning process and conclusions to the user

60. 60 Expert Systems (continued)  Components  Knowledge base  Software resources

61. 61 Expert Systems (continued)  Knowledge base  Contains  Facts about a specific subject area  Heuristics that express the reasoning procedures of an expert on the subject

62. 62 Expert Systems (continued)  Software Resources  Contains an inference engine and other programs for refining knowledge and communicating  Inference engine processes the knowledge, and makes associations and inferences  User interface programs, including an explanation program, allows communication with user

63. 63 Developing Expert Systems  Begin with an expert system shell  Add the knowledge base  Built by a “knowledge engineer”  Works with experts to capture their knowledge  Works with domain experts to build the expert system

64. 64 The Value of Expert Systems

65. 65 The Value of Expert Systems (continued)  Benefits  Can outperform a single human expert in many problem situations  Helps preserve and reproduce knowledge of experts  Limitations  Limited focus, inability to learn, maintenance problems, developmental costs

66. 66 Discussion Questions  Is the form and use of information and decision support in e-business changing and expanding?  Has the growth of self-directed teams to manage work in organizations changed the need for strategic, tactical, and operational decision making in business?

67. 67 Discussion Questions (continued)  What is the difference between the ability of a manager to retrieve information instantly on demand using an MIS and the capabilities provided by a DSS?  In what ways does using an electronic spreadsheet package provide you with the capabilities of a decision support system?

68. 68 Discussion Questions (continued)  Are enterprise information portals making executive information systems unnecessary?  Can computers think? Will they EVER be able to?

69. 69 Discussion Questions (continued)  What are some of the most important applications of AI in business?  What are some of the limitations or dangers you see in the use of AI technologies such as expert systems, virtual reality, and intelligent agents? What could be done to minimize such effects?

70. 70 Real World Case 1 – AmeriKing & Others  AmeriKing’s old system  Relied on an antiquated corporate information system.  Involved mailing or faxing paper reports to managers.  AmeriKing’s new system  An intranet-based enterprise information portal  Enables employees to use Web browsers to instantly access financial, marketing, human resource, and other reports.

71. 71 Real World Case 1 (continued)  What is the business value to a company of an enterprise portal like AmeriKing’s?  What are several ways AmeriKing could improve the business value of its portal?

72. 72 Real World Case 1 (continued)  How might an enterprise portal help you as a business professional or manager in your work activities?  Is it becoming necessary for all companies to provide an enterprise information portal to their employees?

73. 73 Real World Case 2 – BAE Systems  Problems  Wasted time trying to find information to do the job.  Duplication of effort  Information overload  Inadequate search capability  Solution  An intranet-based knowledge management system

74. 74 Real World Case 2 (continued)  What problems was BAE having in knowledge sharing? Are such problems common to many companies?  How does BAE’s knowledge management system help solve such problems?

75. 75 Real World Case 2 (continued)  What are some of the business benefits and potential limitations of BAE’s knowledge management system?  What is the difference between a corporate intranet and a knowledge management system? What is the difference in their business value?

76. 76 Real World Case 3 – Cisco Systems, NetFlix, & Office Depot  What are the business benefits and limitations of Cisco’s Web-based system for its channel managers?  Do you agree that NetFlix’s real-time personalization system is critical to their success?

77. 77 Real World Case 3 (continued)  Do you think salespeople will appreciate and benefit from the real-time alert system envisioned for Office Depot?

78. 78 Real World Case 4 – Producers Assistance, Kinko’s, & Champion Printing  Using Spatial Information Systems to…  Find workers  Find services  Find customers

79. 79 Real World Case 4 (continued)  What is the business value of spatial information systems?  How else could spatial information systems be used in business?

80. 80 Real World Case 4 (continued)  How helpful is Kinko’s location finder service? What else can they do to improve this spatial information management application?

81. 81 Real World Case 5 – Schneider National  The business value of business intelligence (BI)  “We were drowning in data but starving for information.”

82. 82 Real World Case 5 (continued)  What problem was Schneider National having with their business data?  How did business intelligence solve the problem?

83. 83 Real World Case 5 (continued)  What are the benefits and limitations of business intelligence software as demonstrated by Schneider National?  What is the business value of business intelligence as defined by Cognos?