2. 1 Managerial Support Systems MIS 503 Management Information Systems MBA Program

3. 2 Information Requirements by Management Level Strategic Management Tactical Management Operational Management Decisions Information

4. 3 Structured vs. Semi-Structured For each decision you make, the decision will fall into one of the following categories: –Structured Decisions –Unstructured –Semi-Structured

5. 4 Structured Decisions Often called “programmed decisions” because they are routine and there are usually specific policies, procedures, or actions that can be identified to help make the decision –“This is how we usually solve this type of problem”

6. 5 Unstructured Decisions Decision scenarios that often involve new or unique problems and the individual has little or no programmatic or routine procedure for addressing the problem or making a decision

7. 6 Semi-structured Decisions Decision scenarios that have some structured components and some unstructured components.

8. 7 The Role of the Decision Maker Decision makers can be –Individuals –Teams –Groups –Organizations All of these types of decision makers will differ in their knowledge and experience; therefore, there will be differences in how they will react to a given problem scenario

9. 8 The Decision Making Process Regardless of the type of decision maker, all decisions involve the following steps –Intelligence –Design –Choice –Decision –Implementation

10. 9 Strategies for Making Decisions Optimization Satisficing Elimination by Aspects Incrementalism Mixed Scanning Analytic Hierarchy Process

11. 10 Types of Models Deterministic: linear programming and production planning Stochastic: queuing theory and regression analysis Simulation: transportation analysis and production modeling Domain-specific: meteorological models, geologic models, economic models

12. 11 Conceptual Models Formal approaches are not always feasible Most all problem is always completely new Decision makers can therefore recall and combine a variety of past experiences to create a model of the current situation The Garbage can approach to decision making

13. 12 How can IT be used to support decision makers? By supporting various individual and team activities and roles: –Communication and team interaction –The assimilation and filtering of data –Assist with problem recognition –Assist with problem solving –Putting together the results into a cohesive package

14. 13 Types of Managerial Support Systems and Applications Decision Support Systems –Geographic Information Systems (GIS) –Data Mining –Group Support Systems Business Intelligence Systems Knowledge Management Systems Artificial Intelligence Expert Systems Neural Networks Virtual Reality

15. 14 D ECISION S UPPORT S YSTEMS Designed to assist decision makers with unstructured problems Usually interactive Incorporates data and models Data often comes from transaction processing systems or data warehouse Page 212

16. 15 Page 213 Figure 7.1 Decision Support Systems Components D ECISION S UPPORT S YSTEMS Three major components

17. 16 Decision Support Systems (DSS) DSS can be classified as –data-oriented provide tools for the manipulation and analysis of data –model-based generally have some kind of mathematical model of the decision being supported

18. 17 So, how do decision support systems benefit decision makers? Supplements the decision maker Allows improved intelligence, decision, and choice activities Facilitates problem solving Provides assistance with non-structures decisions Assists with knowledge management

19. 18 Spatial DSS: A Geographic Information System A geographic information system (GIS) is a computer-based information system that provides tools to collect, integrate, manage, analyze, model, and display data that is referenced to an accurate cartographic representation of objects in space. (Mennecke, Dangermond, Santoro, Darling, & Crossland, 1995).

20. 19 Location Based Services Location-based services incorporate information about the user's location into the provision of products or services. These include… –Locator services (e.g., where’s the closest ATM?) –Navigation systems (e.g., in the car or on your PC) –M-commerce applications (e.g., proximity alerts, closest service, mobile advertizing)

21. 20 GIS Examples Online: –www.MapQuest.comwww.MapQuest.com –Maps.google.comMaps.google.com Desktop –ArcGIS by ESRIESRI –MS MapPoint 2004MS MapPoint 2004

22. 21 GISs – systems based on manipulation of relationships in space that use geographic data G EOGRAPHIC I NFORMATION S YSTEMS –Early GIS users: Natural resource management Public administration NASA and the military Urban planning Forestry Map makers Page 219

23. 22 Business Adopts Geographic Technologies G EOGRAPHIC I NFORMATION S YSTEMS –Business uses: Determining site locations Market analysis and planning Logistics and routing Environmental engineering Geographic pattern analysis Page 219

24. 23 Figure 7.3 Department Store Analysis Page 219 (Reprinted courtesy of Environmental Systems Research Institute, Inc. Copyright © 2003 Environmental Systems Research Institute, Inc. All rights reserved.)

25. 24 Page 220 Approaches to representing spatial data: –Raster-based – rely on dividing space into small, uniform cells (rasters) in a grid –Vector-based GISs – associate features in the landscape with a point, line, or polygon –Geodatabase model – uses object-oriented data concepts What’s Behind Geographic Technologies G EOGRAPHIC I NFORMATION S YSTEMS

26. 25 Page 221 Figure 7.4 Map Layers in a GIS G EOGRAPHIC I NFORMATION S YSTEMS Coverage model uses different layers to represent similar types of geographic features in the same area

27. 26 Page 221 Questions geographic analysis can answer: –What is adjacent to this feature? –Which site is the nearest one? –What is contained within this area? –Which features does this element cross? –How many features are within a certain distance of a site? What’s Behind Geographic Technologies G EOGRAPHIC I NFORMATION S YSTEMS

28. 27 Page 215 D ATA M INING –Data mining software: Oracle 9i Data Mining and Oracle Data Mining Suite SAS Enterprise Miner IBM Intelligent Miner Modeling Angoss Software’s KnowledgeSEEKER, Knowledge Studio, and KnowledgeExcelerator Datamation’s Data Mining and Business Intelligence Product Data Mining – uses different technologies to search for (mine) “nuggets” of information from data stored in a data warehouse

29. 28 Page 215 –Decision techniques used: Decision trees Linear and logistic regression Clustering for market segmentation Rule induction Nearest neighbor Genetic algorithms D ATA M INING

30. 29 Page 216 see Table 7.1 Uses of Data Mining Uses: –Cross-selling –Customer churn –Customer retention –Direct marketing –Fraud detection –Interactive marketing –Market basket analysis –Market segmentation –Payment or default analysis –Trend analysis D ATA M INING

31. 30 Type of DSS to support a group rather than an individual Specialized type of groupware Attempt to make group meetings more productive Now focus on supporting team in all its endeavors, including “different time, different place” mode – virtual teams Page 217-218 G ROUP S UPPORT S YSTEMS Middle managers spend 35%, and top managers spend 50-80% of time in meetings!

32. 31 G ROUP S UPPORT S YSTEMS Figure 7.2 Group Support System Layout Page 217 Traditional “same time, same place” meeting layout

33. 32 E XECUTIVE I NFORMATION S YSTEMS/ B USINESS I NTELLIGENCE S YSTEMS Page 222-223 Where does EIS data come from? –Filtered and summarized transaction data (internal) –Collected competitive information (internal and external) EISs – a hands-on tool that focuses, filters, and organizes an executive’s information so he or she can make more effective use of it

34. 33 E XECUTIVE I NFORMATION S YSTEMS/ B USINESS I NTELLIGENCE S YSTEMS Page 222-223 Executive information system (EIS): –Delivers online current information about business conditions in aggregate form –Easily accessible to senior executives and other managers –Designed to be used without intermediary assistance –Uses state-of-the-art graphics, communications and data storage methods

35. 34 Page 225 Figure 7.5 Example Geac Performance Management Displays (Courtesy of Geac Computer Corporation Limited. Copyright © 2003 Geac Computer Corporation Limited.)

36. 35 Figure 7.5 Example Geac Performance Management Displays (Courtesy of Geac Computer Corporation Limited. Copyright © 2003 Geac Computer Corporation Limited.) Page 225

37. 36 Artificial Intelligence Artificial intelligence systems include the people, procedures, hardware, software, data and knowledge to develop computer systems and machines that demonstrate characteristics of intelligence.

38. 37 Intelligent Systems Turing’s test for Artificial Intelligence (AI) –place a computer and a human in two separate rooms –an interviewer in a third room, who cannot see the human or the computer user, asks questions that are passed to the computer and to the human –if the interviewer cannot tell the difference between the answers from the computer and the human, the machine is said to exhibit intelligent behavior

39. 38 AI Versus Traditional Programs AI programs manipulate symbols or rules rather than numbers AI programs are generally non- algorithmic often employing heuristics or rules of thumb Many AI programs are concerned with pattern recognition

40. 39 Page 229 Six areas: Natural languages Robotics Perceptive systems Genetic programming Expert systems Neural networks AI – the study of how to make computers do things that are currently done better by people ARTIFICIAL INTELLIGENCE

41. 40 Page 229 Six areas: Natural languages Robotics Perceptive systems Genetic programming Expert systems Neural networks Most relevant for managerial support ARTIFICIAL INTELLIGENCE

42. 41 Page 229 Expert systems – attempt to capture the expertise of humans in a computer program E XPERT S YSTEMS Knowledge engineer: –A specially trained systems analyst who works closely with one or more experts in the area of study –Tries to learn about how experts make decisions –Loads information (what learned) into module called knowledge base

43. 42 Page 229 E XPERT S YSTEMS Figure 7.6 Architecture of an Expert System

44. 43 Page 230 E XPERT S YSTEMS Approaches: –Buy a fully developed system created for a specific application –Develop using a purchased expert system shell (basic framework) and user-friendly special language –Have knowledge engineers custom build using special-purpose language (such as Prolog or Lisp) Obtaining an Expert System

45. 44 Page 230 Standford University’s MYCIN – to diagnose and prescribe treatment for meningitis and blood diseases General Electric’s CATS-1 to diagnose mechanical problems in diesel locomotives AT&T’s ACE to locate faults in telephone cables Market Surveillance software – to detect insider trading FAST software – for credit analysis, used by banking industry Nestle Food’s developed system to provide employees information on pension fund status E XPERT S YSTEMS Examples of Expert Systems

46. 45 Online Expert Systems What’s wrong with your car? http://www.expertise2go.com/webesie/car/ http://www.expertise2go.com/webesie/car/ Buying the right PDA http://www.expertise2go.com/shop/pda.htm http://www.expertise2go.com/shop/pda.htm Choosing a Desktop PC http://www.expertise2go.com/shop/desktop.htm http://www.expertise2go.com/shop/desktop.htm

47. 46 Page 232 Neural networks – attempt to tease out meaningful patterns from vast amounts of data Process: 1.Program given set of data 2.Program analyzed data, works out correlations, selects variables to create patterns 3.Pattern used to predict outcomes, then results compared to known results 4.Program changes pattern by adjusting variable weights or variables themselves 5.Repeats process over and over to adjust pattern 6.When no further adjustment possible, ready to be used to make predictions for future cases N EURAL N ETWORKS

48. 47 Page 232 N EURAL N ETWORKS Table 7.2 Uses of Neural Networks

49. 48 Neural Networks Two Types: –Biological neural networks –Artificial neural networks The most popular type of artificial NN are used to classify input into different categories A neural network has to be first trained by presenting it with past cases –After training the network can be used for classification

50. 49

51. 50 Intelligent Agents An agent is a piece of software that performs a task for its owner –involves AI combined with networks –applications for intelligent agents have been for consumer tasks like shopping and providing recommendations based on profile matches (check out botspot.com)botspot.com

52. 51 Data is turned into information, but the decision maker also needs Knowledge to make decisions Types of knowledge: –Descriptive Knowledge –Procedural Knowledge –Reasoning Knowledge Forms of Knowledge –Tacit Knowledge –Explicit Knowledge

53. 52

54. 53 Examples of technologies that can support or enhance the transformation of knowledge (IBM Systems Journal) Tacit to TacitTacit to Explicit E-meetingsAnswering questions Synchronous collaboration (chat)Annotation Explicit to TacitExplicit to Explicit VisualizationText search Browsable video/audio of presentations Document categorization

55. 54 Knowledge Management Tools Text and Forms management Database and Reporting management Spreadsheet, Solvers and Charts management Programming management. Rules management

56. 55 Page 233 V IRTUAL R EALITY Virtual reality – use of a computer-based system to create an environment that seems real to one or more senses of users Non-entertainment categories: –Training –Design –Marketing

57. 56 Page 234-235 TrainingU.S. Army to train tank crews Amoco for training its drivers Duracell for training factory workers on using new equipment DesignDesign of automobiles Walk-throughs of air conditioning/ furnace units MarketingInteractive 3-D images of products (used on the Web) Virtual tours used by real estate companies or resort hotels V IRTUAL R EALITY

58. 57 Page 235 V IRTUAL R EALITY Figure 7.7 Hometour 360 o Virtual Tour of Living Room (Courtesy of Homestore, Inc. Copyright © 2004 Homestore, Inc.)

59. 58 Page 188-189 Also include transaction processing systems Set of integrated business applications (modules) that carry out common business functions: General ledger, accounts payable, accounts receivable, material requirements planning, order management, inventory control, human resources management Usually purchased from software vendor E NTERPRISE R ESOURCE P LANNING S YSTEMS

60. 59 Page 189 How they differ: 1.ERP modules are integrated 2.ERP modules reflect a particular way of doing business E NTERPRISE R ESOURCE P LANNING S YSTEMS

61. 60 Page 190 Choosing right software and implementation difficult and expensive Requires large investment of money and people resources Leading ERP software vendors: –SAP –PeopleSoft, Inc. (bought J.D. Edwards) –Oracle –Baan E NTERPRISE R ESOURCE P LANNING S YSTEMS

62. 61 And now… what really needs to happen to be an innovator! Entrepreneurship and creativity are really represented by a process! –Identify an Opportunity –Develop a Concept –Determine the Required Resources –Acquire the Necessary Resources –Implement and Manage –Harvest the Venture Source: Morris et al. Entrepreneurship & Innovation

63. 62 Entrepreneurship and Business Models Frameworks Source: Morris et al. Entrepreneurship & Innovation Entrepreneurial Process The Environment The Entrepreneur The ResourcesThe Concept The Organizational Context

64. 63 Entrepreneurship and Business Models How to find opportunities Source: Morris et al. Entrepreneurship & Innovation TypesMethodsSourcesDetractors Perennial  Deliberate  Search vs. Discovery  The Rules Change  Demographics Change  No Need Present  Window is not yet open Occasional  Market Pull vs.  Resource or Capacity Push  Underserved Markets  Social Trends  Strong Loyalties  High Switching Costs Multiple Causes  New customers to the market  Satisfied customers Multiple Effects  Increase in usage rates  Shortages  Easy for others to enter with alternatives  Intense competition  New Knowledge  Customers hard to reach

65. 64 Entrepreneurship and Business Models Types of Innovations –New to the world products or services –New to the market products or services –New product or service line that at least one competitor is offering –Addition to existing products or service lines –Product/service improvement, revision, including addition of new features or options –New application of existing products or services, including application to a new market segment –Repositioning of an existing product or service Source: Morris et al. Entrepreneurship & Innovation

66. 65 Entrepreneurship and Business Models Entry Wedges Source: Morris et al. Entrepreneurship & Innovation

67. 66 What is a Business Model? Six key questions –How do we create value? –For whom do we create value? –What is our source of competence/ advantage? –How do we differentiate ourselves? –How do we make money? –What are our time, scope, and size ambitions?

68. 67 Porter’s Competitive Forces Model: How the Internet Influences Industry Structure