1. CHAPTER 11 1 Managerial Support Systems

2. Opening Case 2 BLUE MOUNTAIN RESORTS PUT BUSINESS INTELLIGENCE TO WORK Canada’s third-busiest ski resort, and Ontario’s largest mountain resort, is Blue Mountain Resorts located near Collingwood. More than a million people visit each winter.. The Business Problem Previously, Blue Mountain used a spreadsheet to keep track of financial information, but the system was not keeping up. Further, the information technology (IT) department consisted of only three people, none of whom was responsible for updating and maintaining the old system. The IT department had to wait for each business segment to report its own information because the financial system was not automated..

3. Opening Case Discussion Why is it necessary for businesses to measure and track their performance? How important is data management for business intelligence applications? Explain your answer. What areas of a business can business intelligence software be applied to? What type of business problems can business intelligence software assist with? 3

4. Opening Case What we learned from this case? The Blue Mountain Resorts case illustrates the importance and far- reaching nature of business intelligence (BI) applications. BI applications enable decision makers to quickly ascertain the status of a business enterprise by looking at key performance indicators. Blue Mountain managers needed current, timely, and accurate information that they were not receiving from their old system. Implementing the BI applications produced significant benefits throughout the company, supporting important decisions across Blue Mountain’s lines of business. 4

5. Agenda 11.1 Managers and Decision Making 11.1.1 The manager’s job and decision making 11.1.2 Why managers need IT support 11.1.3 What IT technologies are available to support managers 11.2 Business Intelligence 11.2.1 Definition and concepts 11.2.2 Multidimensional Data Analysis 11.2.3 Data mining 11.2.4 Decision support systems 11.2.5 Digital Dashboards 11.2.6 The management cockpit 5

6. 11.3 Data Visualization Technologies 11.3.1 Geographic information systems 11.3.2 Virtual reality 11.4 Intelligent Systems 11.4.1 Definition 11.4.2 Expert systems 11.4.3 Natural language processing and voice technologies 11.4.4 Neural networks 11.4.5 Fuzzy logic 6

7. CHAPTER OVERVIEW 7

8. LEARNING OBJECTIVES 1. Describe the concepts of management, decision making, and computerized support for decision making.(11.1) 2. Describe business intelligence systems, including multidimensional data analysis, data mining, and decision support systems and digital dashboards.(11.2) 3. Describe data visualization, including geographical information systems and virtual reality.(11.3) 4. Describe artificial intelligence, including expert systems, natural language processing, and neural networks.(11.4) 8

9. 11.1 Managers and Decision Making 11.1.1 The manager’s job and decision making 11.1.2 Why managers need IT support 11.1.3 What IT technologies are available to support managers 9

10. 11.1.1 The manager’s job and decision making Management is a process by which organizational goals are achieved through the use of resources (people, money, energy, materials, space, time). Managers have three basic roles (Mintzberg 1973) ◦ Interpersonal roles: figurehead, leader, liaison ◦ Informational roles: monitor, disseminator, spokesperson ◦ Decisional roles: entrepreneur, disturbance handler, resource allocator, negotiator. 10

11. Decision refers to a choice that individuals and group make among two or more alternatives. Decision making is a systematic process composed of three major phases: intelligence, design and choice (Simon 1977) ◦ Implementation phase was added later. 11

12. 12 Figure 11.1 The process and phases in decision making.

13. 11.1.2 Why managers need IT support The number of alternatives to be considered constantly increases. Decisions must be made under time pressure. Decisions are more complex. Decision makers can be in different locations and as can the information. 13

14. 11.1.3 What IT technologies are available to support managers In addition to discovery, communication and collaboration tools (chapter 5) that provide indirect support to decision making, several other information technologies have been successfully used to support managers. They are collectively referred to as business intelligence (BI) systems and intelligent systems. Business Intelligence (BI) refers to applications and technologies for consolidating, analyzing, and providing access to vast amounts of data to help users make better business and strategic decisions. (Details of BI is discussed in 9.2.1) 14

15. To better understand BI and intelligent systems, it helps if we classify decisions along two major dimensions: 1.Problem structure 2.The nature of the decision 15

16. The first dimension deals with the problem structure, where the decision-making processes fall along the continuum ranging from highly structured to highly unstructured decisions. ◦ Structured ◦ Unstructured ◦ Semi-structured 16

17. Structured problems are routine and repetitive problems for which standard solutions exist. Unstructured problems are fuzzy, complex problems for which there are no cut-and-dried solutions. Semi-structured problems are problems in which only some of the decision process phases are structured. 17

18. The second dimension of decision support deals with the nature of decisions. ◦ Operational control ◦ Management control ◦ Strategic planning 18

19. Operational control involves executing specific tasks efficiently and effectively. Management control involves decisions concerning acquiring and using resources efficiently in accomplishing organizational goals. Strategic planning involves decisions concerning the long range goals and policies for growth and resource allocation. 19

20. 20 Figure 11.2 Decision support framework. Technology is used to support the decisions shown in the column at the far right and in the bottom row.

21. 11.2 Business Intelligence 11.2.1 Definition and concepts 11.2.2 Multidimensional Data Analysis 11.2.3 Data mining 11.2.4 Decision support systems 11.2.5 Digital Dashboards 11.2.6 The management cockpit 21

22. 11.2.1 Definition and concepts Business Intelligence (BI) refers to applications and technologies for consolidating, analyzing, and providing access to vast amounts of data to help users make better business and strategic decisions. Two types of BI Systems: ◦ Those that provide data analysis tools  Multidimensional data analysis (or online analytical processing)  Data mining  Decision support systems ◦ Those that provide information in structured format  Dashboards 22

23. 23 Figure 11.3 How business intelligence works.

24. 11.2.2 Multidimensional Data Analysis Multidimensional analysis provides users with an excellent view of what is happening or what has happened. Allows users to analyze data in such a way that they can quickly answer business questions To accomplish this multidimensional analysis tools allow users to “slice and dice” the data in any desired way. 24

25. 11.2.3 Data mining Searching for valuable business information in a large database or data warehouse Data mining performs two basic operations: ◦ Predicting trends and behaviors ◦ Identifying previously unknown patterns and relationships 25

26. 11.2.4 Decision support systems Decision support systems DSS capabilities ◦ Sensitivity analysis ◦ What-if analysis ◦ Goal-seeking analysis 26

27. Decision support systems (DSSs) are computer-based information systems that combine models and data in an attempt to solve semi-structured and some unstructured problems with extensive user involvement. Sensitivity analysis is the study of the impact that changes in one (or more) parts of a model have on other parts. What-if analysis is the study of the impact of a change in the assumptions (input data) on the proposed solution. Goal-seeking analysis is the study that attempts to find the value of the inputs necessary to achieve a desired level of output. 27

28. 11.2.5 Digital Dashboards 11.2.5 Digital Dashboards Dashboards: ◦ Provide rapid access to timely information. ◦ Provide direct access to management reports. ◦ Are very user-friendly and supported by graphics. 28 Samples of Performance Dashboard

29. 11.2.6 The management cockpit A strategic management room that enables top-level decision makers to pilot their businesses better The environment encourages more efficient management meetings and boosts team performance via effective communication Key performance indicators and information relating to critical success factors are displayed graphically on the walls of the meeting room External information can be easily imported to the room to allow competitive analysis 29

30. 11.3 Data Visualization Technologies 30 Data visualization is the process of presenting data to users in visual formats, thereby making IT applications more attractive and understandable to users. Types of data visualization systems 11.3.1 Geographic information systems 11.3.2 Virtual reality

31. Video: The Power of Visualization ◦ Even though a picture is “worth a thousand words,” we have to be very careful about just what we are seeing. Video: Hans Rosling at the TED Talks Video: Hans Rosling ◦ This is an outstanding 21-minute video that illustrates data visualization 31

32. 11.3.1 Geographic information systems Geographical Information Systems: a computer-based system for capturing, integrating, manipulating, and displaying data using digitized maps. 32 GIS for existing land use Geographic Information System

33. 11.3.2 Virtual reality Virtual Reality: interactive, computer-generated, 3 dimensional graphics delivered to the user via a head-mounted display. 33 Virtual Tour of a museumVirtual Reality manipulation with data glove

34. 34 3D image similar to that produced by CyberWell Halliburton’s CyberWell

35. 35 New York City Police Department Command Center

36. 11.4 Intelligent Systems 11.4.1 Definition 11.4.2 Expert systems 11.4.3 Natural language processing and voice technologies 11.4.4 Neural networks 11.4.5 Fuzzy logic 36

37. 11.4.1 Definition Intelligent systems is a term that describes the various commercial applications of AI. Artificial intelligence (AI) is a subfield of computer science concerned with: ◦ studying the thought processes of humans ◦ recreating those processes via machines, such as computer and robots. 37

38. 11.4.2 Expert systems Expertise refers to the extensive, task-specific knowledge acquired from training, reading and experience. Expert systems (ESs) are computer systems that attempt to mimic human experts by applying expertise in a specific domain. Can support decision makers or completely replace them. 38 Star Trek Voyager’s doctor: a 24 th century expert system

39. The transfer of expertise from an expert to a computer and then to a user involves four activities: ◦ Knowledge acquisition ◦ Knowledge representation ◦ Knowledge inference ◦ Knowledge transfer The Components of Expert Systems ◦ Knowledge base ◦ Inference engine ◦ User interface ◦ Blackboard ◦ Explanation subsystem 39

40. 40 Structure and Process of an Expert System

41. Knowledge acquisition: Knowledge is from experts or from documented sources. Knowledge representation: Acquired knowledge is organized as rules or frames (objective-oriented) and stored electronically in a knowledge base. Knowledge inferencing: Given the necessary expertise stored in the knowledge base, the computer is programmed so that it can make inferences. The reasoning function is performed in a component called the inference engine, which is the brain of ES. Knowledge transfer: The inferenced expertise is transferred to the user in the form of a recommendation. 41

42. Knowledge base contains knowledge necessary for understanding, formulating and solving problems. Inference engine is a computer program that provides a methodology for reasoning and formulating conclusions. User interface enables users to communicate with the computer Blackboard is an area of working memory set aside for the description of a current problem. Explanation subsystem explains its recommendations. 42

43. 11.4.3 Natural language processing and voice technologies Natural language processing (NLP) Natural language understanding / speech (voice) recognition Natural language generation/voice synthesis 43

44. Natural language processing (NLP): Communicating with a computer in English or whatever language you may speak. Natural language understanding/speech (voice) recognition: The ability of a computer to comprehend instructions given in ordinary language, via the keyboard or by voice. Natural language generation/voice synthesis: Technology that enables computers to produce ordinary language, by “ voice ” or on the screen, so that people can understand computers more easily. 44

45. 11.4.4 Neural networks Neural network is a system of programs and data structures that approximates the operation of the human brain. Neural networks are particularly good at recognizing subtle, hidden and newly emerging patterns within complex data as well as interpreting incomplete inputs. 45

46. 11.4.5 Fuzzy logic Fuzzy logic deals with the uncertainties by simulating the process of human reasoning, allowing the computer to behave less precisely and logically than conventional computers do. ◦ Involves decision in gray areas. ◦ Uses creative decision-making processes. 46

47. Closing Case BUSINESS INTELLIGENCE AT DOREL Dorel Industries is the leading marketer of juvenile products and bicycles in North America and it is headquartered in Montreal. Dorel employs 4,600 people in 15 countries across North America and Europe and has annual sales over $2 billion. The company sells its products under a suite of brands, including Cosco, Eddie Bauer, Schwinn, Mongoose, and Maxi-Cosi in over 60 countries worldwide. The Business Problem Until recently the company had struggled with a common problem across the board. The company was generating too much data without timely or consistent access to them. In addition, there was the problem of obtaining consistent financial and non-financial data across subsidiaries, especially those located in different countries. There were too many ad-hoc requests by different people in different formats, which made comparisons by year or by business unit not reliable. 47

48. Closing Case Discussion 1. Describe the various benefits that Dorel is seeing from its business intelligence system. 2. Discuss additional analyses that Dorel managers and analysts could run that would benefit the company and its business units and provide competitive advantage. 3. How could visualization technologies be used to support Dorel’s senior managers’ decision-making process? 4. Identify different ways in which intelligent systems could be used at Dorel. Build a table identifying the type of intelligent system, how it would work, and the benefits for the company. 48

49. Closing Case The Results The implementation of BI has helped Dorel in several ways. It has improved the access to data across the business regardless of where a unit is located and what currency it uses. For example, executives at headquarters can instantly drill down into a business unit figures and identify what is happening. Previously, if sales revenue had dropped in one of the units in Europe, say the French unit, managers had no way of identifying if the problem was due to a sales drop in France, Spain, or Portugal. Now they have access to all this detailed information. Consequently, senior managers at Dorel have gained a much better understanding of the company’s global operations. 49

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