1. Chapter 8
Decision Support Systems

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. 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. 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. 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. Decision Support in Business
Section I
Decision Support in Business

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. 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. Business and Decision Support (continued)

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. 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. 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. Business and Decision Support (continued)

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

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

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. 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. 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. 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. 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

35. 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

36. 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

37. 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

38. 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

39. 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

40. Artificial Intelligence Technologies in Business
Section II
Artificial Intelligence Technologies in Business

41. 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.”

42. 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

43. Artificial Intelligence (continued)
Domains of AI
Three major areas
Cognitive science
Robotics
Natural interfaces

44. 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

45. 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

46. 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

47. 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

48. 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

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

50. 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

51. 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

52. Expert Systems (continued)
Components
Knowledge base
Software resources

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

54. 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

55. 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

56. The Value of Expert Systems

57. 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

58. 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?

59. 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?

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

61. 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?

62. 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.

63. 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?

64. 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?

65. 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

66. 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?

67. 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?

68. 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?

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

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

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

72. 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?

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

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

75. 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?