1. Chapter 10 Decision Support Systems
James A. O'Brien, and George Marakas. Management Information Systems with MISource 2007, 8th ed.  Boston, MA: McGraw-Hill, Inc., 2007.  ISBN:

2. Learning Objectives
Identify the changes taking place in the form and use of decision support in business
Identify the role and reporting alternatives of MIS
Describe how online analytical processing can meet key information needs of managers
Explain the decision support system concept and how it differs from traditional MIS
Explain how the following IS can support the information needs of executives, managers, and business professionals: Executive information systems, Enterprise information portals, and Knowledge management systems
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
Chapter 10 Decision Support Systems

3. Decision Support in Business
Companies are investing in data-driven decision support application frameworks to help them respond to
Changing market conditions
Customer needs
This is accomplished by several types of
Management information
Decision support
Other information systems
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4. Case 1 Dashboards for Executives
Web-based “dashboards”
Displays critical information in graphic form
Assembled from data pulled in real time from corporate software and databases
Managers see changes almost instantaneously
Now available to smaller companies
Potential problems
Pressure on employees
Divisions in the office
Tendency to hoard information
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5. Case Study Questions
What is the attraction of dashboards to CEOs and other executives?
What real business value do they provide to executives?
The case emphasizes that managers of small businesses and many business professionals now rely on dashboards.
What business benefits do dashboards provide to this business audience?
What are several reasons for criticism of the use of dashboards by executives?
Do you agree with any of this criticism?
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6. Levels of Managerial Decision Making
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7. Information Quality
Information products made more valuable by their attributes, characteristics, or qualities
Information that is outdated, inaccurate, or hard to understand has much less value
Information has three dimensions
Time
Content
Form
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8. Attributes of Information Quality
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9. Decision Structure Structured (operational)
The procedures to follow when decision is needed can be specified in advance
Unstructured (strategic)
It is not possible to specify in advance most of the decision procedures to follow
Semi-structured (tactical)
Decision procedures can be pre-specified, but not enough to lead to the correct decision
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10. Decision Support Systems
Management Information Systems
Decision Support Systems
Decision support provided
Provide information about the performance of the organization
Provide information and techniques to analyze specific problems
Information form and frequency
Periodic, exception, demand, and push reports and responses
Interactive inquiries and responses
Information format
Prespecified, fixed format
Ad hoc, flexible, and adaptable format
Information processing methodology
Information produced by extraction and manipulation of business data
Information produced by analytical modeling of business data
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11. Decision Support Trends
The emerging class of applications focuses on
Personalized decision support
Modeling
Information retrieval
Data warehousing
What-if scenarios
Reporting
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12. Business Intelligence Applications
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13. Decision Support Systems
Decision support systems use the following to support the making of semi-structured business decisions
Analytical models
Specialized databases
A decision-maker’s own insights and judgments
An interactive, computer-based modeling process
DSS systems are designed to be ad hoc, quick-response systems that are initiated and controlled by decision makers
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14. DSS Components
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15. DSS Model Base Model Base
A software component that consists of models used in computational and analytical routines that mathematically express relations among variables
Spreadsheet Examples
Linear programming
Multiple regression forecasting
Capital budgeting present value
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16. Applications of Statistics and Modeling
Supply Chain: simulate and optimize supply chain flows, reduce inventory, reduce stock-outs
Pricing: identify the price that maximizes yield or profit
Product and Service Quality: detect quality problems early in order to minimize them
Research and Development: improve quality, efficacy, and safety of products and services
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17. Management Information Systems
The original type of information system that supported managerial decision making
Produces information products that support many day-to-day decision-making needs
Produces reports, display, and responses
Satisfies needs of operational and tactical decision makers who face structured decisions
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18. Management Reporting Alternatives
Periodic Scheduled Reports
Prespecified format on a regular basis
Exception Reports
Reports about exceptional conditions
May be produced regularly or when an exception occurs
Demand Reports and Responses
Information is available on demand
Push Reporting
Information is pushed to a networked computer
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19. Online Analytical Processing
OLAP
Enables managers and analysts to examine and manipulate large amounts of detailed and consolidated data from many perspectives
Done interactively, in real time, with rapid response to queries
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20. Online Analytical Operations
Consolidation
Aggregation of data
Example: data about sales offices rolled up to the district level
Drill-Down
Display underlying detail data
Example: sales figures by individual product
Slicing and Dicing
Viewing database from different viewpoints
Often performed along a time axis
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21. Geographic Information Systems
DSS uses geographic databases to construct and display maps and other graphic displays
Supports decisions affecting the geographic distribution of people and other resources
Often used with Global Positioning Systems (GPS) devices
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22. Data Visualization Systems
Represents complex data using interactive, three-dimensional graphical forms (charts, graphs, maps)
Helps users interactively sort, subdivide, combine, and organize data while it is in its graphical form
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23. Using Decision Support Systems
Using a decision support system involves an interactive analytical modeling process
Decision makers are not demanding pre-specified information
They are exploring possible alternatives
What-If Analysis
Observing how changes to selected variables affect other variables
Sensitivity Analysis
Observing how repeated changes to a single variable affect other variables
Goal-seeking Analysis
Making repeated changes to selected variables until a chosen variable reaches a target value
Optimization Analysis
Finding an optimum value for selected variables, given certain constraints
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24. Data Mining Provides decision support through knowledge discovery
Analyzes vast stores of historical business data
Looks for patterns, trends, and correlations
Goal is to improve business performance
Types of analysis
Regression
Decision tree
Neural network
Cluster detection
Market basket analysis
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25. Analysis of Customer Demographics
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26. Market Basket Analysis
One of the most common uses for data mining
Determines what products customers purchase together with other products
Results affect how companies
Market products
Place merchandise in the store
Lay out catalogs and order forms
Determine what new products to offer
Customize solicitation phone calls
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27. Executive Information Systems
Combines many features of MIS and DSS
Provide top executives with immediate and easy access to information
Identify factors that are critical to accomplishing strategic objectives (critical success factors)
So popular that it has been expanded to managers, analysis, and other knowledge workers
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28. Features of an EIS
Information presented in forms tailored to the preferences of the executives using the system
Customizable graphical user interfaces
Exception reports
Trend analysis
Drill down capability
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29. Enterprise Information Portals
An EIP is a Web-based interface and integration of MIS, DSS, EIS, and other technologies
Available to all intranet users and select extranet users
Provides access to a variety of internal and external business applications and services
Typically tailored or personalized to the user or groups of users
Often has a digital dashboard
Also called enterprise knowledge portals
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30. Dashboard Example
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31. Enterprise Information Portal Components
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32. Enterprise Knowledge Portal
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33. Case 2 Automated Decision Making
Automated decision making has been slow to materialize
Early applications were just solutions looking for problems, contributing little to improved organizational performance
A new generation of AI applications
Easier to create and manage
Decision making triggered without human intervention
Can translate decisions into action quickly, accurately, and efficiently
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34. Case 2 Automated Decision Making
AI is best suited for
Decisions that must be made quickly and frequently, using electronic data
Highly structured decision criteria
High-quality data
Common users of AI
Transportation industry
Hotels
Investment firms and lenders
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35. Case Study Questions
Why did some previous attempts to use artificial intelligence technologies fail?
What key differences of the new AI-based applications versus the old cause the authors to declare that automated decision making is coming of age?
What types of decisions are best suited for automated decision making?
What role do humans plan in automated decision-making applications?
What are some of the challenges faced by managers where automated decision-making systems are being used?
What solutions are needed to meet such challenges?
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36. Artificial Intelligence (AI)
AI is a field of science and technology based on
Computer science
Biology
Psychology
Linguistics
Mathematics
Engineering
The goal is to develop computers than can simulate the ability to think
And see, hear, walk, talk, and feel as well
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37. Attributes of Intelligent Behavior
Some of the 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
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38. Domains of Artificial Intelligence
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39. Cognitive Science Applications in the cognitive science of AI
Expert systems
Knowledge-based systems
Adaptive learning systems
Fuzzy logic systems
Neural networks
Genetic algorithm software
Intelligent agents
Focuses on how the human brain works and how humans think and learn
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40. Robotics
AI, engineering, and physiology are the basic disciplines of robotics
Produces robot machines with computer intelligence and humanlike physical capabilities
This area include applications designed to give robots the powers of
Sight or visual perception
Touch
Dexterity
Locomotion
Navigation
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41. Natural Interfaces
Major thrusts in the area of AI and the development of natural interfaces
Natural languages
Speech recognition
Virtual reality
Involves research and development in
Linguistics
Psychology
Computer science
Other disciplines
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42. Latest Commercial Applications of AI
Decision Support
Helps capture the why as well as the what of engineered design and decision making
Information Retrieval
Distills tidal waves of information into simple presentations
Natural language technology
Database mining
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43. Latest Commercial Applications of AI
Virtual Reality
X-ray-like vision enabled by enhanced-reality visualization helps surgeons
Automated animation and haptic interfaces allow users to interact with virtual objects
Robotics
Machine-vision inspections systems
Cutting-edge robotics systems
From micro robots and hands and legs, to cognitive and trainable modular vision systems
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44. Expert Systems An Expert System (ES)
A knowledge-based information system
Contain knowledge about a specific, complex application area
Acts as an expert consultant to end users
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45. Components of an Expert System
Knowledge Base
Facts about a specific subject area
Heuristics that express the reasoning procedures of an expert (rules of thumb)
Software Resources
An inference engine processes the knowledge and recommends a course of action
User interface programs communicate with the end user
Explanation programs explain the reasoning process to the end user
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46. Components of an Expert System
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47. Methods of Knowledge Representation
Case-Based
Knowledge organized in the form of cases
Cases are examples of past performance, occurrences, and experiences
Frame-Based
Knowledge organized in a hierarchy or network of frames
A frame is a collection of knowledge about an entity, consisting of a complex package of data values describing its attributes
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48. Methods of Knowledge Representation
Object-Based
Knowledge represented as a network of objects
An object is a data element that includes both data and the methods or processes that act on those data
Rule-Based
Knowledge represented in the form of rules and statements of fact
Rules are statements that typically take the form of a premise and a conclusion (If, Then)
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49. Expert System Application Categories
Decision Management
Loan portfolio analysis
Employee performance evaluation
Insurance underwriting
Diagnostic/Troubleshooting
Equipment calibration
Help desk operations
Medical diagnosis
Software debugging
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50. Expert System Application Categories
Design/Configuration
Computer option installation
Manufacturability studies
Communications networks
Selection/Classification
Material selection
Delinquent account identification
Information classification
Suspect identification
Process Monitoring/Control
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51. Expert System Application Categories
Process Monitoring/Control
Machine control (including robotics)
Inventory control
Production monitoring
Chemical testing
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52. Benefits of Expert Systems
Captures the expertise of an expert or group of experts in a computer-based information system
Faster and more consistent than an expert
Can contain knowledge of multiple experts
Does not get tired or distracted
Cannot be overworked or stressed
Helps preserve and reproduce the knowledge of human experts
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53. Limitations of Expert Systems
The major limitations of expert systems
Limited focus
Inability to learn
Maintenance problems
Development cost
Can only solve specific types of problems in a limited domain of knowledge
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54. Developing Expert Systems
Suitability Criteria for Expert Systems
Domain: the domain or subject area of the problem is small and well-defined
Expertise: a body of knowledge, techniques, and intuition is needed that only a few people possess
Complexity: solving the problem is a complex task that requires logical inference processing
Structure: the solution process must be able to cope with ill-structured, uncertain, missing, and conflicting data and a changing problem situation
Availability: an expert exists who is articulate, cooperative, and supported by the management and end users involved in the development process
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55. Development Tool Expert System Shell
The easiest way to develop an expert system
A software package consisting of an expert system without its knowledge base
Has an inference engine and user interface programs
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56. Knowledge Engineering
A knowledge engineer
Works with experts to capture the knowledge (facts and rules of thumb) they possess
Builds the knowledge base, and if necessary, the rest of the expert system
Performs a role similar to that of systems analysts in conventional information systems development
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57. Neural Networks
Computing systems modeled after the brain’s mesh-like network of interconnected processing elements (neurons)
Interconnected processors operate in parallel and interact with each other
Allows the network to learn from the data it processes
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58. Fuzzy Logic Fuzzy logic Resembles human reasoning
Allows for approximate values and inferences and incomplete or ambiguous data
Uses terms such as “very high” instead of precise measures
Used more often in Japan than in the U.S.
Used in fuzzy process controllers used in subway trains, elevators, and cars
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59. Example of Fuzzy Logic Rules and Query
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60. Genetic Algorithms Genetic algorithm software
Uses Darwinian, randomizing, and other mathematical functions
Simulates an evolutionary process, yielding increasingly better solutions to a problem
Being uses to model a variety of scientific, technical, and business processes
Especially useful for situations in which thousands of solutions are possible
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61. Virtual Reality (VR) Virtual reality is a computer-simulated reality
Fast-growing area of artificial intelligence
Originated from efforts to build natural, realistic, multi-sensory human-computer interfaces
Relies on multi-sensory input/output devices
Creates a three-dimensional world through sight, sound, and touch
Also called telepresence
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62. Typical VR Applications
Current applications of virtual reality
Computer-aided design
Medical diagnostics and treatment
Scientific experimentation
Flight simulation
Product demonstrations
Employee training
Entertainment
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63. 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 to make decisions and accomplish tasks in a way that fulfills the intentions of a user
Also call software robots or bots
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64. User Interface Agents
Interface Tutors – observe user computer operations, correct user mistakes, provide hints/advice on efficient software use
Presentation Agents – show information in a variety of forms/media based on user preferences
Network Navigation Agents – discover paths to information, provide ways to view it based on user preferences
Role-Playing – play what-if games and other roles to help users understand information and make better decisions
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65. Information Management Agents
Search Agents – help users find files and databases, search for information, and suggest and find new types of information products, media, resources
Information Brokers – provide commercial services to discover and develop information resources that fit business or personal needs
Information Filters – Receive, find, filter, discard, save, forward, and notify users about products received or desired, including , voice mail, and other information media
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66. Case 3 Centralized Business Intelligence
A reinventing-the-wheel approach to business intelligence implementations can result in
High development costs
High support costs
Incompatible business intelligence systems
A more strategic approach
Standardize on fewer business intelligence tools
Make them available throughout the organization, even before projects are planned
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67. Case 3 Centralized Business Intelligence
About 10 percent of the 2,000 largest companies have a business intelligence competency center
Centralized or virtual
Part of the IT department or independent
Cost reduction is often the driving force behind creating competency centers and consolidating business intelligence systems
Despite the potential savings, funding for creating and running a BI center can be an issue
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68. Case Study Questions What is business intelligence?
Why are business intelligence systems such a popular business application of IT?
What is the business value of the various BI applications discussed in the case?
Is the business intelligence system an MIS or a DSS?
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69. Case 4 Robots, the Common Denominator
In early 2004, 22 patients underwent complex laparoscopic operations
The operations included colon cancer procedures and hernia repairs
The primary surgeon was 250 miles away
A three-armed robot was used to perform the procedures
Left arm, right arm, camera arm
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70. Case 4 Robots, the Common Denominator
Automakers heavily use robotics
Ford has a completely wireless assembly factory
It also have a completely automated body shop
BMW has two wireless plants in Europe and is setting one up in the U.S.
Vehicle tracking and material replenishment are automated as well
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71. Case Study Questions
What is the current and future business value of robotics?
Would you be comfortable with a robot performing surgery on you?
The robotics being used by Ford Motor Co. are contributing to a streamlining of its supply chain
What other applications of robots can you envision to improve supply chain management beyond those described in the case?
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