1. Decision Support Systems
Chapter 9
Decision Support Systems
McGraw-Hill/Irwin
Copyright © 2010 by the McGraw-Hill Companies, Inc. All rights reserved.

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 management information systems
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

3. Learning Objectives
Explain how these information systems can support the information needs of executives, managers, and business professionals
Executive information systems
Enterprise information portals
Knowledge management systems

4. Learning Objectives
Identify how neural networks, fuzzy logic, genetic algorithms, virtual reality, and intelligent agents can be used in business
Give examples of ways expert systems can be used in business decision-making situations

5. Decision Support in Business
Companies invest in data-driven decision support application frameworks to help them respond to
Changing marketing conditions
Customer needs
Management information
Accomplished by several types of
Decision support
Other information systems

6. Case 1: Hillman Group, Avnet, Quaker Chemical
BI refers to a variety of software applications used to analyze an organization’s raw data and extract useful insights from them
BI tools, coupled with business process changes, can have a significant impact on the bottom line
Most companies don’t understand their business processes well enough to determine how to improve them
Companies using BI to uncover flawed business processes can more successfully compete against companies using BI merely to monitor what’s happening

7. Case Questions
What are the business benefits of BI deployments such as those implemented by Avnet and Quaker Chemical?
What roles do data and business processes play in achieving those benefits?
What are the main challenges to the change of mindset required to extend BI tools beyond mere reporting?
What can companies do to overcome them?

8. Case Questions
Avnet and Quaker Chemical implemented systems and processes that affect the practices of their salespeople
In which ways did the latter benefit from these new implementations?
How important was their buy-in to the success of these projects?
Discuss alternative strategies for companies to foster adoption of new systems like these

9. Levels of Managerial Decision Making

10. Information has three dimensions
Information Quality
Information products are made more valuable by their attributes, characteristics, or qualities
Outdated, inaccurate, or hard to understand information has much less value
Information has three dimensions
Time
Content
Form

11. Attributes of Information Quality

12. Decision Structure
Structured (operational)
The procedures to follow when a 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

13. 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
Pre-specified, 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

14. Decision Support Trends
Personalized decision support
Modeling
Information retrieval
Data warehousing
What-if scenarios
Reporting

15. Decision Support Trends

16. Business Intelligence Applications

17. Decision Support Systems
To support the making of semi-structured business decisions, DSS uses
Analytical models
Specialized databases
Decision-maker’s own insights and judgments
Interactive, computer-based modeling process
DS systems
Ad hoc, quick-response systems
Initiated and controlled by decision makers

18. DSS Components

19. DSS Model Base Model Base Spreadsheet Examples A software component
Consists of models used in computational and analytical routines
Mathematically expresses relationships among variables
Spreadsheet Examples
Linear programming
Multiple regression forecasting
Capital budgeting present value

20. Applications of Statistics and Modeling
Supply Chain
Simulate & optimize supply chain flows, reduce inventory & stock-outs
Pricing
Identify the price that maximizes yield or profit
Product & Service Quality
Detect quality problems early in order to minimize them
Research & Development
Improve quality, efficacy, and safety of products and services

21. 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, displays, and responses
Satisfies needs of operational and tactical decision makers who face structured decisions

22. Management Reporting Alternatives
Periodic Scheduled Reports
Pre-specified format, issued on a regular basis
Exception Reports
Reports about exceptional conditions, scheduled or on event
Demand Reports & Responses
Information is available on demand
Push Reporting
Information is pushed to a networked computer

23. 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

24. Online Analytical Operations
Consolidation
Aggregation of data
Ex: sales office data, rolled up to the district level
Drill-Down
Display underlying detail data
Ex: sales figures by individual product
Slicing and Dicing
Viewing database from different viewpoints
Often performed along a time axis

25. Geographic Information Systems (GIS)
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 System (GPS) devices

26. Data Visualization Systems (DVS)
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

27. 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

28. Using Decision Support Systems
What-If Analysis
Sensitivity Analysis
Basic analytical modeling activities
Goal-seeking Analysis
Optimization Analysis
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

29. Data Mining 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

30. Analysis of Customer Demographics

31. Market Basket Analysis
One of the most common uses for data mining
Determines what products customers purchase together with other products
Typical applications of MBA
Cross-selling
Product placement
Affinity promotion
Survey analysis
Fraud detection
Customer behavior identification

32. Executive Information Systems (EIS)
Combines many features of MIS and DSS
Provides top executives with immediate, easy access to information
Identifies factors critical to accomplishing strategic objectives
So popular it was expanded to managers, analysis, and other knowledge workers

33. 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

34. Web-Based Executive Information System

35. Enterprise Information Portals
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

36. Enterprise Information Portal Components

37. Enterprise Knowledge Portal

38. Case 2: Goodyear, JEA, OSUMC, Monsanto
Advanced technologies (AI, mathematical simulations, robotics) can have dramatic impacts on business processes and financial results
Goodyear designers can perform tests 10 times faster using simulation, reducing a new tire’s time to market from two years to nine months
Public Utility Company JEA uses neural network technology to automatically determine the optimal combinations of oil and natural gas the utility’s boilers need to produce electricity cost effectively, given fuel prices and the amount of electricity required
The Ohio State University Medical Center replaced its overhead rail transport system with 46 self-guided robotic vehicles to move linens, meals, trash, and medical supplies throughout the 1,000-bed hospital

39. Case Study Questions
In all of the project outcomes in the case, the payoffs are both larger and achieved more rapidly than in more traditional system implementations
Why do you think this is the case?
How are these projects different from others you have come across in the past?
What are those differences?
How do these technologies create business value for the implementing organizations?
In which ways are these implementations similar in how they accomplish this, and how are they different?

40. Case Study Questions
In all of the case examples, companies had an urgent need that prompted them to investigate radical, new technologies
Do you think the story would have been different had the companies been performing well already? Why or why not?
To what extent are these innovations dependent on the presence of a problem or crisis?

41. Artificial Intelligence (AI)
Engineering
Computer science
AI is a field of science and technology based on…
Mathematics
Biology
Linguistics
Psychology

42. Artificial Intelligence (AI)
Think
Feel
See
Ultimate goal for computers
Hear
Talk
Walk

43. 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 relative importance of situation elements
Handle ambiguous, incomplete, erroneous info

44. Domains of Artificial Intelligence
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
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
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

45. Expert Systems An Expert System (ES)
Knowledge-based information system
Contains knowledge about a specific, complex application area
Acts as an export consultant to end users

46. 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, recommends a course of action
User interface programs communicate with the end user
Explanation programs explain the reasoning process to the end user

47. Methods of Knowledge Representation
Case-based
Frame-based
Object-based
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
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)
Rule-based

48. Expert System Application Categories
Decision Management
Diagnostic/Troubleshooting
Design/Configuration
Selection/Classification
Decision Management
Loan portfolio analysis
Employee performance evaluation
Insurance underwriting
Diagnostic/Troubleshooting
Equipment calibration
Help desk operations
Medical diagnosis
Software debugging
Design/Configuration
Computer option installation
Manufacturability studies
Communications networks
Selection/Classification
Material selection
Delinquent account identification
Information classification
Suspect identification
Process Monitoring/Control
Machine control (including robotics)
Inventory control
Production monitoring
Chemical testing
Process Monitoring/Control

49. Benefits of Expert Systems
Captures expertise of expert(s) 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

50. Limitations of Expert Systems
Major limitations of expert systems
Limited focus
Inability to learn
Maintenance problems
Development and maintenance costs
Can only solve specific types of problems in a limited domain of knowledge

51. Developing Expert Systems
Suitability Criteria for Expert Systems
Domain The domain or subject area of the problem is small and well-defined
Expertise
Solutions to the problem require the efforts of an expert
Complexity
Problem solving is complex, and requires logical inference processing

52. Developing Expert Systems
Suitability Criteria for Expert Systems
Structure… 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

53. 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

54. 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

55. 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
Recognizes patterns and relationships in data

56. Fuzzy Logic Resembles human reasoning
Allows approximate values and inferences, and incomplete or ambiguous data
Uses terms like “very high” instead of precise measures
Allows processing of incomplete data
Results in quick, approximate solutions
Used in fuzzy process controllers (subway trains, elevators, cars)

57. Example of Fuzzy Logic Rules and Query

58. Genetic Algorithms
Uses Darwinian, randomizing, and other mathematical functions
Stimulates an evolutionary process, yielding increasingly better solutions
Genetic algorithm software
Especially useful for situations in which thousands of solutions are possible
Being used to model a variety of scientific, technical, and business processes

59. 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

60. Typical VR Applications
Computer-aided design
Medical diagnostics and treatment
Entertainment
Scientific experimentation
Current applications of virtual reality
Employee training
Flight simulation
Product demonstrations

61. Also called software robots or bots
Intelligent Agents
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 called software robots or bots

62. Network Navigation Agents
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

63. 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

64. Case 3: Harrah’s, LendingTree, DeepGreen, Cisco
The promise of AI of automating decision making has been very slow to materialize
The new generation AI applications
Easier to create and manage
Don’t require anyone to identify problems or to initiate analysis
Decision-making capabilities are embedded into the normal flow of work, and are triggered without human intervention

65. Case 4: Harrah’s, LendingTree, DeepGreen, Cisco
The new generation AI applications
Sense online data or conditions, apply codified knowledge or logic and make decisions with minimal human intervention
Rely on experts and managers to create and maintain rules and monitor the results
Managers in charge of automated decision systems must develop processes for managing exceptions

66. Case Study Questions
Why did some previous attempts to use artificial intelligence technologies fail?
What differences between the new AI-based applications versus the old caused the authors to declare that automated decision making is finally coming of age?
What types of decisions are best suited for automated decision making?
Provide examples of successful applications from the companies in this case

67. Case Study Questions
What role do humans play in automated decision making applications?
What challenges face managers where automated decision-making systems are being used?
What solutions are needed to meet such challenges?