1. Supporting Decision Making
Chapter 10
Supporting Decision Making
McGraw-Hill/Irwin
Copyright © 2011 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 the following information systems can support the information needs of executives, managers, and business professionals
Executive information systems
Enterprise information portals
Knowledge management systems
Identify how neural networks, fuzzy logic, genetic algorithms, virtual reality, and intelligent agents can be used in business

4. Learning Objectives
Give examples of several ways expert systems can be used in business decision-making situations

5. Decision Support in Business
Provide responses to:
Changing market conditions
Customer needs
Several types of systems
Management information
Decision support
Other information systems
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

6. RWC 1: Fact-Based Decision Making
Decisions based on facts beat decisions based on gut
Dashboard
Makes detailed statistics available in real-time
Scorecard
Software compares details to defined metrics
How prepared are organizations to synthesize and share key performance indicators?
How prepared are executives to draw insight from information?
Dashboard and scorecard are often used interchangeably, but important distinction
Fact-based decisions expected by
Financial analysts
Board members
News media

7. Levels of Managerial Decision Making

8. Attributes of 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Information that is outdated, inaccurate, or hard to understand has much less value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

9. Structured (operational) Unstructured (strategic)
Decision Structure
Structured (operational)
Procedures can be specified in advance
Unstructured (strategic)
Not possible to specify procedures in advance
Semi-structured (tactical)
Decision procedures can be pre-specified, but not enough to lead to the correct decision
Structured (operational)
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

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

11. Decision Support Trends
Add info from new paragraphs
The emerging class of applications focuses on
Personalized decision support
Modeling
Information retrieval
Data warehousing
What-if scenarios
Reporting

12. Business Intelligence Applications
Business Intelligence – umbrella term for using fact-based support systems in business decision making
Developed through the 1990s, now competitive necessity
Business intelligence is more associated with querying, reporting, online analytical processing (OLAP), and “alerts.”
Can answer the questions: what happened; how many; how often; where; where exactly is the problem; and what actions are needed.
Further development has created business analytics
Uses tools to gain insight and drive strategic business planning
Can answer the questions: why is this happening; what if these trends continue; what will happen next (that is, predict); and what is the best that can happen (that is, optimize). One of the most common techniques and approaches associated with business analytics is data mining, a concept introduced

13. Decision Support Systems Support:
DSS Components
Decision Support Systems
Support:
Semi-structured business decisions
Answers to ad hoc questions
Quick response systems
Tools used:
Analytical models
Specialized databases
A decision-maker’s own insights and judgments
An interactive, computer-based modeling process
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
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

14. 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
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

15. 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
OLAP 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

16. GIS and DVS Systems GIS - Global 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
DVS – 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

17. Using Decision Support Systems
Interactive analytical modeling process
Exploring possible alternatives
Not pre-specified information
What-If Analysis
Changing variables to see affect on 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

18. Provides decision support through knowledge discovery
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

19. Market Basket Analysis
One of the most common uses for data mining
Determines what products customers purchase together with other products
Other uses
Cross Selling
Product Placement
Affinity Promotion
Survey Analysis
Fraud Detection
Analyze Customer Behavior
Consider some of the typical applications of MBA:
• Cross Selling. Offer the associated items when customer buys any items from
your store.
• Product Placement. Items that are associated (such as bread and butter, tissues
and cold medicine, potato chips and beer) can be put near each other. If
the customers see them, it has higher probability that they will purchase them
together.
• Affinity Promotion. Design the promotional events based on associated products.
• Survey Analysis. The fact that both independent and dependent variables of market basket analysis are nominal (categorical) data type makes MBA very useful to analyze questionnaire data.
• Fraud Detection. Based on credit card usage data, we may be able to detect certain purchase behaviors that can be associated with fraud.
• Customer Behavior. Associating purchase with demographic, and socio economic data (such as age, gender, and preference) may produce very useful results for marketing.

20. Executive Information Systems (EIS)
Combines many features of MIS and DSS
Provides immediate and easy information
Identifies critical success factors
Features
Customizable graphical user interfaces
Exception reports
Trend analysis
Drill down capability
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

21. Enterprise Information Portal Components
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

22. Enterprise Knowledge Portal
Enterprise knowledge portals play an essential role in helping companies use their intranets as knowledge management systems to share and disseminate knowledge in support of business decision making by managers and business professionals.

23. RWC 2: Shopping in Virtual Stores
Benefits of virtual stores
Help understand customer behavior
Test products faster, more convenient and precise
Win shelf space
Focus on ways to get customers’ attention
Avoids tipping off competitors
Cuts testing time
Avoid displays that clash with store decor
Environment of virtual shopping
Change variables with each test

24. Attributes of Intelligent Behavior
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, see, hear, walk, talk, and feel as well

25. Domains of Artificial Intelligence
Cognitive Science area focuses on how the human brain works and how humans think and learn
Robotics area involves engineering and physiology
Produces robot machines with computer intelligence and humanlike physical capabilities
The Natural Interfaces area involves research and development in Linguistics, Psychology, Computer Science and other disciplines
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
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

26. Components of an Expert System
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
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

27. Methods of Knowledge Representation
Case-Based
Examples from the past
Frame-Based
Collection of knowledge about an entity
Object-Based
Data elements include both data and the methods or processes that act on those data
Rule-Based
Factual statements in the form of a premise and a conclusion (If, Then)
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)

28. 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

29. Expert System Application Categories
Design/Configuration
Selection/Classification
Process Monitoring/Control
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

30. Benefits of Expert Systems
Captures human experience in a computer-based information system
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
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
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

31. Suitability Criteria for Expert Systems
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
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

32. Knowledge Engineering
A knowledge engineer
Works with experts to capture the knowledge they possess
Facts and rules of thumb
Builds the knowledge base
if necessary, the rest of the expert system
Similar role to systems analysts
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

33. Neural Networks
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
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

34. Example of Fuzzy Logic Rules and Query
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

35. Genetic algorithm software
Uses Darwinian, randomizing, and other mathematical functions
Simulates an evolutionary process, yielding increasingly better solutions to a problem
Used to model a variety of scientific, technical, and business processes
Useful when thousands of solutions are possible

36. Virtual reality is a computer-simulated reality
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
Telepresence
Using VR to perform a task in a different location
Current applications of virtual reality
Computer-aided design
Medical diagnostics and treatment
Scientific experimentation
Flight simulation
Product demonstrations
Employee training
Entertainment

37. 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 accomplish tasks
Software robots or bots
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
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

38. Types of Intelligent Agents
User Interface Agents
Interface Tutors
Presentation Agents
Network Navigation Agents
Role-Playing Agents
Information Management Agents
Search Agents
Information Brokers
Information Filters
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
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

39. RWC 3: Driving Competitive Advantage
Advanced technologies impact businesses
Goodyear reduced time to market
Public Utility Company JEA determines optimal combinations of oil and natural gas
The Ohio State University Medical Center (OSUMC) uses robots to move supplies
Advanced technologies such as AI, mathematical simulations, and robotics can have dramatic impacts on both business processes and financial results.
At Goodyear, designers can perform tests 10 times faster using simulation, reducing a new tire’s time to market from two years to as little as 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 (OSUMC) 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.
10-39

40. RWC 4: Business Intelligence Deployments
Analyze raw data (e.g., sales transactions)
Extract useful insights
Can transform business processes
Can impact the bottom line
Major impediment - most companies don’t understand their business processes well enough
Uncovering flawed business processes beats merely to monitoring