Chapter 10 Decision Support Systems

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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 13
  9780073323091

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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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Levels of Managerial Decision Making
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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• Information that is outdated, inaccurate, or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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Attributes of Information Quality
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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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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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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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Business Intelligence Applications
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Decision Support Systems

• Decision support systems use the following to
  support the making of semi-structured business
  decisions
• Analytical models
• Specialized databases
• A decision-makers 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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DSS Components
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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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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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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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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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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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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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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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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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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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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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Analysis of Customer Demographics
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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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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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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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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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Dashboard Example
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Enterprise Information Portal Components
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Enterprise Knowledge Portal
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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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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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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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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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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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Domains of Artificial Intelligence
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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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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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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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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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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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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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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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Components of an Expert System
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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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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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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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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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Expert System Application Categories

• Process Monitoring/Control
• Machine control (including robotics)
• Inventory control
• Production monitoring
• Chemical testing

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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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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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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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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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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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Neural Networks

• Computing systems modeled after the brains
  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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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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Example of Fuzzy Logic Rules and Query
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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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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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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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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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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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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 e-mail,
  voice mail, and other information media

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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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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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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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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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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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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?