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Chap13 Chap13 Presentation Transcript

  • Chapter 13
    • Decision Support Systems
    MANAGEMENT INFORMATION SYSTEMS 8/E Raymond McLeod, Jr. and George Schell Copyright 2001 Prentice-Hall, Inc. 13-
  • Simon’s Types of Decisions
    • Programmed decisions
      • repetitive and routine
      • have a definite procedure
    • Nonprogrammed decisions
      • Novel and unstructured
      • No cut-and-dried method for handling problem
    • Types exist on a continuum
    13-
  • Simon’s Problem Solving Phases
    • Intelligence
      • Searching environment for conditions calling for a solution
    • Design
      • Inventing, developing, and analyzing possible courses of action
    • Choice
      • Selecting a course of action from those available
    • Review
      • Assessing past choices
    13-
  • Definitions of a Decision Support System (DSS)
    • General definition - a system providing both problem-solving and communications capabilities for semistructured problems
    • Specific definition - a system that supports a single manager or a relatively small group of managers working as a problem-solving team in the solution of a semistructured problem by providing information or making suggestions concerning specific decisions.
    13-
  • The DSS Concept
    • Gorry and Scott Morton coined the phrase ‘DSS’ in 1971, about ten years after MIS became popular
    • Decision types in terms of problem structure
      • Structured problems can be solved with algorithms and decision rules
      • Unstructured problems have no structure in Simon’s phases
      • Semistructured problems have structured and unstructured phases
    13-
  • Degree of problem structure The Gorry and Scott Morton Grid Management levels Structured Semistructured Unstructured Operational control Management control Strategic planning Accounts receivable Order entry Inventory control Budget analysis-- engineered costs Short-term forecasting Tanker fleet mix Warehouse and factory location Production scheduling Cash management PERT/COST systems Variance analysis-- overall budget Budget preparation Sales and production Mergers and acquisitions New product planning R&D planning 13-
  • Alter’s DSS Types
    • In 1976 Steven Alter, a doctoral student built on Gorry and Scott-Morton framework
      • Created a taxonomy of six DSS types
      • Based on a study of 56 DSSs
    • Classifies DSSs based on “degree of problem solving support.”
    13-
  • Levels of Alter’s DSSs
    • Level of problem-solving support from lowest to highest
      • Retrieval of information elements
      • Retrieval of information files
      • Creation of reports from multiple files
      • Estimation of decision consequences
      • Propose decisions
      • Make decisions
    13-
  • Importance of Alter’s Study
    • Supports concept of developing systems that address particular decisions
    • Makes clear that DSSs need not be restricted to a particular application type
    13-
  • Retrieve information elements Analyze entire files Prepare reports from multiple files Estimate decision consequen-ces Propose decisions Degree of problem solving support Degree of complexity of the problem-solving system Little Much Alter’s DSS Types Make decisions 13-
  • Three DSS Objectives
    • 1. Assist in solving semistructured problems
    • 2. Support, not replace, the manager
    • 3. Contribute to decision effectiveness, rather than efficiency
    Based on studies of Keen and Scott-Morton 13-
  • GDSS software Mathematical Models Other group members Database GDSS software Environment Individual problem solvers Decision support system Environment Legend : Data Information Communication A DSS Model Report writing software 13-
  • Database Contents
    • Used by Three Software Subsystems
      • Report writers
        • Special reports
        • Periodic reports
        • COBOL or PL/I
        • DBMS
      • Mathematical models
        • Simulations
        • Special modeling languages
      • Groupware or GDSS
    13-
  • Group Decision Support Systems
    • Computer-based system that supports groups of people engaged in a common task (or goal) and that provides an interface to a shared environment.
    • Used in problem solving
    • Related areas
      • Electronic meeting system (EMS)
      • Computer-supported cooperative work (CSCW)
      • Group support system (GSS)
      • Groupware
    13-
  • How GDSS Contributes to Problem Solving
    • Improved communications
    • Improved discussion focus
    • Less wasted time
    13-
  • GDSS Environmental Settings
    • Synchronous exchange
      • Members meet at same time
      • Committee meeting is an example
    • Asynchronous exchange
      • Members meet at different times
      • E-mail is an example
    • More balanced participation.
    13-
  • GDSS Types
    • Decision rooms
      • Small groups face-to-face
      • Parallel communication
      • Anonymity
    • Local area decision network
      • Members interact using a LAN
    • Legislative session
      • Large group interaction
    • Computer-mediated conference
      • Permits large, geographically dispersed group interaction
    13-
  • Smaller Larger GROUP SIZE Face-to- face Dispersed Decision Room Local Area Decision Network Legislative Session Computer- Mediated Conference MEMBER PROXIMITY Group Size and Location Determine GDSS Environmental Settings 13-
  • Groupware
    • Functions
      • E-mail
      • FAX
      • Voice messaging
      • Internet access
    • Lotus Notes
      • Popular groupware product
      • Handles data important to managers
    13-
  • Main Groupware Functions IBM TeamWARE Lotus Novell Function Workgroup Office Notes GroupWise X = standard feature O = optional feature 3 = third party offering 13-
  • Artificial Intelligence (AI) The activity of providing such machines as computers with the ability to display behavior that would be regarded as intelligent if it were observed in humans. 13-
  • History of AI
    • Early history
      • John McCarthy coined term, AI, in 1956, at Dartmouth College conference.
      • Logic Theorist (first AI program. Herbert Simon played a part)
      • General problem solver (GPS)
    • Past 2 decades
      • Research has taken a back seat to MIS and DSS development
    13-
  • Areas of Artificial Intelligence Expert systems AI hardware Robotics Perceptive systems (vision, hearing) Neural networks Natural language Learning Artificial Intelligence 13-
  • Appeal of Expert Systems
    • Computer program that codes the knowledge of human experts in the form of heuristics
    • Two distinctions from DSS
      • 1. Has potential to extend manager’s problem-solving ability
      • 2. Ability to explain how solution was reached
    13-
  • Know- ledge base User User interface Instructions & information Solutions & explanations Knowledge Inference engine Problem Domain Expert and knowledge engineer Development engine Expert system An Expert System Model 13-
  • Expert System Model
    • User interface
      • Allows user to interact with system
    • Knowledge base
      • Houses accumulated knowledge
    • Inference engine
      • Provides reasoning
      • Interprets knowledge base
    • Development engine
      • Creates expert system
    13-
  • User Interface
    • User enters:
      • Instructions
      • Information
    • Expert system provides:
      • Solutions
      • Explanations of
        • Questions
        • Problem solutions
    } Menus, commands, natural language, GUI 13-
  • Knowledge Base
    • Description of problem domain
    • Rules
      • Knowledge representation technique
      • ‘IF:THEN’ logic
      • Networks of rules
        • Lowest levels provide evidence
        • Top levels produce 1 or more conclusions
        • Conclusion is called a Goal variable.
    13-
  • Evidence Conclusion Conclusion Evidence Evidence Evidence Evidence Evidence Evidence Evidence Conclusion A Rule Set That Produces One Final Conclusion 13-
  • Rule Selection
    • Selecting rules to efficiently solve a problem is difficult
    • Some goals can be reached with only a few rules; rules 3 and 4 identify bird
    13-
  • Inference Engine
    • Performs reasoning by using the contents of knowledge base in a particular sequence
    • Two basic approaches to using rules
      • 1. Forward reasoning (data driven)
      • 2. Reverse reasoning (goal driven)
    13-
  • Forward Reasoning (Forward Chaining)
    • Rule is evaluated as:
      • (1) true, (2) false, (3) unknown
    • Rule evaluation is an iterative process
    • When no more rules can fire, the reasoning process stops even if a goal has not been reached
    Start with inputs and work to solution 13-
  • Rule 1 Rule 3 Rule 2 Rule 4 Rule 5 Rule 6 Rule 7 Rule 8 Rule 9 Rule 10 Rule 11 Rule 12 IF A THEN B IF C THEN D IF M THEN E IF K THEN F IF G THEN H IF I THEN J IF B OR D THEN K IF E THEN L IF K AND L THEN N IF M THEN O IF N OR O THEN P F IF (F AND H) OR J THEN M The Forward Reasoning Process T T T T T T T T T F T Legend: First pass Second pass Third pass 13-
  • Reverse Reasoning Steps (Backward Chaining)
    • Divide problem into subproblems
    • Try to solve one subproblem
    • Then try another
    Start with solution and work back to inputs 13-
  • T Rule 1 Rule 2 Rule 3 Rule 9 Rule 11 Legend: Problems to be solved Step 4 Step 3 Step 2 Step 1 Step 5 IF A THEN B IF B OR D THEN K IF K AND L THEN N IF N OR O THEN P IF C THEN D IF M THEN E IF E THEN L IF (F AND H) OR J THEN M IF M THEN O IF M THEN O The First Five Problems Are Identified Rule 7 Rule 10 Rule 12 Rule 8 13- T
  • If K Then F Legend: Problems to be solved If G Then H If I Then J If M Then O Step 8 Step 9 Step 7 Step 6 Rule 4 Rule 5 Rule 11 Rule 6 T IF (F And H) Or J Then M T Rule 9 T T Rule 12 T If N Or O Then P The Next Four Problems Are Identified 13-
  • Forward Versus Reverse Reasoning
    • Reverse reasoning is faster than forward reasoning
    • Reverse reasoning works best under certain conditions
      • Multiple goal variables
      • Many rules
      • All or most rules do not have to be examined in the process of reaching a solution
    13-
  • Development Engine
    • Programming languages
      • Lisp
      • Prolog
    • Expert system shells
      • Ready made processor that can be tailored to a particular problem domain
    • Case-based reasoning (CBR)
    • Decision tree
    13-
  • Expert System Advantages
    • For managers
      • Consider more alternatives
      • Apply high level of logic
      • Have more time to evaluate decision rules
      • Consistent logic
    • For the firm
      • Better performance from management team
      • Retain firm’s knowledge resource
    13-
  • Expert System Disadvantages
    • Can’t handle inconsistent knowledge
    • Can’t apply judgment or intuition
    13-
  • Keys to Successful ES Development
    • Coordinate ES development with strategic planning
    • Clearly define problem to be solved and understand problem domain
    • Pay particular attention to ethical and legal feasibility of proposed system
    • Understand users’ concerns and expectations concerning system
    • Employ management techniques designed to retain developers
    13-
  • Neural Networks
    • Mathematical model of the human brain
      • Simulates the way neurons interact to process data and learn from experience
    • Bottom-up approach to modeling human intuition
    13-
  • The Human Brain
    • Neuron -- the information processor
      • Input -- dendrites
      • Processing -- soma
      • Output -- axon
    • Neurons are connected by the synapse
    13-
  • Soma (processor ) Axon Synapse Dendrites (input) Axonal Paths (output) Simple Biological Neurons 13-
  • Evolution of Artificial Neural Systems (ANS)
    • McCulloch-Pitts mathematical neuron function (late 1930s) was the starting point
    • Hebb’s learning law (early 1940s)
    • Neurocomputers
      • Marvin Minsky’s Snark (early 1950s)
      • Rosenblatt’s Perceptron (mid 1950s)
    13-
  • Current Methodology
    • Mathematical models don’t duplicate human brains, but exhibit similar abilities
    • Complex networks
    • Repetitious training
      • ANS “learns” by example
    13-
  • Single Artificial Neuron 13- y 1 y 2 y 3 y n-1 y w 1 w 2 w 3 w n-1
  • The Multi-Layer Perceptron Y n2 IN n OUT n OUT 1 IN 1 Y 1 Input Layer OutputLayer 13-
  • Knowledge-based Systems in Perspective
    • Much has been accomplished in neural nets and expert systems
    • Much work remains
    • Systems abilities to mimic human intelligence are too limited and regarded as primitive
    13-
  • Summary [cont.]
    • AI
      • Neural networks
      • Expert systems
    • Limitations and promise
    13-