Chapter 3 Decision Support Systems: An Overview

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Chapter 3Decision Support SystemsAn Overview
Turban, Aronson, and Liang
Decision Support Systems
and Intelligent Systems, Seventh
Edition
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Learning Objectives

• Understand DSS configurations.
• Learn characteristics and capabilities of DSS.
• Understand DSS components.
• Describe structure of DSS components.
• Understand how DSS and the Web interact.
• Learn the role of the user in DSS.
• Understand DSS hardware and integration.
• Learn DSS configurations.

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Southwest Airlines Flies in the Face of
Competition Through DSS Vignette

• Successfully integrates DSS applications
• Ties ERP applications to OLAP, allowing retrieval
  of financial data
• Allows access to both financial and operational
  data

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Decision Support Systems

• Systems designed to support managerial
  decision-making in unstructured problems
• More recently, emphasis has shifted to inputs
  from outputs
• Mechanism for interaction between user and
  components
• Usually built to support solution or evaluate
  opportunities

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DSS Configurations

• Supports individuals and teams
• Used repeatedly and constantly
• Two major components data and models
• Web-based
• Uses subjective, personal, and objective data
• Has a simulation model
• Used in public and private sectors
• Has what-if capabilities
• Uses quantitative and qualitative models

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DSS

• A DSS is a methodology that supports
  decision-making.
• It is
• Flexible
• Adaptive
• Interactive
• GUI-based
• Iterative and
• Employs modeling.

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DSS Definitions

• Little 1970 model-based set of procedures for
  processing data and judgments to assist a manager
  in his decision making Assumption that the
  system is computer-based and extends the users
  capabilities.
• Alter 1980 Contrasts DSS with traditional EDP
  systems (Table 3.1)

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• Moore and Chang 1980
• 1.extendible systems2.capable of supporting ad
  hoc data analysis and decision modeling3.oriented
  toward future planning4.used at irregular,
  unplanned intervals
• Bonczek et al. 1980 A computer-based system
  consisting of 1. a language system --
  communication between the user and DSS
  components2. a knowledge system3. a
  problem-processing system--the link between the
  other two components

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• Keen 1980
• DSS apply to situations where a final system
  can be developed only through an adaptive process
  of learning and evolution
• Central Issue in DSSsupport and improvement of
  decision making

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DSS application

• A DSS is usually built to support the solution of
  a certain problem or to evaluate an opportunity
  (so called DSS application)
• A DSS is an interactive, flexible, and adaptable
  CBIS, specially developed for supporting the
  solution of a non-structured management problem
  for improved decision making. It utilizes data,
  it provides easy user interface, and it allows
  for the decision makers own insights.

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• DSS may utilize models, is built by an
  interactive process (frequently by end-users),
  supports all the phases of the decision making,
  and may include a knowledge component

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Business Intelligence

• Proactive
• Accelerates decision-making
• Increases information flows
• Components of proactive BI
• Real-time warehousing
• Exception and anomaly detection
• Proactive alerting with automatic recipient
  determination
• Seamless follow-through workflow
• Automatic learning and refinement

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Components of DSS

• Subsystems
• Data management
• Managed by DBMS
• Model management
• Managed by MBMS
• User interface
• Knowledge Management and organizational knowledge
  base

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Data Management Subsystem

• Components
• Database
• Database management system
• Data directory
• Query facility

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Database

• Interrelated data extracted from various sources,
  stored for use by the organization, and queried
• Internal data, usually from TPS
• External data from government agencies, trade
  associations, market research firms, forecasting
  firms
• Private data or guidelines used by decision-makers

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Database Management System

• Extracts data
• Manages data and their relationships
• Updates (add, delete, edit, change)
• Retrieves data (accesses it)
• Queries and manipulates data
• Employs data dictionary

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Data Directory

• Catalog of all data
• Contains data definitions
• Answers questions about the availability of data
  items
• Source
• Meaning
• Allows for additions, removals, and alterations

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Model Management Subsystem

• Components
• Model base
• Model base management system
• Modeling language
• Model directory
• Model execution, integration, and command
  processor

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The Structure of the Model Management Subsystem
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Models

• Strategic
• Supports top management decisions
• Tactical
• Used primarily by middle management to allocate
  resources
• Operational
• Supports daily activities
• Analytical
• Used to perform analysis of data

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Model Base Management System

• Functions
• Model creation
• Model updates
• Model data manipulation
• Generation of new routines
• Model directory
• Catalog of models
• Definitions

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Model Management Activities

• Model execution
• Controls running of model
• Model command processor
• Receives model instructions from user interface
• Routes instructions to MBMS or module execution
  or integration functions
• Model integration
• Combines several models operations

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User Interface System
Knowledge-based system
Data management and DBMS
Model management and MBMS
User Interface Management System (UIMS)
Natural Language Processor
Input Action Languages
Output Display Language
PC Display
Users
Printers, Plotters
Based on Figure 3.6, Schematic View of the User
Interface
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User Interface Management System

• GUI
• Natural language processor
• Interacts with model management and data
  management subsystems
• Examples
• Speech recognition
• Display panel
• Tactile interfaces
• Gesture interface

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Knowledge-Based Management System

• Expert or intelligent agent system component
• Complex problem solving
• Enhances operations of other components
• May consist of several systems
• Often text-oriented DSS

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Types of DSS Users

• Tourists
• Look over lots of data on random basis
• Often never look over the same data twice
• Do not know what the requirements are
• Make heavy use of metadata
• Occasionally stumble on something that proves to
  be useful
• Use Internet regularly
• Monitor beds of data regularly
• Look over huge amounts of data on a regular basis
• Sporadic usage of data
• Heavy reliance on tools for scanning
• Sometimes find arenas for further exploration

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Types of DSS Users

• Farmers
• Regular access to data
• Know what they are looking for
• Access small amounts of data
• Predictable access to data
• Predictable processing once data accessed
• Requirements known before search for data starts
• Access data marts regularly
• Unusual to access current level of details
• Find small flakes of gold regularly
• Make use of tools of presentation

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Types of DSS Users

• Explores
• Irregular access data
• Does not know what they are looking for
• Look over masses of data
• Unpredictable pattern of access
• Sometimes find huge nuggets
• Often find nothing
• Requirements are totally unknown
• Access current level detail regularly
• Look at relationships of data user rather than
  occurrences of data
• Make use of tools of discovery and statistical
  analysis and exploration

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DSS Hardware

• De facto standard
• Web server with DBMS
• Operates using browser
• Data stored in variety of databases
• Can be mainframe, server, workstation, or PC
• Any network type
• Access for mobile devices

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DSS Classifications

• Alter
• Extent to which outputs can directly support or
  determine the decision
• Data oriented or model oriented
• Holsapple and Whinston
• Text oriented, database oriented, spreadsheet
  oriented, solver oriented, rule oriented, or
  compound
• Intelligent

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DSS Classifications

• Donovan and Madnick
• Institutional
• Problems of recurring nature
• Ad hoc
• Problems that are not anticipated or are not
  repetitive
• Hackathorn and Keen
• Personal support, group support, or
  organizational support

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DSS Classifications

• GSS v. Individual DSS
• Decisions made by entire group or by lone
  decision maker
• Custom made v. vendor ready made
• Generic DSS may be modified for use
• Database, models, interface, support are built in
• Addresses repeatable industry problems
• Reduces costs

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Web and DSS

• Data collection
• Communications
• Collaborations
• Download capabilities
• Run on Web servers
• Simplifies integration problems
• Increased usability features

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