DECISION%20SUPPORT%20SYSTEMS%20CONCEPTS,%20METHODOLOGIES,%20AND%20TECHNOLOGIES:

1
Chapter 3
Study sub-sections 3.5-10, 3.12(p118-120)

• DECISION SUPPORT SYSTEMS CONCEPTS, METHODOLOGIES,
  AND TECHNOLOGIES
• AN OVERVIEW

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Components of a Closed DSS
3
Components of an Open DSS
Network
4
DSS Classifications(based on dominance of each
component)

• Model-driven DSS quantitative models
  (statistical, financial, optimization,
  simulation) used to generate a recommended
  solution to a problem
• Data-driven DSS support ad-hoc reporting and
  queries on internal external database
• Communication-driven multiple user interface,
  support shared tasks, either cooperative or
  hostile mode
• Knowledge-driven qualitative models uses stored
  rules (Expert Sys Mining)
• Document-driven search, retrieve, analyze,
  classify text documents (eg. Law firms use it to
  create a case)

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Data Management Subsystem-1
6
Data Management Subsystem-2

• The Database
• Internal data come mainly from the organizations
  transaction processing system
• External data include industry data, market
  research data, census data, regional employment
  data, government regulations, tax rate schedules,
  and national economic data
• Private data can include guidelines used by
  specific decision makers and assessments of
  specific data and/or situations

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Data Management Subsystem-3

• Data extraction
• The process of capturing data from several
  disparate sources, synthesizing them, summarizing
  them, determining which of them are relevant, and
  organizing them, resulting in their effective
  integration

8
Data Management Subsystem-4

• Database management system (DBMS)
• Software for establishing, updating, and querying
  a database
• Directory
• A catalog of all the meta-data in a database or
  all the models in a model base

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Data Management Subsystem-5

• Key database and database management system
  issues
• Data quality
• Data integration
• Scalability
• Data security

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The Model Management Subsystem-1
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The Model Management Subsystem-2

• Model Directory
• Index/Catalog/List of all models (meta-data on
  models)
• Model Base
• Contains the actual collection of available
  models themselves that can be readily
  instantiated with data
• Model Base Management
• Tools for creating, manipulating, updating

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The Model Management Subsystem-3

• Four categories of models in the model base
  (based on Business Functions)
• Strategic models
• Tactical models
• Operational models
• Analytical models
• Model integration involves combining the
  operations of several models when needed Eg.
  Factor analysis determines which variables are
  most promising and regression analysis follows up
  with creating the actual prediction model.

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The Model Management Subsystem-4

• Strategic models
• Models that represent problems for the executive
  level of management (eg. How many plants should
  we have five years from now? Uses considerable
  external data)
• Tactical models
• Models that represent problems for the mid-level
  of management (eg. Short-term labor
  recruitmenttraining, sales promotion planning,
  budgeting)
• Operational models
• Models that represent problems for the
  operational (day-to-day activities) level of
  management (eg. Production scheduling, staffing,
  inventory control)
• Analytical models
• Mathematical models typically integrated into
  the above models
• Egs.Statistical, Financial, MS, data mining
  algorithms

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The Model Management Subsystem

• Operational models
• Models that represent problems for the
  operational level of management
• Analytical models
• Mathematical models into which data are loaded
  for analysis

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The Model Management Subsystem-5

• Model building blocks /routines
• Helps to create a custom model from smaller
  components
• Preprogrammed software elements that can be used
  to build computerized models.
• For example, a random-number generator can be
  employed in the construction of a simulation
  model
• Models created using blocks /routines can easily
  be updated
• Some programming is required
• Modeling languages (MDX, XMLA similar to SQL for
  DBs) can also be used

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User Interface (Dialog) Subsystem-1

• User interface management system (UIMS)
• The DSS component that handles all interaction
  between users and the system

GUI
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User Interface (Dialog) Subsystem-2

• Since managers are used to verbal interactions
  and have time constraints, designing DSS
  interfaces pose a challenge
• Voice input and output (no typing)
• Natural language processing (typing spoken
  language)
• GUI (more info can be presented compared to text)
• Touchscreen (slice dice data)
• Responding to body movements (including face)
• Portable devices/ Web interface (as they travel a
  lot)
• Intelligent agents search engines
• Flexibility to suit styles of decision-maker
• Ability to support group decision-making

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Knowledge-Based Management Subsystem (Chapters
11-12)

• Optional component of a DSS
• Typically captures qualitative knowledge
  mathematical models
• Static knowledge of a domain
• Decision rules used in the domain (If-Then)
• Heuristic / logical reasoning
• Backward (goal to data)/ Forward chaining (data
  to goal)
• Repository of past decisions and outcomes
  (machine learning)
• Ability to consult other experts in the field

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The User
A DSS may be directly used by a decision-maker.
But many managers employ an Intermediary/
chauffer A person who uses a DSS to answer
questions for top management The intermediary
should be a(n) Expert tool user with skills in
the application of one or more types of
specialized problem-solving tools Facilitator
who can plan, organize, and electronically
control a group in a collaborative computing
environment