DECISION SUPPORT SYSTEMS

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DECISION SUPPORT SYSTEMS
CONTENTS 1. Definition 2. Components of
decision support systems (DSS) 3. Software tools
for DSS application developments 4. Advantages
and possible drawbacks of DSS usage
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Decision support systems (DSS) are computer
programs thatprovide information in a given
domain of application by means ofanalytical
decision models and access to databases,in order
to support a decision maker in making decisions
effectively in complex and ill-structured tasks.

• well-structured problems routine and
  repetitive
• ill-structured problems no known and clear
  methods of obtaining solution

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• Characteristics of ill-structured problems
• the preferences, judgements, intuition and
  experience of the decision maker are essential
• the search for a solution implies a mixture of
• search for information
• formalisation (problem definition and
  structuring)
• computation
• data manipulation
• the sequence of the above operations is not
  known in advance
• it can be function of data
• it can be modified
• it can be a function of the user preferences
• criteria for the decision are numerous, in
  conflict, and dependent on the perception of
  the user
• the solution must be achieved in limited time
• the problems evolves rapidly

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• Characteristics and Capabilities of DSS
• Provide support in semi-structured and
  unstructured situations
• Support to individuals and groups
• Support all phases of the decision-making
  process
• Are adaptive
• Have user friendly interfaces
• Goal is to improve the effectiveness of
  decision making
• The decision maker controls the decision-making
  process
• End-users can build simple systems
• Utilizes models for analysis
• Provides access to a variety of data sources,
  formats, and types
• Decision makers can make better, more consistent
  decisions in a timely manner.

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Components of DSS
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• Data management subsystem includes the
  database, which contains relevant data for the
  situation and is managed by software called the
  database management system (DBMS).
• Model management subsystem includes financial,
  statistical, management science, or other models
  and usually contains a modelling language for
  building custom models. It is managed by software
  called the model base management system (MBMS).
• Knowledge management subsystem provides
  intelligence to augment the operations of the
  other DSS components.
• User interface subsystem enables the user to
  communicate with the DSS.

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1. Data Management Subsystem
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• Database - a collection of interrelated data
  organised to meet the needs and structure of an
  organisation
• Data warehouse - combines various data sources
  into a single resource
• Internal data
• transaction information systems
• Intranet - internal web-based system
• External data
• Internet
• Private data
• Extraction - to capture data from several sources

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• Database Management System (DBMS)
• Solves two problems
• centralisation, coordination, and diffusion of
  information
• logical independence between data and DSS
  application
• Functions of the DBMS
• Data definition
• describes the data entities and their
  relationships
• Data manipulation
• updates data records and files (adds records,
  deletes, modifies)
• Data integrity
• integrity constraints - rules to maintain the
  integrity of the database
• Control of access rights
• subsets of data must be used only by
  authorised persons

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• Functions of the DBMS (continued)
• Concurrency control
• users may access the same information in the
  database at the same time
• Transaction recovery
• to restart the system when the hardware failure
  has been corrected
• Retrieves data from the database for queries
  and reports
• Captures/extracts data for inclusion in a DSS
  database
• Interrelates data from different sources
• Tracks data use within the DSS
• Manages data through a data dictionary
• catalogue of all the data in database

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2. Model Management Subsystem
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• Model base contains routine and special
  statistical, financial, forecasting, management
  science and other quantitative models.
• Four major categories
• Strategic models
• support top-management's strategic planning
  responsibilities
• Managerial (tactical) models
• assist in allocating and controlling the
  organisational resources
• Operational models
• supports the day-to-day activities of the
  organisation
• Model Building Blocks and Routines
• random number generator routine
• regression analysis
• Other classifications
• by functional areas
• by discipline

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Types of models
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• Model Base Management System (MBMS)
• MBMS capabilities similar to that of DBMS
• But, there are no comprehensive model base
  management packages.
• Each organization uses models somewhat
  differently.
• There are many model classes.
• Functions of the MBMS
• Create models
• Manipulation of the models (experiments,
  sensitivity analysis, etc.)
• Uses multiple models to support problem solving
• Maintains the model base (stores, retrieves and
  manages different types of models)
• Accesses and integrates the model building
  blocks
• Model directory
• catalogue of all the modules in the model base
• Interrelates models with the database and
  integrates them within the DSS
• Modelling languages

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• 3. Knowledge Management Subsystem
• Terms intelligent DSS, knowledge-based DSS,
  DSS/ES
• Provides expertise for solving some aspects of
  the problem and provides knowledge that can
  enhance the operation of other DSS components.
• support the steps of the decision process
  unaddressed by mathematics
• help user to build, apply and manage libraries
  of models
• integrate methods to handle uncertainty

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4. User Interface Subsystem
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• Covers all aspects of communication between a
  user and the DSS.
• User Interface Management Subsystem (UIMS)
• Functions of the UIMS
• Provides graphical user interface
• Provides interactions with the database and the
  model base
• Store input and output data
• Display forms to input data needed to solve the
  problem
• Display and print reports
• Obtain the decision criteria
• Help him/her select between alternatives
• Store the decision and monitor it
• Gives users help capabilities

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Software Tools for DSS Applications Developments

• 1. General programming languages
• require time and competence to learn
• do not provide subsystems
• difficult for the application to evolve
• Appropriate if
• the DSS application does not evolve too much
• performance is crucial
• there is no severe time constraint on
  implementation delay
• 2. Spreadsheets
• do not provide a satisfactory readability of
  decision models
• do not provide a way to represent easily
  complex data structure
• evolution of the application is difficult due
  to the non-separation between data management,
  models, forms and reports
• Good for simple personal applications!

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3. DSS generator
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• Database management systems (DBMS)
• provides a link to the database using SQL
• Modelling subsystems
• a modelling language
• a library of algorithms for solving the
  problems modelled by decision models
• an interface to manage the model base

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• Report definition and management
• a language to manage the report base
• a graphical interface to define reports
• Form definition and management
• test the input data (type, value taken in an
  interval, etc.)
• a language to manage the form base
• an interface to manage the form base
• Graphics
• curves, barcharts, piecharts, Gantt charts, etc.
• Library of useful algorithms
• statistical and/or management science models
• statistical analysis
• short-term forecasting
• operational research methods

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• Communication between users
• e-mail
• PC-based video-conferencing
• Exchange of data in a distributed environment
• to support decision process involving several
  persons

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• Advantages of DSS generators
• designed for end-users or domain specialist
• provide the resources for application
  development
• allow development of complex applications

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Improvements Expected from DSS Usage

• greater effectiveness of decision making
• improved efficiency
• better communication among decision makers
• improving the learning process of users

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Possible Drawbacks of DSS Usage
1. Tendency to overemphasise the decision making
aspect criticism DSS denies the communication
in decision making process 2. Assumption of
relevance decision must belong to the class that
the DSS was built to support 3. Unintended
transfer of power decision process is only
assisted by DSS 4. Obscuring responsibility risk
that a DSS is used in a way which does not fit
the assumptions of the designer
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References 1. (a) Decision Support Systems and
Intelligent Systems, Fifth Edition E.Turban, Jay
Aronson, Prentice Hall, 1998. (b) Decision
Support Systems and Expert Systems, Management
Support Systems, E.Turban, Fourth
Edition, Prentice Hall, 1995. 2.
Knowledge-based Decision Support Systems, With
Applications in Business, 2nd Edition, M. Klein,
L. Methlie, Wiley, 1995.
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SUMMARY

• DSS is composed of the following subsystems
• data management subsystems,
• model management subsystem,
• knowledge management subsystem,
• user interface subsystem.
• DSS support all the phases in the decision
  making process.
• DSS applications have been developed using
• general programming languages
• spreadsheets
• DSS generators