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Decision Support Systems:


CHAPTER 3 Decision Support Systems: An Overview 3.1 DSS configurations Strategic planning is one of the most important tasks of modern management. – PowerPoint PPT presentation

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Title: Decision Support Systems:

  • Decision Support Systems
  • An Overview

3.1 DSS configurations
  • Strategic planning is one of the most important
    tasks of modern management. It involves all
    functional areas in an organization and several
    relevant outside factors, a fact that complicates
    the planning process, especially in dealing with
    long-rum uncertainties. Thus, strategic planning
    is clearly not a structured decision situation,
    so it is potential candidate for DSS application.
  • The Gotass-Larsen Shipping Corp. (GLSC),
    subsidiary of International Utilities (IU),
    operates cargo ships all over the world. The
    company developed a comprehensive DSS for
    performing both short-and long-term planning. The
    system is composed of two major parts data and
  • The data include both external data (port or
    cannel characteristics, competitors activities,
    and fares) and

3.1 DSS configurations
  • internal data (existing plans, availability of
    resources, and individual ships
    characteristics). In addition, users can
    incorporate their own data or express their
    attitudes (for example, by adding their own risk
  • The models include routine standard accounting
    and financial analysis model (such as cash flow
    computations and pro forma income and expenses)
    organized on a per ship, per voyage, per
    division, and company-wide basis. These models
    permit elaborate financial analyses. A simulation
    model is used to analyze short- and long-term
    plans and to evaluate the desirability of
    projects. In addition, the system interfaces with
    a commercially available application program for
    analyzing individual voyages.

3.1 DSS configurations
  • A highly decentralized 15 month operational
    planning and control document is prepared within
    the framework of the long-term strategic plan.
    This document is used as the basis for detailed
    goal formation for the various ships and
    individual voyages. A detailed monitoring and
    control mechanism is also provided, including a
    regular variance report and diagnostic analysis.
    In addition, a detailed performance tracking
    report is executed (by voyage, ship, division,
    and entire corporation).
  • Once the assessment of the opportunity of
    individual projects (such as contracting a
    specific voyage) is examined, an aggregation is
    performed. The objective is to determine whether
    a series of individually profitable project

3.1 DSS configurations
  • Adds to feasible and effective long-range plan.
    The DSS uses a simulation model that examines
    various configurations of projects in an attempt
    to fine-tune the aggregate plan. Specifically,
    when several projects are selected, resources
    might be insufficient for all projects.
    Therefore, modifications in scheduling and
    financial arrangements might be necessary. This
    fine-tuning provides a trial-and-error approach
    to feasibility testing and sensitivity analyses.
    The what-if capabilities of the DSS are
    especially important in this case because a
    trial-and-error approach to managing the
    organization would be disastrous. The strategic
    plan of GLSC is very detailed and accurate
    because of the contractual nature of the sales
    and some of the expenses. The model is geared to
    a traditional business policy structure, which
    helps in assessing the threats and risks in

3.1 DSS configurations
  • The general operating environment and makes
    possible an examination of the impacts of new
    opportunity on existing plans.
  • This is an example of large-scale, strategic DSS.
    We refer to this vignette throughout this

3.1 DSS configurations
  • Supports individuals and teams
  • Used repeatedly and constantly
  • Two major components data and models
  • It supports several interrelated decisions
  • Web-based
  • It uses both internal and external data
  • 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

3.1 DSS configurations
  • This vignette demonstrates some of the potential
    diversification of DSS. Decision support can be
    provided in many different configurations. These
    configurations depend on the nature of the
    management decision situation and the specific
    technologies used for support. These technologies
    are assembled from four basic components (each
    with several variations) data, models,
    knowledge, and user interface. Each of these
    components is managed by software that either is
    commercially available or must be programmed for
    the specific task. The manner in which these
    components are assembled defines their major
    capabilities and the nature of the support
    provided. For example, models are emphasized in a
    model-oriented DSS, as in the opening vignette.
    Such models can be customized with a modeling
    language (such as spreadsheet) or can be provided
    by standard algorithm-based tools such as linear

3.2 What is a DSS
  • programming. Similarly, in a data-oriented DSS,
    a database (or data warehouse) and its management
    play the major role.
  • DSS definitions
  • We have defined the DSS in chapter 1 like this
  • Decision support systems couple the intellectual
    resources of individuals with the capabilities of
    the computer to improve the quality of decisions.
    It is a computer based support system for
    management decision makers who deal with
    semistructured problems (Keen and Morton,1987).
  • Why do we redefine it in this chapter?

3.2 What is a DSS
  • Why do we redefine it in this chapter?
  • Keen and Mortons definition is identified as a
    system intended to support managerial decision
    makers in semistructured decision situations. DSS
    were meant to be an adjunct to decision makers,
    to extend their capabilities but not to replace
    their judgment. It is a computer-based system.
  • Several other definitions appeared that caused
    considerable disagreement as to what really is a

3.2 What is a DSS
  • 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
    problem-solving capabilities.
  • Alter (1980) Contrasts DSS with traditional
    EDP(electronic data processing) systems (Table

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  • Moore and Chang (1980)
  • 1. Extendible systems
  • 2. Capable of supporting ad hoc data analysis and
  • decision modeling
  • 3. Oriented toward future planning
  • 4. 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 components
  • 2. A knowledge system
  • 3. A problem-processing system--the link between
    the other two components

  • 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
  • These definitions are compared and contrasted by
    examining the various concepts used to define

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3.2 What is a DSS
  • Working Definition of DSS
  • 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
  • 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

3.3 Characteristics and Capabilities of DSS
  • 1. Provide support in semi-structured and
    unstructured situations, includes human judgment
    and computerized information
  • 2. Support for various managerial levels (top to
    line manager)
  • 3. Support to individuals and groups
  • 4. Support to interdependent and/or sequential
  • 5. Support all phases of the decision-making
  • 6. Support a variety of decision-making processes
    and styles
  • (more)

  • 7. Are adaptive
  • 8. Have user friendly interfaces
  • 9. Goal improve effectiveness of decision
  • 10. The decision maker controls the
    decision-making process
  • 11. End-users can build simple systems
  • 12. Utilizes models for analysis
  • 13. Provides access to a variety of data sources,
    formats, and types, ranging from geographic
    information systems to object-oriented
    ones.Decision makers can make better, more
    consistent decisions in a timely manner.

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3.4 DSS Components
  • 1. Data Management Subsystem
  • Includes the database, which contains relevant
    data for the situation and is managed by software
    called DBMS.
  • 2. Model Management Subsystem
  • A software package that includes financial,
    statistical, management science, or other
    quantitative models that provides the systems
    analytical capabilities and appropriate software
    management. Modeling language for building custom
    models are also included. This is often called a

3.4 DSS Components
  • 3. Knowledge-based (Management) Subsystem
  • This subsystem can support any of the other
    subsystems or act as an independent component. It
    provides intelligence to augment the decision
    makers own.
  • 4. User Interface Subsystem
  • The user communicates with and commands the DSS
    through this subsystem.
  • 5. The User
  • is considered to be part of system. Researchers
    assert that some of the unique contributions of
    DSS are derived from the intensive interaction
    between the computer and the decision makers.

Other Computer-based systems
Data external and internal
Data Management
Model Management
Knowledge Management
User Interface
Manager (user)
3.5 The Data Management Subsystem
  • DSS database
  • A database is a collection of interrelated data
    organized to meet the need and structure of an
    organization and can be used by more than one
    person for more than one application. There are
    several possible configurations for a database.
    For lager DSS, the database is basically included
    in the data warehouse (next chapter). For some
    applications, a special database is constructed
    as needed. Several databases may be used in one
    DSS application, depending on the data sources.
    Data is extracted from internal and external data
    sources, as well as personal data belonging to
    one or more users. (Figure 3.3)

Internal Data Source
External Data Source
Organizational knowledge base
Private, personal data
Decision Support database or data
Query Facility
Corporate data Warehouse
  • DBMS
  • Retrieval
  • Inquiry
  • Update
  • Report
  • Delete

Interface Management
Model Management
Data Directory
Knowledge Management
Internal Data Source
External Data Source
Organizational knowledge base
Private, personal data
Decision Support database or data
Query Facility
Corporate data Warehouse
  • DBMS
  • Retrieval
  • Inquiry
  • Update
  • Report
  • Delete

Interface Management
Model Management
Data Directory
Knowledge Management
3.5 The Data Management Subsystem
Internal data come mainly from the organizations
transaction processing system. Example are
payroll monthly. Other internal data are machine
maintenance scheduling, forecasts of future
sales, cost of out-of-stock items, and future
hiring plans. Some times internal data are made
available through Web browser over an Internet,
an internal Web-based system. External data may
include industry data, marketing research data,
census data, regional employment data, government
regulations, tax rate schedules, or national
economic data. Internet also is an important data
sources. Private data may include guidelines used
by specific decision makers and assessment of
specific data or situations.
3.5 The Data Management Subsystem
Data Organization Should a DSS have an
independent database? It depends. In small ad hoc
DSS, data can be entered directly into models
sometimes extracted directly from larger
database. The organizations data warehouse is
often used for building DSS applications. Some
large DSS have their own fully integrated,
multiple-source DSS databases. A separate DSS
database need not be physically separate from the
corporate database. They can be physically stored
together for economic reasons. A DSS database can
also share a DBMS with other systems. A DSS
database may include multimedia objects (such as
pictures, maps, or sounds). An object-oriented
database is found in some recent DSS.
3.5 The Data Management Subsystem
Extraction To create a DSS database, or a data
warehouse, it is often necessary to capture data
from several sources. This operation is called
extraction. It is basically the importing of
files, summarization, filtration, and
condensation of data. Extraction also occurs when
the user produces reports from the data in the
DSS database. The extraction process is managed
by a DBMS.
3.5 The Data Management Subsystem
  • Database management system
  • The data base is created, accessed, and updated
    by a DBMS.
  • Most DSS are built with a standard commercial
    DBMS that provides capabilities such as those
    shown in the following list
  • Captures/extracts data for inclusion in a DSS
  • Updates (adds, deletes, edits, changes) data
    records and files
  • Interrelates data from different sources

3.5 The Data Management Subsystem
  • Retrieves data from the database for queries and
  • Provides comprehensive data security (protection
    from unauthorized access, recovery capabilities,
  • Handles personal and unofficial data so that
    users can experiment with alternative solutions
    based on their own judgment
  • Performs complex data manipulation tasks based on
  • Tracks data use within the DSS
  • Manages data through a data dictionary

3.5 The Data Management Subsystem
  • Data directory
  • Data directory is a catalog of all the data in
    the database. It contains the data definitions,
    and its main function is to answer questions
    about the availability of data items, their
    source, and their exact meaning. The directory
    especially appropriate for supporting the
    intelligence phase of the decision-making process
    by helping scan data and identify problem areas
    or opportunities. The directory, like any other
    catalog, supports the addition of new entries,
    deletion of entries, and retrieval of information
    on specific object.
  • Query facility In building and using DSS, it is
    often necessary to access, manipulate, and query
    the data. The Query facility performs all these
    tasks. It accepts requests for data from other DSS

3.5 The Data Management Subsystem
  • Query facility components, determine how these
    requests can be filled, formulates the detailed
    requests, and returns the results to the issuer
    of the request. The query facility includes a
    special query language. Important functions of a
    DSS query system are the selection and
    manipulation operations.

3.5 The Data Management Subsystem
  • Data Management Issues
  • Data warehouse
  • Data mining
  • Special independent DSS databases
  • Extraction of data from internal, external, and
    private sources
  • Web browser data access
  • Web database servers
  • Multimedia databases
  • Special GSS databases (like Lotus Notes / Domino
  • Online Analytical Processing (OLAP)
  • Object-oriented databases
  • Commercial database management systems (DBMS)

3.6 The Model Management Subsystem
  • Analog of the database management
    subsystem(Figure on next slide )
  • Model base
  • Model base management system
  • Modeling language
  • Model directory
  • Model execution, integration, and command

3.6 The Model Management Subsystem
  • Models (Model Base)
  • Stratigic, tactical,operational
  • Statistical, Financial, marketing,MS, Accounting,
  • Model building blocks

Model Directory
  • Model Base Management
  • Modeling commands creation
  • Maintenanceupdate
  • DB interface
  • Modeling language

Model execution, Integration, and command
Data Management
Interface Management.
Knowledge management
3.6 The Model Management Subsystem
  • Model Base
  • A model base contains routine and special
    statistical, financial, forecasting, management
    science, and other quantitative models that
    provide the analysis capabilities in a DSS. The
    ability to invoke, run, change, combine, and
    inspect models is a key DSS capability that
    differentiates it from other CBIS. The models in
    the model base can be divided into four major
    categories strategic, tactical, operational, and
    model-building blocks and routine.
  • Strategic Models are used to support top
  • strategic planning responsibilities.
    Potential applications include developing
    corporate objectives, planning for mergers and
    acquisitions, plant location selection,environment
    al impact analysis, and nonroutine capital

3.6 The Model Management Subsystem
  • Mostly external data are used. The GLSC
    opening vignette includes a long-range planning
  • Tactical Models are used mainly by middle
    management to assist in allocating and
    controlling the organizations resources.
    Examples of tactical models include labor
    requirement planning, sales promotion planning,
    plant layout determination, and routine capital
    budgeting. Tactical models are usually applicable
    only to an organizational subsystem such as the
    accounting department. Their time horizon varies
    from 1 month to less than 2 years. Some external
    data are needed, but the greatest requirements
    are for internal data. The GLSC vignette includes
    some tactical models for its 15 months plan.

3.6 The Model Management Subsystem
  • Operational Models are used to support the
    day-to-day working activities of the
    organization. Typical decisions are approving
    personal loans by a bank, production scheduling,
    inventory control, maintenance planning and
    scheduling, and quality control. Operational
    models support mainly managers decision making
    with a daily to monthly time horizon. These
    models normally use internal data.
  • The models in the model base can also be
    classified by functional areas ( such as
    financial models or production control models) or
    by discipline (such as statistical model, or
    management science allocation models). The number
    of models in a DSS can vary from a few to several

3.6 The Model Management Subsystem
  • Modeling Language
  • Because DSS deal with semistructured or
    unstructured problems, it is often necessary to
    customize models. This can be done with
    high-level languages. Some examples of these are
    COBOL, with a spreadsheet or with other
    fourth-generation languages, and special modeling
    language such as IFPS/Plus.
  • The Model Base Management System (MBMS)
  • The functions of the model base management
    system (MBMS) software are model creation using
    subroutine and other building blocks, generation
    of new routine and reports, model updating and
    changing, and model data manipulation. The MBMS
    is capable of interrelating models with the
    appropriate linkages through a database.

3.6 The Model Management Subsystem
  • Major Functions of the MBMS
  • Creates models easily and quickly, either from
    scratch or from existing models or from the
    building blocks
  • Allows users to manipulate the models so they can
    conduct experiments and sensitivity analysis
    ranging from what-if to goal seeking.
  • Stores, retrieves, and manages a wide variety of
    different types of models in a logical and
    integrated manner
  • Accesses and integrates the model building blocks
  • Catalogs and displays the directory of models for
    use by several individuals in the organization
  • Tracks model data and application use
  • Interrelates models with appropriate linkages
    with the database and integrates them within the

3.6 The Model Management Subsystem
  • Major Functions of the MBMS
  • Manages and maintains the model base with
    management functions analogous to database
    management store, access, run, update link
    ,catalog, and query
  • Uses multiple models to support problem solving
  • The Model Directory
  • The role of the model directory is similar to
    that of a database directory. It is a catalog of
    all the models and other software in the model
    base. It contains the model definitions, and its
    main function is to answer questions about the
    availability and capability of the models.

3.6 The Model Management Subsystem
  • Model Execution, Integration, and Command
  • The following activities are usually
    controlled by model management
  • Model execution is the process of controlling the
    actual running of the model.
  • Model integration means combining the operations
    of several models when needed (such as directing
    the output of one model to be processed by
    another one).
  • A model command processor is used to accept and
    interpret modeling instructions from the dialog
    component and to rout them to the MBMS, the model
    execution, or the integration functions.

3.6 The Model Management Subsystem
  • Model Management Issues
  • Model level Strategic, managerial (tactical),
    and operational
  • Modeling languages
  • Lack of standard MBMS activities. WHY?
  • Use of AI and fuzzy logic in MBMS

3.7 The Knowledge Based (Management) Subsystem
  • Provides expertise in solving complex
    unstructured and semi-structured problems
  • Expertise provided by an expert system or other
    intelligent system
  • Advanced DSS have a knowledge based (management)
    component that can provide the required expertise
    for solving some aspects of problem and providing
  • Silverman 1995 suggested that
  • knowledge-based decision aids (support
    unaddressed problem with mathematics)
  • Intelligent decision modeling systems (build,
    apply and manage libraries of models)
  • Decision analytic expert systems (integrate
    theoretically rigorous methods of uncertainty
    into the expert system knowledge bases)

3.7 The Knowledge Based (Management) Subsystem
  • Knowledge-based DSS can be called intelligent
    DSS, or DSS/ES, expert support system, or simply
    knowledge-based DSS.
  • Data mining application can be one of them.

Knowledge worker
I access services Authentication Translation
and transformation for diverse applications and
appliances (e.g., browser, PIM, file system,
PDA, mobile phone
II Personalization service Personalized
knowledge portals profiling push-service
process- project- or role-oriented knowledge

III Knowledge service
Collaboration Skill/expertise mgmt, community
space, experience mgmt, awareness mgmt.
Discovery Search, mining, knowledge maps,
navigation, visualization
Publication Formats, structuring,
contextualization, workflow, o-authoring
Learning Authoring, course mgmt, tutoring,
learning paths, examinations
IV Integration service Taxonomy, knowledge
structure, ontology multi-dimensional metadata
(tagging) directory services synchronization
V Infrastructure service Intranet infrastructure
service groupware services extract
transformation loading inspection service.
VI- data and knowledge source
(1) Intranet/extranet, Messages, contents of CMS,
e-learning platforms (2) DMS documents, files
from office information systems, (3) Data from
RDMS, TPS, data warehouse, (4) Personal
information management data (5) Content from
Internet, WWW, newsgroups (6) Data from external
online database
3.8 The User Interface (Dialog) Subsystem
  • Includes all communication between a user and the
  • Graphical user interfaces (GUI)
  • Voice recognition and speech synthesis possible
  • To most users, the user interface is the system
  • Management of the User Interface Subsystem

3.8 The User Interface (Dialog) Subsystem
  • Management of the User Interface Subsystem
  • This subsystem is managed by software called
    the user interface management system(UIMS)
  • UIMS capabilities
  • Provides graphical user interface
  • Accommodates the user with a variety of input
  • Presents data with a variety of formats and
    output devices
  • Gives users help capabilities, prompting,
    diagnostic and suggestion routines, or any other
    flexible support
  • Provides interactions with the database and the
    model base
  • Stores input and output data

3.8 The User Interface (Dialog) Subsystem
  • UIMS capabilities
  • Provides color graphics, three-dimensional
    graphics, and data plotting
  • Has windows to allow multiple functions to be
    displayed concurrently
  • Can support communication among and between users
    and building of MSS
  • Provides training by example (guiding user
    through the input and modeling process)
  • Provides flexibility and adaptiveness so the MSS
    can accommodate different problems and
  • Interacts in multiple, different dialog styles
  • Captures, stores, and analyzes dialog usage to
    improve the dialog system

3.8 The User Interface (Dialog) Subsystem
  • The User Interface Process
  • Figure 3.5 shows the process for an MSS. The
    user interacts with the computer via an action
    language processed via the UIMS. In advanced
    system the user interface component includes a
    natural language processor or may use standard
    objects (such as poll-down menu and buttons)
    through a graphical user interface (GUI). The
    UIMS provides the capabilities listed in DSS in
    Focus 3.5 and enables the user to interact with
    the model management and data management

Data Management and DBMS
Knowledge Management
Model Management and MBMS
User Interface Management Sys. UIMS
Natural Language Processor.
Action Language
Display Language
Printers, Plotters
3.9 The User
  • The person faced with the decision that the
    MSS is designed to support is called the user,
    the manager, or the decision maker
  • Two board classes
  • Managers
  • Staff specialists e.g. Financial analysts,
    production planners, etc.
  • Intermediaries the connectors between manager
    and DSS 1. Staff assistant 2. Expert tool
    user 3. Business (system) analyst 4. GDSS
  • In detail,

3.9 The User
  • 1. Staff assistant has specialized knowledge
    about management problems and some experience
    with decision support technology
  • 2. Expert tool user is skilled in the
    application of one or more types of specialized
    problem-solving tools.
  • 3. Business (system) analyst has a general
    knowledge of the application area, a formal
    business administrator education (not computer
    science), and considerable sill in DSS
    construction tools.
  • 4. GDSS Facilitator controls and coordinates the
    software of GDSS.

3.10 DSS Hardware
  • Evolved with computer hardware and
  • software technologies
  • Major Hardware Options
  • Mainframe
  • Workstation
  • Personal computer
  • Web server system
  • Internet
  • Intranets
  • Extranets

3.11 Distinguishing DSS from Management Science
and MIS
  • MIS can be viewed as an IS infrastructure that
    can generate standard and exception reports and
    summaries, provide answers to queries, and help
    in monitoring and tracking. It is usually
    organized along functional areas. Thus, there are
    marketing MIS, accounting MIS, and so on. A DSS,
    on the other hand, is basically a problem-solving
    tool and it is often used to address ad doc and
    unexpected problems. MIS is usually developed by
    the IS department because of its permanent
    infrastructure nature. DSS is usually and
    end-user tool it can provide decision support
    within a short time. An MIS can provide quick
    decision support only to situations for which the
    models and software were prewritten.

3.11 Distinguishing DSS from Management Science
and MIS
  • Because of its unstructured nature, DSS is
    usually developed by a prototype approach. MIS,
    on the other hand, is often developed by a
    structured methodology such as the system
    development life cycle (SDLC)
  • A DSS can evolves as the decision maker learn
    more about the problem. Often managers cannot
    specify in advance what they want from computer
    programmers and model builders. Many computerized
    applications are developed in a way that requires
    detailed specifications to be formalized in
    advance. This requirement is not reasonable in
    many semistructured and unstructured
    decision-making tasks.

3.11 Distinguishing DSS from Management Science
and MIS
  • The Major characteristics of MIS, MS, and DSS
  • MIS
  • The main impact has been on structured tasks,
    where standard operating procedures, decision
    rules, and information flows can be reliably
  • The main payoff has been in improving efficiency
    by reducing costs, turnaround time, and so on,
    and by replacing clerical personnel.
  • The relevance for managers decision making has
    mainly been indirect ( for example, by report and
    access the data)
  • MS/OR
  • The impact has mostly been on structured problems
    (rather than tasks), where the objective, data,
    and constraints can be pre-specified.

3.11 Distinguishing DSS from Management Science
and MIS
  • The payoff has been in generating better
    solutions for given types of problems.
  • The relevance for manager has been the provision
    of detailed recommendations and new methods for
    handling complex problems.
  • DSS
  • the impact is on decisions in which there is
    sufficient structure for computer and analytic
    aids to be of value but where the managers
    judgment is essential.
  • The payoff is in extending the range and
    capability of managers decision processes to
    help them improve their effectiveness

3.11 Distinguishing DSS from Management Science
and MIS
  • The relevance for manager is the creation of a
    supportive tool, under their own control, that
    dose not attempt to automate the decision
    process, predefine objectives, or impose
  • Example, Marketing DSS.

Standard Statistical Models
Regresssion analysis Factor analysis Cluster
analysis Discriminant analysis
Marketing data
Marketing model
Sales Reports Market reports Government reports
Media Mix Site Location Advertising
budget Product Design .
Marketing recommendations evaluation
Standard MS Models
Linear Programming Markov analysis Decision
table Inventory
User Interface
3.12 DSS Classifications
  • Alters Output Classification (1980)
  • Degree of action implication of system
    outputs (supporting decision) (Table 3.4)

(No Transcript)
3.12 DSS Classifications
  • Holsapple and Whinstons Classification 1996
  • 1. Text-oriented DSSInformation (including
    data and knowledge) is often stored in a textual
    format and must be accessed by the decision
    makers. The amount of information to be searched
    by the decision makers is exponentially growing.
    Therefore, it is necessary to represent and
    process text documents and fragments effectively
    and efficiently.
  • A text-oriented DSS supports a decision maker by
    electronically keeping track of texually
    represented information that could have a bearing
    on decisions. It allows documents to be
    electronically created, revised, and viewed as
    needed. Information technologies such as document
    imaging, hypertext, and intelligent agents can be
    incorporated into the text-oriented DSS
    application. New Web-based systems are
    revolutionizing the development and deployment of
    text-oriented DSS.

3.12 DSS Classifications
  • 2. Database-oriented DSS
  • Database plays a major role in the DSS structure.
    Rather than being treated as streams of text,
    data are organized in highly structured format
    (relational or objective-oriented). The early
    generations of database-oriented DSS used mainly
    the relational database configuration. The
    information handled by relational databases tends
    to be voluminous, descriptive, and rigidly
    structured. Database-oriented DSS features strong
    report generation and query capability
  • 3. Spreadsheet-oriented DSSA spreadsheet is a
    modeling language that allows the user to write
    models to execute DSS analysis. These not only
    create, view, and modify procedural knowledge,
    but also

3.12 DSS Classifications
  • instruct the system to execute their
    self-contained instructions. Spreadsheets are
    widely used in end-user developed DSS. The most
    popular end-user tools for developing DSS are
    Microsoft Excel and Lotus 1-2-3, both of which
    are spreadsheets.
  • Because package such Excel can include a
    rudimentary DBMS, or can readily interface with
    one, such as Access, they can handle some
    properties of a database-oriented DSS, especially
    the manipulation of descriptive knowledge. Some
    spreadsheet development tools include what-if
    analysis and goal-seeking capabilities and they
    are revisted in Chapter 5.

3.12 DSS Classifications
  • 4. Solver-oriented DSS
  • A solver is an algorithm or procedure written as
    a computer program for performing certain
    computation for solving a particular problem
    type. Examples of a solver can be an economic
    order quantity procedure for calculating an
    optimal ordering quantity or a linear regression
    routine for calculating trend.
  • 5. Rule-oriented DSSThe knowledge component of
    DSS described earlier includes both procedural
    and inferential (reasoning) rules, often an
    expert system. These rules can be qualitative or
    quantitative. This application of artificial
    intelligence is describes in Chapter 6.

3.12 DSS Classifications
  • 6. Compound DSS
  • A compound DSS is a hybrid system that includes
    two or more of the five basic structured
    described above. A compound DSS can be built by
    using a set of independent DSS, each specializing
    in one area. A compound DSS can also build as a
    single, tightly integrated DSS.

3.13 Other Classifications
  • Institutional DSS vs. Ad Hoc DSS
  • Institutional DSS deals with decisions of a
    recurring nature
  • Example, a portfolio management system, GLSC
  • Ad Hoc DSS deals with specific problems that are
    usually neither anticipated nor recurring.
  • Often involving strategic planning and
    sometimes management control problems.

3.13 Other Classifications
  • Degree of nonprocedurality (Bonczek et al., 1980)
  • BASIC and COBOL language called procedure
    language, most non-procedure language is used in
    DSS building. This non-procedure language is four
    generation language (4GL)
  • Personal, group, and organizational support
    (Hackathorn and Keen, 1981)
  • Individual versus group support systems (GSS)
  • Custom-made versus ready-made systems
  • Ready-made systems Some organizations such as
    school , hospital, banks etc. have similar
    problems to be solved. Building a DSS can be used
    (mirror modification) in several organizations.
    Such DSS called ready-made systems.

Individual Assignment
  • 1. How to distinguish DSS from MS and MIS?
  • 2. According to Alter 1980, how to Classify
  • 3. What are the relationships and distinguishes
    between Alters 1980 classification and
    Holsapple and Whinstons 1996 classification
    about DSS?
  • 4. What components does a DSS have? Briefly
    describing functions of each component in DSS.
  • What are the model-oriented DSS and data-oriented

Group Assignment (option)
Design a DSS framework for the case of this
chapter open vignette, that is Gotaas-Larsen
Shipping Gorp.
Individual Assignment
  • 3. What are the relationships and distinguishes
    between Alters 1980 classification and
    Holsapple and Whinstons 1996 classification
    about DSS?
  • Distinguish Alters classification is based the
    degree of action implication of system output,
    or system output can directly support (or
    determine the decision). HP classification is
    based on the applications or processing
  • Relationships According to Alters
    classification, there are seven categories the
    first two types are data oriented, performing
    data retrieval or analysis, which is correspond
    to the HPs Text- and Database-oriented DSS the
    third deals with data and models (HPdatabase,
    Spreadsheet). The reminders are model oriented,
    providing either simulation capabilities,
    optimization, or computations that suggest an
    answer (solver- and rule-oriented DSS). Not every
    DSS fits neatly into a single classification
    system. Some have equally strong data and
    modeling orientation