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CH03 withFigures

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    Learni ng Obj ectiv es Understand possible decision support system

    (DSS) configurations

    Understand the key differences and similarities

    between DSS and business intelligence (BI)systems

    Describe DSS characteristics and capabilities

    Understand the essential definition of DSS

    Understand DSS components and how they

    integrate

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    Learni ng Obj ectiv es Describe the components and structure

    of each DSS component: the datamanagement subsystem, the model

    management subsystem, the userinterface (dialog) subsystem, theknowledge-based managementsubsystem, and the user

    Explain Internet impacts on DSS and viceversa

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    DSS Confi gurat ions 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

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    DSS Confi gurat ions These technologies are assembled from

    four basic components (each with several

    variations and are typically deployed over

    the Web) Data

    Models

    User interface Knowledge (optional)

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    DSS Descri pti on DSS application

    A DSS program built for a specificpurpose (e.g., a scheduling system for a

    specific company) Business intelligence (BI)

    A conceptual framework for decision

    support. It combines architecture,databases (or data warehouses),analytical tools, and applications

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    DSS Descri pti on A DSS supports all phases of decision

    making and may include a knowledge

    component

    A DSS can be used by a single user on

    a PC or can be Web-based for use by

    many people at several locations

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    DSS C haracteri st icsand Capabi liti es

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    DSS Descri pti on

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    DSS Characteri st icsand Capabi lit ies Business analytics

    The application of models directly to

    business data. Business analytics

    involves using DSS tools, especially

    models, in assisting decision makers. It

    is essentially OLAP/DSS. See also

    business intelligence (BI).

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    DSS Characteri st icsand Capabi lit ies Predictive analytics

    A business analytical approach toward

    forecasting (e.g., demand, problems,

    opportunities) that is used instead of

    simply reporting data as they occur

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    DSS Characteri st icsand Capabi lit ies The key characteristics and capabilities of

    DSS

    Support for decision makers, mainly in

    semistructured and unstructured situations,by bringing together human judgment and

    computerized information

    Support for all managerial levels, rangingfrom top executives to line managers

    Support for individuals as well as groups

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    DSS Characteri st icsand Capabi lit ies Support for interdependent and/or sequential

    decisions

    Support in all phases of the decision-making

    process Support for a variety of decision-making processes

    and styles

    DSS are flexible, so users can add, delete,

    combine, change, or rearrange basic elements;

    DSS can be readily modified to solve other, similar

    problems

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    DSS Characteri st icsand Capabi lit ies User-friendliness, strong graphical capabilities,

    and a natural language interactive human

    machine interface can greatly increase the

    effectiveness of DSS Improved effectiveness of decision making

    The decision maker has complete control over all

    steps of the decision-making process in solving aproblem

    End users are able to develop and modify simple

    systems by themselves

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    DSS Characteri st icsand Capabi lit ies Models are generally utilized to analyze decision-

    making situations

    Access is provided to a variety of data sources,

    formats, and types Can be employed as a standalone tool used by an

    individual decision maker in one location ordistributed throughout an organization and in

    several organizations along the supply chain Can be integrated with other DSS and/or

    applications, and it can be distributed internallyand externally, using networking and Web

    technologies

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    Com ponents of DSS

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    Com ponents of DSS Database management system (DBMS)

    Software for establishing, updating, and querying(e.g., managing) a database

    Data warehouse

    A physical repository where relational data areorganized to provide clean, enterprise-wide datain a standardized format

    Database

    The organizing of files into related units that arethen viewed as a single storage concept. Thedata in the database are generally made availableto a wide range of users

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    Com ponents of DSS Model management subsystem

    Model base management system (MBMS)

    Software for establishing, updating, combining,

    and so on (e.g., managing) a DSS model base

    User interface

    The component of a computer system that

    allows bidirectional communication between

    the system and its user

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    Data ManagementSub system The data management subsystem is

    composed of

    DSS database

    DBMS

    Data directory

    Query facility

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    Data ManagementSubsystem

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    The Database

    Internal data come mainly from the organizations

    transaction processing system

    External data include industry data, marketresearch 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

    Data ManagementSub system

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    Data ManagementSubsystem Data organization

    Data extraction

    The process of capturing data from several

    sources, synthesizing them, summarizing

    them, determining which of them are relevant,

    and organizing them, resulting in their

    effective integration

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    Data ManagementSubsystem Database management system (DBMS)

    Software for establishing, updating, andquerying (e.g., managing) a database

    Query FacilityThe (database) mechanism that acceptsrequests for data, accesses them,

    manipulates them, and queries them Directory

    Acatalog of all the data in a database or all

    the models in a model base

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    Data ManagementSub system Key database and database management

    system issues

    Data quality

    Data integration

    Scalability

    Data security

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    The Model M anagementSubsys tem

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    Model base

    A collection of preprogrammed quantitative

    models (e.g., statistical, financial,

    optimization) organized as a single unit

    The Model M anagementSubsys tem

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    Four categories of models with the model

    base

    Strategic models

    Tactical models

    Operational models

    Analytical models

    The Model M anagementSubsys tem

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    Strategic models

    Models that represent problems for the

    strategic level (i.e., executive level) of

    management

    Tactical models

    Models that represent problems for the

    tactical level (i.e., midlevel) of management

    The Model M anagementSubsys tem

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    Operational models

    Models that represent problems for the

    operational level of management

    Analytical models

    Mathematical models into which data are

    loaded for analysis

    The Model M anagementSubsys tem

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    Model building blocks and routines

    Model building blocks

    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

    Model components for building DSS Modeling tools

    The Model M anagementSubsys tem

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    Model base management system: MBMS

    software has four main functions

    Model creation, using programming languages,

    DSS tools and/or subroutines, and other buildingblocks

    Generation of new routines and reports

    Model updating and changing Model data manipulation

    The Model M anagementSubsys tem

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    Model directory

    Model execution is the process of controlling the

    actual running of the model

    Model integration involves combining theoperations of several models when needed

    A model command processoris used to accept

    and interpret modeling instructions from the userinterface component and route them to the

    MBMS, model execution, or integration functions

    The Model M anagementSubsys tem

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    Use r In terface ( Dialog)Subsys tem User interface

    The component of a computer system that

    allows bidirectional communication between

    the system and its user.

    User interface management system (UIMS)

    The DSS component that handles all

    interaction between users and the system

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    Use r In terface ( Dialog)Subsys tem The user interface process

    Object

    A person, place, or thing about which information

    is collected, processed, or stored Graphical user interface (GUI)

    An interactive, user-friendly interface in which, by

    using icons and similar objects, the user cancontrol communication with a computer

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    Use r In terface (Dialog)Subsys tem

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    Use r In terface ( Dialog)Subsys tem DSS user interfaces access is provided

    through Web browsers including:

    Voice input and output

    Portable devices

    Direct sensing devices

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    Use r In terface ( Dialog)Subsys tem DSS developments

    Parallel processing hardware and software

    technologies have made major inroads in solving

    the scalability issue Web-based DSS have made it easier and less

    costly to make decision-relevant information and

    model-driven DSS available to users in

    geographically distributed locations, especially

    through mobile devices

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    Use r In terface ( Dialog)Subsys tem DSS developments

    Artificial intelligence continues to make inroads in

    improving DSS

    Faster, intelligent search engines Intelligent agents promise to improve the interface in

    areas such as direct natural language processing and

    creating facial gestures

    The development of ready-made (or near-ready-made) DSS solutions for specific market

    segments has been increasing

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    Use r In terface ( Dialog)Subsys tem DSS developments

    DSS is becoming more embedded in or linked to

    most EIS

    GSS improvements support collaboration at theenterprise level

    Different types of DSS components are being

    integrated more frequently

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    Know ledge- Bas edManagem ent Subsy stem Advanced DSS are equipped with a

    component called a knowledge-based

    management subsystem that can supply the

    required expertise for solving some aspects ofthe problem and provide knowledge that can

    enhance the operation of other DSS

    components

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    The Us er The person faced with a decision that an MSS

    is designed to support is called the user, the

    manager,or the decision maker

    MSS has two broad classes of users:managers and staff specialists

    Staff specialists use the system much more

    frequently than manager and tend to be moredetail-oriented

    Staff analysts are often intermediaries between

    managers and the MSS

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    The Us er Intermediary

    A person who uses a computer to fulfill

    requests made by other people (e.g., a

    financial analyst who uses a computer toanswer questions for top management)

    Staff assistant

    An individual who acts as an assistant to amanager

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    The Us er Expert tool user

    A person who is skilled in the application of one ormore types of specialized problem-solving tools

    Business (system) analysts

    An individual whose job is to analyze businessprocesses and the support they receive (or need)from information technology

    Facilitators (in a GSS)A person who plans, organizes, and electronicallycontrols a group in a collaborative computingenvironment

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    DSS Har dware Hardware affects the functionality and usability of

    the MSS

    The choice of hardware can be made before, during,

    or after the design of the MSS software Major hardware options:

    Organizations servers

    Mainframe computers with legacy DBMS,

    Workstations

    Personal computers

    Client/server systems

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    DSS C lassi fi cati ons AIS SIGDSS classification for DSS

    Communications-driven and group DSS (GSS)

    Data-driven DSS

    Document-driven DSS

    Knowledge-driven DSS, data mining, and

    management ES applications

    Model-driven DSS Compound DSS

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    DSS C lassi fi cati ons Holsapple and Whinstons classification

    Text-oriented DSS

    Database-oriented DSS

    Spreadsheet-oriented DSS

    Solver-oriented DSS

    Rule-oriented DSS

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    DSS C lassi fi cati ons Alters output classification

    Data File drawer systems

    Data analysis systems

    Data or models Analysis information systems

    Models

    Accounting models Representational models

    Optimization models

    Suggestion models

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    DSS C lassi fi cati ons Other DSS categories

    Institutional DSS

    A DSS that is a permanent fixture in an

    organization and has continuing financial support.It deals with decisions of a recurring nature

    Ad hoc DSS

    A DSS that deals with specific problems that areusually neither anticipated nor recurring

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    DSS C lassi fi cati ons Other DSS categories

    Personal support

    Group support

    Organizational support

    Group support system (GSS)

    Information systems, specifically DSS, that

    support the collaborative work of groups Custom-made systems versus ready-made

    systems


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