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Good Information is essential for fact-baseddecision-making
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A Brief History of Decision Support Systems
A Conceptual Perspective
Management Information
DSS versus MIS TPS versus DSS
Examples of DSS Applications
Five Main Categories of DSS
Building Decision Support Systems
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Prior to the mid-1960s
it was not cost effective to build large-scale information systems.
The first Management Information Systems (MIS) were developed at about
that time in large companies.
MISfocused on providing managers with structured, periodic reports.
Much of the information was from accountingand transactionsystems.
Late 1960s
a new type of information system became practicalmodel-oriented DSSor
management decision systems.
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1978
Peter Keen and Charles Stabell, claim the
concept of decision support evolved from :
Theoretical studies of organizational decision
making, (Carnegie Institute of Technology (1950s and60s ).
Interactive computer systems, (MassachusettsInstitute of Technology in the 1960s).
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1981 Bonczek, Holsapple, and Whinston:
They identified four components common to all DSS:
Language system (LS) - all messages the DSS can accept Presentation system (PS) - all messages the DSS can emit
Knowledge system (KS) -- all knowledge the DSS has storedand retained
Problem-processing system (PPS) -- the "software engine"that tries to recognize and solve problems during use of the
DSS.
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Mid-1980s
, doctoral Dissertation (MIT)
Group and organizational decision-making.
o Executive Information Systems(EIS).
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o and
(OLAP).
o
o
o
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.
and
.
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Decision Support Systems.
Interactive information systems that uses dataandmodelsto help managers analyze semi-structured
problems. They can support operations, financial management
and strategicdecision-making.
DSS can be designed to support decision-makers at
any levelin an organization.
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Designed forspecifictypes of organizations likehospitals, banksorinsurancecompanies.
DSS are both off-the-shelfand customdesignedsystems.
DSS may support a small groupof managers using asingle personal computer or a large groupof managersin a networked client-server environment.
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Based on Steven Alter(1980) :
1. DSS are designed specifically to facilitatedecision processes,
2. DSS should support rather than automatedecision making, and
3. DSS should be able to respond quickly to the
changing needsof decision makers.
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Managers need both detailed transaction data andsummarized data in several formats and timing.
Most managers only want summaries of transactions.
Others usually want lots ofchartsand graphs; a few only want
tables of numbers. Many managers want information provided routinely or
periodically.
There are want information available on-lineand on-demand.
Managers want financial analysesand some managers wantprimarily "soft", non-financialorqualitative information.
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Information must be both and .
These characteristics mean the information is up-to-date andavailable when managers want it.
Also, information must be , and .
Finally, managers want information presented in a thatassists them in making decisions.
, management information should be and and any support system should have an option
for managers to information.
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it refers to a management reporting system that
provides periodic, structured paper-based reports.
Data-Driven are intended to be interactive, real-time systems that
are responsive to unplanned as well as plannedinformation requests and reporting needs.
Model-Driven are usually focused on modeling a specific decision or a
set of related decisions.
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Are designed to and processing, record keeping, and simple business reportingof transactions.
Are designed to and decision
implementation.
Transaction processing is to the design of DSSbecause transaction databases often provide data fordecision-oriented reporting systems and data warehouses.
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Major airlines have DSS used by analysts for manytasks including pricingand route selection.
Many companies have DSS that aid in corporateplanningand forecasting.
Monsanto, FedEx and most transportation companies
use DSS forscheduling trucks, airplanesand ships.
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Many manufacturingcompanies use ManufacturingResources Planning (MRP) software.
Foroperational levelDSS supports master productionscheduling, purchasing, and materials requirements
planning.
More recent MRP systems support "what-if" analysis and
simulation capabilities.
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Frito-Layhas a DSS that aids in pricing, advertising,and promotion.
Companies like Wal-Marthave large data warehousesand use data mining software.
On the World-Wide Webone can find DSS that helptrackand manage stock portfolios, choose stocks,plan trips, and suggest gifts.
DSS support distributed decision activities usinggroupware and a corporate intranet.
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Dominant DSScomponent User Groups:Internal, External Purpose:General, Specific EnablingTechnology
CommunicationsCommunications-Driven
DSS
Internal teams, now
expanding
Conduct a meetingBulletin Board
Help users collaborate
Web or Client/Server
DatabaseData-Driven DSS
Managers, staff, now
suppliers
Query a Data Warehouse Main Frame,
Client/Server, Web
Document baseDocument-Driven DSS
Specialists and user
group is expanding
Search Web pages
Find documents
Web
Knowledge baseKnowledge-Driven DSS
Internal users, now
customers
Management AdviceChoose products
Client/Server, Web
ModelsModel-Driven DSS
Managers and staff, now
customers
Crew SchedulingDecision Analysis
Stand-alone PC
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These systems include and,
and , ExecutiveInformation Systems ( ) and Geographic
Information Systems ( ).
are alsoexamples of Data-Driven DSS.
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Access and manipulation oflarge databases ofstructured dataand especially a time-seriesof
internalcompany data and some times externaldata.
Data warehousesystems that allow the manipulation ofdata by computerized tools tailored to a specific task and
setting or by more general tools and operators provide
additional functionality.
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Data-Driven DSS with Online AnalyticalProcessing(OLAP) provide the highest level offunctionality and decision support that is linked
to analysis of large collections of historical data
(cf., Dhar and Stein, 1997).
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Model-Driven DSS, includes systems that useaccountingand financialmodels,
representationalmodels, and optimization
models.
Model-Driven DSS emphasize access to and
manipulation of a model.
Simple statistical and analytical tools provide the
most elementary level of functionality.
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Some OLAP systems that allow complex analysis ofdata may be classified as hybrid DSS systemsproviding modeling, data retrieval and data
summarization functionality.
Model-Driven DSS use data and parameters provided bydecision-makers to aid them in analyzing a situation, but
they are not usually data intensive.
Very large databases are usually not needed for Model-
Driven DSS.
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Traditionally, academics and practitioners havediscussed building Decision Support Systems in
terms of four major components:(cf., Sprague
and Carlson, 1982).
1. The user interface
2. The database
3. The models and analytical tools
4. The DSS architecture and network
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, Document-Driven andKnowledge-Driven DSS need
.
A DSS may use a simple flat-file database with fewer than 1,000 records,but the .
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A Data-DrivenDSS database is often a collection ofcurrent and historical structured data from a number of
sources that have been organized for easy access and
analysis.
Large databases of structured data in Enterprise-Wide
DSS are often called data warehousesordata marts.
DSS usually use data that has been extractedfrom all
relevant internal and external databases.