1 supporting business decision making corta

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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.

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