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Decision Support Systems (DSS) are a specific class of computerized information
system that supports business and organizational decision-making activities. A properlydesigned DSS is an interactive software-based system intended to help decision makers
compile useful information from raw data, documents, personal knowledge, and/or
business models to identify and solve problems and make decisions.
Typical information that a decision support application might gather and present would
an inventory of all of your current information assets (including legacy and
relational data sources, cubes, data warehouses, and data marts),
comparative sales figures between one week and the next,
projected revenue figures based on new product sales assumptions;
the consequences of different decision alternatives, given past experience in a
context that is described.
A decision support system is clearly not an application that simply manipulates data or
supports decisionmaking. For example, an enhanced user interface that permits querying
and analysis of a single database is not a decision support system; nor is a spreadsheet
application with basic analysis and advanced “if/then” planning features. Even a database
management system (DBMS) that permits a user to select and analyze data within a
single database for reporting and analysis would not qualify, because it does not integrate
multiple databases. Rather, a decision support system is intentionally and explicitly more
comprehensive, and is designed specifically to enable users to support problem solving
and decisionmaking by compiling information from disparate sources of raw data. A
robust definition of a decision support system should encompass:
(1) Users who understand what the data mean and how they can be accessed with
(2) Technology system (hardware, software, and user interfaces) that manipulates
(3) A data system (integratingdata from multiple sources) explicitly for the purposes of
(4) A decisionmaking system (user-driven within an organization).
While not a formal definition, this description was developed for this publication to stress
multiple emphases on user skills, technology tools, data quality, information use, and
organizational management encompassed by true decision support systems. Such a
description incorporates technology tools for managing, analyzing, communicating, and
using data; an understanding of data within the system and the implications of the use of
those data; and an intention by decisionmakers to employ information for the purpose of
planning and action within an organization.
A “decision support system” may be defined in many ways. Some definitions emphasize
hardware and software components; others focus primarily on function (i.e., fulfilling
the information needs of decisionmakers); while a few even describe user interfaces, job
functions, and data flow. As such, competing yet complementary definitions of decision
support systems include:
Decision support system: An interactive software-based computerized
information system intended to help decisionmakers compile useful information
from raw data, documents, personal knowledge, and business models to identify
and solve problems and to make decisions.
 Decision support system: An interactive computerized system that gathers and
presents data from a wide range of sources to help people make decisions.
Applications are not single information resources, such as a database or a graphics
program, but rather the combination of integrated resources working together.
Decision support system: A cohesive and integrated set of programs that share
data and information and provide the ability to query computers on an ad-hoc
basis, analyze information, and predict the impact of possible decisions.
A DSS consists of two major sub-systems – human decision makers and computer
systems. Interpreting a DSS as only a computer hardware and software system is a
common misconception. An unstructured (or semi-structured) decision by definition
cannot be programmed because its precise nature and structure are elusive and complex
(Simon 1960). The function of a human decision maker as a component of DSS is not to
enter data to build a database, but to exercise judgment or intuition throughout the entire
decisionmaking process (see DECISION MAKING AND IT/S).
Imagine a manager who has to make a five-year production planning decision.
The first step of the decisionmaking process begins with the creation of a decision
support model, using an integrated DSS program (DSS generator) such as Microsoft
Excel, Lotus 1-2-3, Interactive Financial Planning Systems (IFPS)/Personal or
Express/PC. The user interface sub-system (or dialogue generation and management
systems) is the gateway to both database management systems (DBMS) and model-based
management systems (MBMS). DBMS are a set of computer programs that create and
manage the database, as well as control access to the data stored within it. the DBMS can
be either an independent program or embedded within a DSS generator to allow users to
create a database file that is to be used as an input to the DSS. MBMS is a set of
computer programs embedded within a DSS generator that allows users to create, edit,
update, and/or delete a model. Users create models and associated database files to make
specific decisions. The created models and databases are stored in the model base and
database in the direct access storage devices such as hard disks. From a user's viewpoint,
the user interface subsystem is the only part of DSS components with which they have to
deal. Therefore, providing an effective user interface must take several important issues
into consideration, including choice of input and output devices, screen design, use of
colours, data and information presentation format, use of different interface styles, etc.
Today's decision support system generator provide the user with a wide variety
of interface modes (styles): menu based interaction mode, command language style,
questions and answers, form interaction, natural language processing based dialogue, and
graphical user interface (GUI). GUIs use icons, buttons, pull-down menus, bars,
and boxes extensively and have become the most widely implemented and versatile type.
The interface system allows users access to:
(1) The data sub-system:
(a) database (b) database management software; and
(2) The model sub-system:
(a) model base (b) model base management software
An organizational decision support system is defined as 'a DSS that is used by
individuals or groups at several work stations in more than one organizational unit who
make varied (interrelated but autonomous) decisions using a common set of tools' (Carter
et al. 1992: 19). According to the same source, an important goal of organizational DSS
is to provide 'the glue that holds a large organization together and keeps its parts
marching to the beat of the same drummer toward common goals'. The two key factors to
achieving these outcomes are: (1) transmittal of consistent, timely information up and
down the organizational hierarchy in forms that are appropriate to each decision maker;
and (2) a set of decision-aiding models that use this information and that are appropriate
for the decisions being made by each decision maker.
There are a number of Decision Support Systems. These can be categorized into five
Communication-driven DSS
Most communications-driven DSSs are targetted at internal teams, including
partners. Its purpose are to help conduct a meeting, or for users to collaborate.
The most common technology used to deploy the DSS is a web or client server.
Examples: chats and instant messaging softwares, online collaboration and netmeeting systems.
Data-driven DSS
Most data-driven DSSs are targeted at managers, staff and also product/service
suppliers. It is used to query a database or data warehouse to seek specific
answers for specific purposes. It is deployed via a main frame system,
client/server link, or via the web. Examples: computer-based databases that have a
query system to check (including the incorporation of data to add value to existing
Document-driven DSS
Document-driven DSSs are more common, targeted at a broad base of user
groups. The purpose of such a DSS is to search web pages and find documents on
a specific set of keywords or search terms. The usual technology used to set up
such DSSs are via the web or a client/server system.
Knowledge-driven DSS
Knowledge-driven DSSs or 'knowledgebase' are they are known, are a catch-all
category covering a broad range of systems covering users within the organization
seting it up, but may also include others interacting with the organization - for
example, consumers of a business. It is essentially used to provide management
advice or to choose products/services. The typical deployment technology used to
set up such systems could be slient/server systems, the web, or software runnung
on stand-alone PCs.
Model-driven DSS
Model-driven DSSs are complex systems that help analyse decisions or choose
between different options. These are used by managers and staff members of a
business, or people who interact with the organization, for a number of purposes
depending on how the model is set up - scheduling, decision analyses etc. These
DSSs can be deployed via software/hardware in stand-alone PCs, client/server
systems, or the web.
Characteristics of a Decision Support System
1. Facilitation. DSS facilitate and support specific decision-making activities and/or
decision processes.
2. Interaction. DSS are computer-based systems designed for interactive use by decision
makers or staff users who control the sequence of interaction and the operations
3. Ancillary. DSS can support decision makers at any level in an organization. They are
NOT intended to replace decision makers.
4. Repeated Use. DSS are intended for repeated use. A specific DSS may be used
routinely or used as needed for ad hoc decision support tasks.
5. Task-oriented. DSS provide specific capabilities that support one or more tasks
related to decision-making, including: intelligence and data analysis; identification and
design of alternatives; choice among alternatives; and decision implementation.
6. Identifiable. DSS may be independent systems that collect or replicate data from other
information systems OR subsystems of a larger, more integrated information system.
7. Decision Impact. DSS are intended to improve the accuracy, timeliness, quality and
overall effectiveness of a specific decision or a set of related decisions.
Advantages of a Decision Support System:
DSS can create positive benefits for both the individual decision makers and the
companies who will use the system.
Time Savings: Research has demonstrated that all categories of decision support systems
reduce decision cycle time, increase employee productivity, and provide more timely
information for decision making. DSS can also create a cost advantage by increasing
efficiency or eliminating activities.
Improved Interpersonal Communication: Improved communication and collaboration
between decision makers can be a result of DSS. Model-driven DSS allows users to share
facts and assumptions. Data-driven DSS allows fact-based decision making by creating
one version of the truth about company operations available to managers.
Increased Decision Maker Satisfaction: DSS can help reduce frustrations of decision
makers by providing the perception that better information is being used.
Increased Organizational Control: Data-driven DSS often makes business transaction
data available for performance monitoring. Such a system can provide users with an
enhanced understanding of business operations, although the financial benefit from
increasingly detailed data is not always immediately clear.
Targeted Marketing: DSS can be used to target a specific customer segment and gain an
advantage in meeting needs in that particular segment. DSS can help track customers and
make it easier to serve a specialized customer group.
DSS design is the process of identifying the key decisions through decision analysis,
specifying requirements of each DSS component to support key decisions identified
through decision analysis. DSS are designed and implemented to support organizational
as well as individual decision making. Without a detailed understanding of decisionmaking behaviour in organizations, 'decision support is close to meaningless as a concept'
(Keen and Scott-Morton 1978: 61). Organizational scientists classify organizational
decision making in terms of several schools of thought:
(1) the rational model which focuses on the selection of the most efficient alternatives,
with the assumption of a rational, completely informed single decision maker;
(2) the organizational process model which stresses the compartmentalization of the
various units in any organization;
(3) the satisficing model which reflects 'bounded rationality' to find an acceptable, good
enough solution; and
(4)other models.
Use of some computer-based information systems such as TPS and MIS are, in most
cases, mandatory. But decision support systems are voluntary systems. In regard to
voluntary systems, DSS implementation research has been important for ascertaining the
influence of success factors of DSS implementations. DSS implementation researchers
are investigating the relationship between user-related factors and implementation
success. User factors include cognitive style (the characteristic ways individuals process
and utilize information to solve problems), personality (the cognitive structures
maintained by individuals to facilitate adjustment to events and situations), demographics
(age, sex and education), and user-situation variables (training, experiences and user
involvement) (Alavi and Joachimsthaler 1992). future implementation research should be
directed toward the development of causal models of user-related implementation factors.
Furthermore, it is suggested that DSS researchers shift the research focus from userrelated factors to the contextual variables. An important assumption on which the DSS
implementation research is based is that DSS are voluntary systems. A recent survey of
DSS suggests that an increasing number of DSS have become a strategic tool for
organizational survival (Eom et al 1998). Thus, these systems are no longer voluntary
Future DSS implementation research must take this changing nature of DSS from
voluntary systems to mandatory survival tools. Consequently, individual differences,
cognitive styles, personality, demographics, and user-situational variables may become
less critical success factors. Shifting the focus of implementation research from userrelated factors to task-related, organizational, and external environmental factors may be
necessary to reflect the changing decision environment in which organization must
survive and prosper.
Evaluation of DSS is concerned with analysing costs and benefits of DSS before and after
DSS development and implementation. The unique nature of DSS evaluation is that
although some DSS provide substantial cost saving and profit increases, measurements of
benefits of DSS have been problematic as quantification of the positive impacts of
improved decision process is difficult.
Therefore, DSS evaluation research deals with the following methodologies: decision
outputs, changes in the decision process, changes in managers' concepts of the decision
situation, procedural changes, cost/benefit analysis, service measures and managers'
assessment of the system's value.
DSS application development is the fruit of DSS study. Theories developed from DSS
research must be assimilated into the DSS development process. The next section briefly
introduces the current status of DSS application development research in corporate
functional management areas. Decision support systems are interactive, computer-based
systems that aid users in judgment and choice activities. They provide data storage and
retrieval but enhance the traditional information access and retrieval functions with
support for model building and model-based reasoning. They support framing, modeling,
and problem solving.
Typical application areas of DSSs are management and planning in business, health care,
the military, and any area in which management will encounter complex decision
situations. Decision support systems are typically used for strategic and tactical decisions
faced by upper-level management|decisions with a reasonably low frequency and high
potential consequences|in which the time taken for thinking through and modeling the
problem pays o_ generously in the long run.
There are three fundamental components of DSSs :
Database management system (DBMS).
A DBMS serves as a data bank for the DSS. It stores large quantities of data that are
relevant to the class of problems for which the DSS has been designed and provides
logical data structures (as opposed to the physical data structures) with which the users
interact. A DBMS separates the users from the physical aspects of the database structure
and processing. It should also be capable of informing the user of the types of data that
are available and how to gain access to them.
Model-base management system (MBMS).
The role of MBMS is analogous to that of a DBMS. Its primary function is providing
independence between speci_c models that are used in a DSS from the applications that
use them. The purpose of an MBMS is to transform data from the DBMS into
information that is useful in decision making. Since many problems that the user of a
DSS will cope with may be unstructured, the MBMS should also be capable of assisting
the user in model building.
Dialog generation and management system (DGMS).
The main product of an interaction with a DSS is insight. As their users are often
managers who are not computer-trained, DSSs need to be equipped with intuitive and
easy-to-use interfaces.
An emergent class of DSSs known as decision-analytic DSSs applies the principles of
decision theory,probability theory, and decision analysis to their decision models.
Decision theory is an axiomatic theory of decision making that is built on a small set of
axioms of rational decision making. It expresses uncertainty in terms of probabilities and
preferences in terms of utilities. These are combined using the operation of mathematical
expectation. The attractiveness of probability theory, as a formalism for handling
uncertainty in DSSs, lies in its soundness and its guarantees concerning long-term
performance. Probability theory is often viewed as the gold standard for rationality in
reasoning under uncertainty. Following its axioms o_ers protection from some
elementary inconsistencies. Their violation, on the other hand, can be demonstrated to
lead to sure losses . Decision analysis is the art and science of applying decision theory to
real-world problems.
It includes a wealth of techniques for model construction, such as methods for elicitation
of model structure and probability distributions that allow minimization of human bias,
methods for checking the sensitivity of a model to imprecision in the data, computing the
value of obtaining additional information, and presentation of results. These methods
have been under continuous scrutiny by psychologists working in the domain of
behavioral decision theory and have proven to cope reasonably well with the dangers
related to human judgmental biases.
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While the quality and reliability of modeling tools and the internal architectures of DSSs
are important, the most crucial aspect of DSSs is, by far, their user interface. Systems
with user interfaces that are cumbersome or unclear or that require unusual skills are
rarely useful and accepted in practice. The most important result of a session with a DSS
is insight into the decision problem. In addition, when the system is based on normative
principles, it can play a tutoring role; one might hope that users will learn the domain
model and how to reason with it over time, and improve their own thinking. A good user
interface to DSSs should support model construction and model analysis, reasoning about
the problem structure in addition to numerical calculations and both choice and
optimization of decision variables.
A host of new tools and technologies are adding new capabilities to DSS/ESS and will
reshape DSS developments in organizations. They include hardware and mathematical
software developments, artificial intelligence techniques,
the datawarehouse /
multidimensional databases (MDDB), data mining, online analytical processing (OLAP),
enterprise resource planning (ERP) systems, intelligent agents,telecommunication
technologies such as World Wide Web technologies, the Internet, and corporate intranets.
Single user decision support systems
Ever-increasing computing power makes it possible to solve a large-scale mathematical
optimization model in a fraction of a second. The size of the problem solvable by
commercial software is virtually unlimited, only dependent upon the size of random
access memory of computers and the user's patience. Moreover, several solvers are built
into the spreadsheet programs such as Microsoft Excel and Borland's Quattro-Pro, along
with the capabilities of linking to databases and graphical user interfaces.With the
increasing trend of national and global communication networking, single user DSS will
increasingly become a part of organization-wide distributed decision-making (DDM)
systems. The DDM system consists of several single user DSS that work together and
independently to make a sequential decision such as joint production/marketing decisions
(Rathwell and Burns 1985). DDM systems work as a mechanism for integrating a number
of separate DSSs that coexist in an organization, facilitating group cooperation between
several DSSs in a distributed environment, and meeting the specific needs of group
planning and group decision making.
Notable developments that will significantly affect the future development of DSS are
the data warehouse, data mining and intelligent agents. The data warehouse is a subjectoriented, integrated, timevariant, and non-volatile (read only) collection of a
relational/multidimensional database (MDDB) optimized for decision support, which is
separated from operational databases. MDDB organizes data as an n-dimensional cube so
that users deal with multidimensional data views such as product, region, sales, time, etc.
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with a faster query response time. Data mining, also known as Knowledge Data
Discovery, refers to discovering hidden patterns/ trends/ classes/ insights/relationships
from data, and it attempts to automatically extract knowledge from the in large databases.
Knowledge-based decision support systems (Intelligent DSS)
An increasing number of systems are incorporating domain knowledge, modelling, and
analysis systems to provide users the capability of intelligent assistance. Knowledge base
modules are being used to formulate problems and decision models, and analyse and
interpret the results. Some systems are adding knowledge-based modules to replace
human judgments. Managerial judgements have been used to ascertain (assess) future
uncertainty and to select assumptions on which decision models can be based. Some
decisions are both knowledge and data intensive. Consequently, a large amount of data
usually requires considerable efforts for their interpretation and use. The knowledgebased DSS include a knowledge management component which stores and manages a
new class of emerging AI tools such as machine learning and case-based reasoning and
learning.These tools can obtain knowledge from prior data, decisions and examples
(cases), and contribute to the creation of DSS to support repetitive, complex real-time
decision making. Machine learning refers to computational methods/tools of a computer
system to learn from experience (past solutions), data and observations, and consequently
alter its behaviour, triggered by a modification to the stored knowledge. Artificial neural
networks and genetic algorithms are the most notable approaches to machine learning.
The role of knowledge-based DSS should be to allow experts to broaden and expand
their expertise,not to narrow it down.
The World Wide Web and Group/Organizational/Global DSS
The World Wide Web is increasingly being used as the client-server platform of many
business organizations due to its network and platform-independence and very low
Software /installation/maintenance costs. More and more groupware will be inextricably
tied to Internet technology. Especially, the World Wide Web is becoming an
infrastructure for the next generation of decision support systems and groupware
applications. Many groupware products, such as Lotus Development's Domino and
Microsoft's Exchange, are integrating more Internet protocols into them. Microsoft's next
version of Office suite is expected to completely remove the boundaries between the
World Wide Web and groupware. Many companies are applying groupware technology
to increase business-to-business collaborations (e.g. collaborations among the company,
its customers, and its suppliers, a.k.a. superworkgroup software) over intranets and
development in the information systems area is the growing importance of enterprise
resources planning (ERP) systems.
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ERP systems are a new generation of information systems packages that integrate
information and information-based processes within and
across functional areas in an organization. ERP has focused primarily on processing of
transaction data resulting in the creation of the extensive, organizational databases of an
organization that may consist of individual business units across the globe. The extensive
databases created by the ERP system provide the platform for decision support, data
warehousing, data mining, and executive support systems. integrated solutions provided
by the ERP system are attributable to the use of the common database.
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Decision support systems are becoming increasingly important information management
tools in education organizations. They are already being used effectively by many
schools, districts, and state education agencies across the nation. Depending on their
configuration, these systems can be powerful tools for addressing a wide range of
questions about student performance, classroom management, organization-wide
operations, and state-level policymaking.
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