Decision support systems
constitute a class of
including knowledge-based systems
A Decision Support System (DSS)
is a class of
information systems (including but not limited to computerized
systems) that support business and organizational decision-making
activities. A properly designed DSS is an interactive
software-based system intended to help decision makers compile
useful information from a combination of raw data, documents,
personal knowledge, or business models to identify and solve
problems and make decisions.
Typical information that a decision support application might
gather and present are:
- 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
to Keen (1978), the concept of decision support has evolved from
two main areas of research: The theoretical studies of
organizational decision making done at the Carnegie Institute of
Technology during the late 1950s and early 1960s, and the
technical work on interactive computer systems, mainly carried out
at the Massachusetts Institute of
Technology in the 1960s.
It is considered that the
concept of DSS became an area of research of its own in the middle
of the 1970s, before gaining in intensity during the 1980s. In the
middle and late 1980s, executive information systems
(EIS), group decision
(GDSS), and organizational decision support
systems (ODSS) evolved from the single user and model-oriented
According to Sol (1987) the definition and scope of DSS has been
migrating over the years. In the 1970s DSS was described as "a
computer based system to aid decision making". Late 1970s the the
DSS movement started focussing on "interactive computer-based
systems which help decision-makers utilize data bases and models to
solve ill-structured problems". In the 1980s DSS should provide
systems "using suitable and available technology to improve
effectiveness of managerial and professional activities", and end
1980s DSS faced a new challenge towards the design of intelligent
development of the Gate Assignment Display System (GADS) for
United Airlines. This decision support
system is credited with significantly reducing travel delays by
aiding the management of ground operations at various airports, beginning with O'Hare
International Airport in Chicago and
Stapleton Airport in Denver Colorado.
Beginning in about 1990, data
(OLAP) began broadening the realm of DSS.
As the turn of the millennium approached, new Web-based analytical
applications were introduced.
It is clear that DSS belong to an environment with
multidisciplinary foundations, including (but not exclusively)
research, artificial intelligence
, human-computer interaction
methods, software engineering
, and telecommunications
The advent of better and better reporting technologies has seen DSS
start to emerge as a critical component of management
design. Examples of this can be seen
in the intense amount of discussion of DSS in the education
DSS also have a weak connection to the user interface
paradigm of hypertext
. Both the University of Vermont PROMIS system
(for medical decision making) and the Carnegie Mellon ZOG/KMS
system (for military and business decision making) were decision
support systems which also were major breakthroughs in user
Furthermore, although hypertext
researchers have generally been
concerned with information
, certain researchers, notably Douglas Engelbart
, have been focused on
decision makers in particular.
As with the definition, there is no universally-accepted taxonomy
of DSS either. Different authors propose
different classifications. Using the relationship with the user as
the criterion, Haettenschwiler differentiates passive
, and cooperative DSS
. A passive
is a system that aids the process of decision making, but
that cannot bring out explicit decision suggestions or solutions.
An active DSS
can bring out such decision suggestions or
solutions. A cooperative DSS
allows the decision maker (or
its advisor) to modify, complete, or refine the decision
suggestions provided by the system, before sending them back to the
system for validation. The system again improves, completes, and
refines the suggestions of the decision maker and sends them back
to her for validation. The whole process then starts again, until a
consolidated solution is generated.
Another taxonomy for DSS has been created by Daniel Power. Using
the mode of assistance as the criterion, Power differentiates
, data-driven DSS
, knowledge-driven DSS
- A communication-driven DSS supports more than
one person working on a shared task; examples include integrated
tools like Microsoft's NetMeeting or Groove
- A data-driven DSS or data-oriented DSS
emphasizes access to and manipulation of a time series of internal company data and,
sometimes, external data.
- A document-driven DSS manages, retrieves, and
manipulates unstructured information in a variety of electronic
- A knowledge-driven DSS provides specialized
problem-solving expertise stored as
facts, rules, procedures, or in similar structures.
- A model-driven DSS emphasizes access to and
manipulation of a statistical, financial, optimization, or
simulation model. Model-driven DSS use data and parameters provided
by users to assist decision makers in analyzing a situation; they
are not necessarily data-intensive. Dicodess is an example of an open source model-driven DSS generator.
Using scope as the criterion, Power differentiates
and desktop DSS
is linked to large data warehouses and
serves many managers in the company. A desktop, single-user
is a small system that runs on an individual manager's
Three fundamental components of a DSS architecture
- the database (or knowledge base),
- the model (i.e., the decision
context and user criteria), and
- the user interface.
also important components of the architecture.
DSS systems are not entirely different from other systems and
require a structured approach. Such a framework includes people,
technology, and the development approach.
DSS technology levels (of hardware and software) may include:
- The actual application that will be used by the user. This is
the part of the application that allows the decision maker to make
decisions in a particular problem area. The user can act upon that
- Generator contains Hardware/software environment that allows
people to easily develop specific DSS applications. This level
makes use of case tools or systems such as Crystal, AIMMS, and iThink.
- Tools include lower level hardware/software. DSS generators
including special languages, function libraries and linking
An iterative developmental approach allows for the DSS to be
changed and redesigned at various intervals. Once the system is
designed, it will need to be tested and revised for the desired
There are several ways to classify DSS applications. Not every DSS
fits neatly into one category, but a mix of two or more
architecture in one.
Holsapple and Whinston classify DSS into the following six
frameworks: Text-oriented DSS, Database-oriented DSS,
Spreadsheet-oriented DSS, Solver-oriented DSS, Rule-oriented DSS,
and Compound DSS.
A compound DSS is the most popular classification for a DSS. It is
a hybrid system that includes two or more of the five basic
structures described by Holsapple and Whinston.
The support given by DSS can be separated into three distinct,
interrelated categories: Personal Support, Group Support, and
DSS components may be classified as:
- Inputs: Factors, numbers, and characteristics
- User Knowledge and Expertise: Inputs requiring
manual analysis by the user
- Outputs: Transformed data from which DSS
"decisions" are generated
- Decisions: Results generated by the DSS based
on user criteria
DSSs which perform selected cognitive
decision-making functions and are based on artificial intelligence
or intelligent agents
technologies are called
Intelligent Decision Support Systems (IDSS).
The nascent field of Decision
treats the decision itself as an engineered object,
and applies engineering principles such as Design
to an explicit representation of the elements that
make up a decision.
As mentioned above, there are theoretical possibilities of building
such systems in any knowledge domain.
One example is the Clinical decision support
for medical diagnosis
Other examples include a bank loan officer verifying the credit of
a loan applicant or an engineering firm that has bids on several
projects and wants to know if they can be competitive with their
DSS is extensively used in business and management. Executive dashboard
and other business
performance software allow faster decision making, identification
of negative trends, and better allocation of business
A growing area of DSS application, concepts, principles, and
techniques is in agricultural production, marketing for sustainable
development. For example, the DSSAT4 package, developed through
financial support of USAID during the 80's and 90's, has allowed
rapid assessment of several agricultural production systems around
the world to facilitate decision-making at the farm and policy
levels. There are, however, many constraints to the successful
adoption on DSS in agriculture.
DSS are also prevalent in forest
where the long planning time frame demands specific
requirements. All aspects of Forest management, from log
transportation, harvest scheduling to sustainability and ecosystem
protection have been addressed by modern DSSs. A comprehensive list
and discussion of all available systems in forest management is
being compiled under the COST
A specific example concerns the Canadian National Railway
which tests its equipment on a regular basis using a decision
support system.A problem faced by any railroad is worn-out or
defective rails, which can result in hundreds of derailments per
year. Under a DSS, CN managed to decrease the incidence of
derailments at the same time other companies were experiencing an
DSS has many applications that have already been spoken about.
However, it can be used in any field where organization is
necessary. Additionally, a DSS can be designed to help make
decisions on the stock market, or deciding which area or segment to
market a product toward.
CACI has begun integrating simulation
and decision support systems.
CACI defines three levels of
simulation model maturity. “Level 1” models are traditional desktop
simulation models that are executed within the native software
package. These often require a simulation expert to implement
modifications, run scenarios, and analyze results. “Level 2” models
embed the modeling engine in a web application that allows the
decision maker to make process and parameter changes without the
assistance of an analyst. “Level 3” models are also embedded in a
web-based application but are tied to real-time operational data.
The execution of “level 3” models can be triggered automatically
based on this real-time data and the corresponding results can be
displayed on the manager’s desktop showing the prevailing trends
and predictive analytics given the current processes and state of
the system. The advantage of this approach is that “level 1” models
developed for the FDA projects can migrate to “level 2 and 3”
models in support of decision support, production/operations
management, process/work flow management, and predictive analytics.
This approach involves developing and maintaining reusable models
that allow decision makers to easily define and extract business
level information (e.g., process metrics). “Level 1” models are
decomposed into their business objects and stored in a database.
All process information is stored in the database, including
activity, resource, and costing data. The database becomes a
template library that users can access to build, change, and modify
their own unique process flows and then use simulation to study
their performance in an iterative manner.
Benefits of DSS
- Improves personal efficiency
- Expedites problem solving (speed up the progress of problems
solving in an organization)
- Facilitates interpersonal communication
- Promotes learning or training
- Increases organizational control
- Generates new evidence in support of a decision
- Creates a competitive advantage over competition
- Encourages exploration and discovery on the part of the
- Reveals new approaches to thinking about the problem space
- Helps automate the managerial processes.
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