Information systems supporting business or organizational decision-making activities
A
decision support system
(
DSS
) is an
information system
that supports business or organizational
decision-making
activities. DSSs serve the management, operations and planning levels of an organization (usually mid and higher management) and help people make decisions about problems that may be rapidly changing and not easily specified in advance?i.e., unstructured and semi-structured decision problems. Decision support systems can be either fully computerized or human-powered, or a combination of both.
While academics have perceived DSS as a tool to support
decision making processes
, DSS users see DSS as a tool to facilitate organizational processes.
[1]
Some authors have extended the definition of DSS to include any
system
that might support
decision making
and some DSS include a
decision-making software
component; Sprague (1980)
[2]
defines a properly termed DSS as follows:
- DSS tends to be aimed at the less well structured, underspecified
problem
that upper level
managers
typically face;
- DSS attempts to combine the use of models or analytic techniques with traditional
data access
and
retrieval
functions;
- DSS specifically focuses on features which make them easy to use by non-computer-proficient people in an
interactive
mode; and
- DSS emphasizes
flexibility
and
adaptability
to accommodate changes in the
environment
and the
decision making
approach of the user.
DSSs include
knowledge-based systems
. 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, and/or business models to identify and solve problems and make decisions.
Typical information that a decision support application might gather and present includes:
History
[
edit
]
The concept of decision support has evolved mainly from the theoretical studies of organizational decision making done at the
Carnegie Institute of Technology
during the late 1950s and early 1960s, and the implementation work done in the 1960s.
[3]
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 support systems (GDSS), and organizational decision support systems (ODSS) evolved from the single user and model-oriented DSS. According to Sol (1987),
[4]
the definition and scope of DSS have been migrating over the years: in the 1970s DSS was described as "a computer-based system to aid decision making"; in the late 1970s the DSS movement started focusing 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 towards the end of 1980s DSS faced a new challenge towards the design of intelligent workstations.
[4]
In 1987,
Texas Instruments
completed 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.
[5]
Beginning in about 1990,
data warehousing
and
on-line analytical processing
(OLAP) began broadening the realm of DSS. As the turn of the millennium approached, new Web-based analytical applications were introduced.
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 interface research. Furthermore, although
hypertext
researchers have generally been concerned with
information overload
, certain researchers, notably
Douglas Engelbart
, have been focused on decision makers in particular.
The advent of more 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 environment.
Applications
[
edit
]
DSS can theoretically be built in any knowledge domain. One example is the
clinical decision support system
for
medical diagnosis
. There are four stages in the evolution of clinical decision support system (CDSS): the primitive version is standalone and does not support integration; the second generation supports integration with other medical systems; the third is standard-based, and the fourth is service model-based.
[6]
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 resources. Due to DSS, all the information from any organization is represented in the form of charts, graphs i.e. in a summarized way, which helps the management to take strategic decisions. For example, one of the DSS applications is the management and development of complex anti-terrorism systems.
[7]
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 costs.
A growing area of DSS application, concepts, principles, and techniques is in
agricultural production
, marketing for
sustainable development
. Agricultural DSSes began to be developed and promoted in the 1990s.
[8]
For example, the
DSSAT4
package,
[9]
The Decision Support System for Agrotechnology Transfer
[10]
developed through financial support of
USAID
during the 1980s
[
citation needed
]
and 1990s, has allowed rapid assessment of several agricultural production systems around the world to facilitate decision-making at the farm and policy levels.
Precision agriculture
seeks to tailor decisions to particular portions of farm fields. There are, however, many constraints to the successful adoption of DSS in agriculture.
[11]
DSS is also prevalent in
forest management
where the long planning horizon and the spatial dimension of planning problems demand specific requirements. All aspects of Forest management, from log transportation, harvest scheduling to sustainability and ecosystem protection have been addressed by modern DSSs. In this context, the consideration of single or multiple management objectives related to the provision of goods and services that are traded or non-traded and often subject to resource constraints and decision problems. The Community of Practice of Forest Management Decision Support Systems provides a large repository on knowledge about the construction and use of forest Decision Support Systems.
[12]
A specific example concerns the
Canadian National Railway
system, 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, the Canadian National Railway system managed to decrease the incidence of derailments at the same time other companies were experiencing an increase.
DSS has been used for risk assessment to interpret monitoring data from large engineering structures such as dams, towers, cathedrals, or masonry buildings. For instance, Mistral is an expert system to monitor dam safety, developed in the 1990s by Ismes (Italy). It gets data from an automatic monitoring system and performs a diagnosis of the state of the dam. Its first copy, installed in 1992 on the
Ridracoli
Dam (Italy), is still operational 24/7/365.
[13]
It has been installed on several dams in Italy and abroad (e.g.,
Itaipu Dam
in Brazil),
[14]
and on monuments under the name of Kaleidos.
[15]
Mistral is a registered trade mark of
CESI
.
GIS
has been successfully used since the '90s in conjunction with DSS, to show on a map real-time risk evaluations based on monitoring data gathered in the area of the
Val Pola disaster
(Italy).
[16]
Components
[
edit
]
Three fundamental components of a DSS
architecture
are:
[17]
[18]
[19]
[20]
[21]
- the
database
(or
knowledge base
),
- the
model
(i.e., the decision context and user criteria)
- the
user interface
.
The
users
themselves are also important components of the architecture.
[17]
[21]
Taxonomies
[
edit
]
Using the relationship with the user as the criterion, Haettenschwiler
[17]
differentiates
passive
,
active
, and
cooperative DSS
. A
passive DSS
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 for an iterative process between human and system towards the achievement of a consolidated solution: the decision maker (or its advisor) can modify, complete, or refine the decision suggestions provided by the system, before sending them back to the system for validation, and likewise the system again improves, completes, and refines the suggestions of the decision maker and sends them back to them for validation.
Another taxonomy for DSS, according to the mode of assistance, has been created by D. Power:
[22]
he differentiates
communication-driven DSS
,
data-driven DSS
,
document-driven DSS
,
knowledge-driven DSS
, and
model-driven DSS
.
[18]
- A
communication-driven DSS
enables cooperation, supporting more than one person working on a shared task; examples include integrated tools like Google Docs or
Microsoft SharePoint Workspace
.
[23]
- 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 formats.
- A
knowledge-driven DSS
provides specialized
problem-solving
expertise stored as facts, rules, procedures or in similar structures like interactive
decision trees
and flowcharts.
[18]
- 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.
[24]
Using scope as the criterion, Power
[25]
differentiates
enterprise-wide DSS
and
desktop DSS
. An
enterprise-wide DSS
is linked to large data warehouses and serves many managers in the company. A
desktop, single-user DSS
is a small system that runs on an individual manager's PC.
Development frameworks
[
edit
]
Similarly to other systems, DSS systems require a structured approach. Such a framework includes people, technology, and the development approach.
[19]
The Early Framework of Decision Support System consists of four phases:
- Intelligence
? Searching for conditions that call for decision;
- Design
? Developing and analyzing possible alternative actions of solution;
- Choice
? Selecting a course of action among those;
- Implementation
? Adopting the selected course of action in decision situation.
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 particular problem.
- 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,
Analytica
and
iThink
.
- Tools include lower level hardware/software. DSS generators including special languages, function libraries and linking modules
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 where necessary for the desired outcome.
Classification
[
edit
]
There are several ways to classify DSS applications. Not every DSS fits neatly into one of the categories, but may be a mix of two or more architectures.
Holsapple and Whinston
[26]
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.
[26]
The support given by DSS can be separated into three distinct, interrelated categories:
[27]
Personal Support, Group Support, and Organizational Support.
DSS components may be classified as:
- Inputs
: Factors, numbers, and characteristics to analyze
- 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)
[28]
The nascent field of
decision engineering
treats the decision itself as an engineered object, and applies engineering principles such as
design
and
quality assurance
to an explicit representation of the elements that make up a decision.
See also
[
edit
]
References
[
edit
]
- ^
Keen, Peter (1980). "Decision support systems : a research perspective". Cambridge, Massachusetts : Center for Information Systems Research, Alfred P. Sloan School of Management.
hdl
:
1721.1/47172
.
- ^
Sprague, R;(1980). "
A Framework for the Development of Decision Support Systems
." MIS Quarterly. Vol. 4, No. 4, pp. 1?25.
- ^
Keen, P. G. W. (1978).
Decision support systems: an organizational perspective
. Reading, Mass., Addison-Wesley Pub. Co.
ISBN
0-201-03667-3
- ^
a
b
Henk G. Sol
et al. (1987).
Expert systems and artificial intelligence in decision support systems: proceedings of the Second Mini Euroconference, Lunteren, The Netherlands, 17?20 November 1985
. Springer, 1987.
ISBN
90-277-2437-7
. pp. 1?2.
- ^
Efraim Turban; Jay E. Aronson; Ting-Peng Liang (2008).
Decision Support Systems and Intelligent Systems
. p. 574.
- ^
Wright, A;
Sittig, D
(2008).
"A framework and model for evaluating clinical decision support architectures q"
.
Journal of Biomedical Informatics
.
41
(6): 982?990.
doi
:
10.1016/j.jbi.2008.03.009
.
PMC
2638589
.
PMID
18462999
.
- ^
Zhang, S.X.; Babovic, V. (2011).
"An evolutionary real options framework for the design and management of projects and systems with complex real options and exercising conditions"
.
Decision Support Systems
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51
(1): 119?129.
doi
:
10.1016/j.dss.2010.12.001
.
S2CID
15362734
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- ^
Papadopoulos, A.P.; Shipp, J.L; Jarvis, William R.; Jewett, Thomas J.; Clarke, N.D. (1 July 1995).
"The Harrow Expert System for Greenhouse Vegetables"
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10.21273/HORTSCI.30.4.846F
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- ^
"DSSAT4 (pdf)"
(PDF)
. Archived from
the original
(PDF)
on 27 September 2007
. Retrieved
29 December
2006
.
- ^
"Official Home of the DSSAT Cropping Systems Model"
.
DSSAT.net
. Retrieved
19 August
2021
.
- ^
Stephens, W. and Middleton, T. (2002). Why has the uptake of Decision Support Systems been so poor? In: Crop-soil simulation models in developing countries. 129-148 (Eds R.B. Matthews and William Stephens). Wallingford:CABI.
- ^
Community of Practice Forest Management Decision Support Systems,
http://www.forestdss.org/
- ^
Salvaneschi, Paolo; Cadei, Mauro; Lazzari, Marco (1996).
"Applying AI to structural safety monitoring and evaluation"
.
IEEE Expert
.
11
(4): 24?34.
doi
:
10.1109/64.511774
. Retrieved
5 March
2014
.
- ^
Masera, Alberto; et al.
"Integrated approach to dam safety"
.
Comite Brasileiro de Barragens
. Retrieved
16 December
2020
.
- ^
Lancini, Stefano; Lazzari, Marco; Masera, Alberto; Salvaneschi, Paolo (1997).
"Diagnosing Ancient Monuments with Expert Software"
(PDF)
.
Structural Engineering International
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7
(4): 288?291.
doi
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10.2749/101686697780494392
.
- ^
Lazzari, M.; Salvaneschi, P. (1999).
"Embedding a Geographic Information System in a Decision Support System for Landslide Hazard Monitoring"
(PDF)
.
Natural Hazards
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20
(2?3): 185?195.
doi
:
10.1023/A:1008187024768
.
S2CID
1746570
.
- ^
a
b
c
Haettenschwiler, P. (1999).
Neues anwenderfreundliches Konzept der Entscheidungsunterstutzung
. Gutes Entscheiden in Wirtschaft, Politik und Gesellschaft. Zurich, vdf Hochschulverlag AG: 189-208.
- ^
a
b
c
Power, D. J. (2002).
Decision support systems: concepts and resources for managers
. Westport, Conn., Quorum Books.
- ^
a
b
Sprague, R. H. and E. D. Carlson (1982). Building effective decision support systems. Englewood Cㄴliffs, N.J., Prentice-Hall.
ISBN
0-13-086215-0
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Haag, Cummings, ㅊㄴㅋMcCubbrey, Pinsonneault, Donovan (2000). Management Informatㅍㅈion Systems: For The Information Age. McGraw-Hill Ryerson Limited: 136-140.
ISBN
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a
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Marakas, G. M. (1999). Decision support systems in the twenty-first century. Upper Saddle River, N.J., Prentice Hall.
- ^
"Decision Support Systems (DSS) Articles On-Line"
.
- ^
Stanhope, Phil (2002).
Get in the Groove: Building Tools and Peer-to-Peer Solutions with the Groove Platform
. Wiley.
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. Retrieved
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- ^
Gachet, A. (2004).
Building Model-Driven Decision Support Systems with Dicodess
. Zurich, VDF.
- ^
Power, D. J. (1996). What is a DSS? The On-Line Executive Journal for Data-Intensive Decision Support 1(3).
- ^
a
b
Holsapple, C.W., and A. B. Whinston. (1996). Decision Support Systems: A Knowledge-Based Approach. St. Paul: West Publishing.
ISBN
0-324-03578-0
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Hackathorn, R. D., and P. G. W. Keen. (1981, September). "
Organizational Strategies for Personal Computing in Decision Support Systems
." MIS Quarterly, Vol. 5, No. 3.
- ^
F. Burstein; C. W. Holsapple (2008).
Handbook on Decision Support Systems. Berlin: Springer Verlag
.
Further reading
[
edit
]
- Marius Cioca, Florin Filip (2015).
Decision Support Systems ? A Bibliography 1947-2007
.
- Borges, J.G, Nordstrom, E.-M. Garcia Gonzalo, J. Hujala, T. Trasobares, A. (eds). (2014).
" Computer-based tools for supporting forest management. The experience and the expertise world-wide
. Dept of Forest Resource Management, Swedish University of Agricultural Sciences. Umea. Sweden.
- Delic, K.A., Douillet, L. and Dayal, U. (2001)
"Towards an architecture for real-time decision support systems:challenges and solutions
.
- Diasio, S., Agell, N. (2009) "The evolution of expertise in decision support technologies: A challenge for organizations," cscwd, pp. 692?697, 13th International Conference on Computer Supported Cooperative Work in Design, 2009.
https://web.archive.org/web/20121009235747/http://www.computer.org/portal/web/csdl/doi/10.1109/CSCWD.2009.4968139
- Gadomski, A.M. et al.(2001) "
An Approach to the Intelligent Decision Advisor (IDA) for Emergency Managers
Archived
5 March 2016 at the
Wayback Machine
", Int. J. Risk Assessment and Management, Vol. 2, Nos. 3/4.
- Gomes da Silva, Carlos; Climaco, Joao; Figueira, Jose (2006). "A scatter search method for bi-criteria {0,1}-knapsack problems".
European Journal of Operational Research
.
169
(2). Elsevier BV: 373?391.
doi
:
10.1016/j.ejor.2004.08.005
.
ISSN
0377-2217
.
- Ender, Gabriela; E-Book (2005?2011) about the OpenSpace-Online Real-Time Methodology: Knowledge-sharing, problem solving, results-oriented group dialogs about topics that matter with extensive conference documentation in real-time. Download
https://web.archive.org/web/20070103022920/http://www.openspace-online.com/OpenSpace-Online_eBook_en.pdf
- Jimenez, Antonio; Rios-Insua, Sixto; Mateos, Alfonso (2006).
"A generic multi-attribute analysis system"
.
Computers & Operations Research
.
33
(4). Elsevier BV: 1081?1101.
doi
:
10.1016/j.cor.2004.09.003
.
ISSN
0305-0548
.
- Jintrawet, Attachai (1995). "A Decision Support System for Rapid Assessment of Lowland Rice-based Cropping Alternatives in Thailand".
Agricultural Systems
.
47
(2): 245?258.
doi
:
10.1016/0308-521X(94)P4414-W
.
- Matsatsinis, N.F. and Y. Siskos (2002),
Intelligent support systems for marketing decisions
, Kluwer Academic Publishers.
- Omid A.Sianaki, O Hussain, T Dillon, AR Tabesh ? ... Intelligence, Modelling and Simulation (CIMSiM), 2010,
Intelligent decision support system for including consumers' preferences in residential energy consumption in smart grid
- Power, D. J. (2000).
Web-based and model-driven decision support systems: concepts and issues
. in proceedings of the Americas Conference on Information Systems, Long Beach, California.
- Reich, Yoram; Kapeliuk, Adi (2005). "A framework for organizing the space of decision problems with application to solving subjective, context-dependent problems".
Decision Support Systems
.
41
(1). Elsevier BV: 1?19.
doi
:
10.1016/j.dss.2004.05.001
.
ISSN
0167-9236
.
- Sauter, V. L.
(1997). Decision support systems: an applied managerial approach. New York, John Wiley.
ISBN
978-0471173359
- Silver, M. (1991). Systems that support decision makers: description and analysis. Chichester; New York, Wiley.
- Sprague, Ralph (1986).
Decision support systems : putting theory into practice
. Englewood Cliffs, N.J: Prentice-Hall.
ISBN
978-0-13-197286-5
.
OCLC
13123699
.
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