Psychology Wiki
Don't have an account?
Register
Register
in: Pages with reference errors, Pages using ISBN magic links, Artificial intelligence,
and 6 more

Changes: Decision support systems

Back to page
Sign in to edit
No edit summary
 
(3 intermediate revisions by the same user not shown)
Line 1: Line 1:
 
{{StatsPsy}}
  
{{StatsPsy}}
  +
{{Infolong}}
  +
'''Decision support systems''' are a form of [[expert system]], a [[computer software]] based [[information system]] including [[knowledge based system|knowledge-based systems]] that support [[decision-making]] activities. They do this by facilitating the [[computer simulation]] of the outcomes of alternative decision choices,
   
  +
===Definition===
−
'''Decision support systems''' are a class of computerized [[information system|information systems]] that support [[decision-making|decision making]] activities.
  
  +
Decision Support Systems (DSS) are a specific class of computerized information system that supports 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 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 be:
−
==Definitions==
  
  +
*an inventory of all of your current information assets (including legacy and relational data sources, cubes, data warehouses, and data marts),
−
The concept of a ''decision support system'' (DSS) is extremely broad and its definitions vary depending upon the author's point of view (Druzdzel and Flynn 1999). A DSS can take many different forms and the term can be used in many different ways (Alter 1980).
 
  +
*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 brief history===
−
On the one hand, Finlay (1994) and others define a DSS broadly as "a computer-based system that aids the process of [[decision making]]." In a more precise way, Turban (1995) defines it as "an interactive, flexible, and adaptable computer-based [[information system]], especially developed for supporting the solution of a non-structured [[management]] problem for improved [[decision making]]. It utilizes data, provides an easy-to-use [[interface]], and allows for the decision maker's own insights."
 
  ⚫
In the absence of an all-inclusive definition, we focus on the history of DSS (see also Power<ref name="Power 2003">Power, D.J. A Brief History of Decision Support Systems DSSResources.COM, World Wide Web, version 2.8, May 31, 2003.</ref>). According to Keen <ref name="Scott Morton 1978"> Keen, P. G. W. (1978). Decision support systems: an organizational perspective. Reading, Mass., Addison-Wesley Pub. Co. ISBN 0-201-03667-3</ref>, 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 support system]]s (GDSS), and [[organizational decision support system]]s (ODSS) evolved from the single user and model-oriented DSS. Beginning in about 1990, [[data warehouse|data warehousing]] and [[Online analytical processing|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.
   
  ⚫
It is clear that DSS belong to an environment with multidisciplinary foundations, including (but not exclusively) [[database]] research, [[artificial intelligence]], [[human-computer interaction]], [[simulation]] methods, [[software engineering]], and [[telecommunications]].
−
Other definitions fill the gap between these two extremes. For Keen and Scott Morton (1978), DSS couple the intellectual resources of individuals with the capabilities of the computer to improve the quality of decisions ("DSS are computer-based support for management decision makers who are dealing with semi-structured problems"). For Sprague and Carlson (1982), DSS are "interactive computer-based systems that help decision makers utilize data and models to solve unstructured problems." On the other hand, Keen (1980) claims that it is impossible to give a precise definition including all the facets of the DSS ("there can be no definition of ''decision support systems'', only of ''decision support''"). Nevertheless, according to Power (1997), the term ''decision support system'' remains a useful and inclusive term for many types of information systems that support decision making. He humorously adds that every time a computerized system is not an on-line transaction processing system ([[OLTP]]), someone will be tempted to call it a DSS. As you can see, there is no universally accepted definition of DSS.
 
   
  +
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 environment.
−
Additionally, the specifics of it is what makes it less generalized and more detailed. In addition, a DSS also is a specific Software application that helps to analyze data contained with a customer database. This approach to customers is used when deciding on target markets as well as customer habits. As you can see in this specific example, it is obvious that DSS can be used for more than just organization.
  
   
  ⚫
DSS also have a weak connection to the [[user interface]] paradigm of [[hypertext]]. Both the [[University of Vermont]] [[Problem-Oriented Medical Information System|PROMIS]] system (for medical decision making) and the Carnegie Mellon [[ZOG (hypertext)|ZOG]]/[[KMS (Hypertext)|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.
−
''Recommended reading:'' Druzdzel and Flynn (1999), Power (2000), Sprague and Watson (1993), the first chapter of Power (2002), the first chapter of Makaras (1999), the first chapter of Silver (1991), the first two chapters of Sauter (1997), and Holsaple and Whinston (1996).
  
   
−
==A brief history==
 +
==Taxonomies==
  ⚫
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 <ref name="Haettenschwiler 1999"> Haettenschwiler, P. (1999). Neues anwenderfreundliches Konzept der Entscheidungsunterstützung. Gutes Entscheiden in Wirtschaft, Politik und Gesellschaft. Zurich, vdf Hochschulverlag AG: 189-208. </ref> 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 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.
⚫
In the absence of an all-inclusive definition, we focus on the history of DSS (see also Power, 2003). According to Keen and Scott Morton (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 support system]]s (GDSS), and [[organizational decision support system]]s (ODSS) evolved from the single user and model-oriented DSS. Beginning in about 1990, [[data warehouse|data warehousing]] and [[Online Analytical Processing|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.
  
   
  +
Another taxonomy for DSS has been created by Daniel Power. Using the mode of assistance as the criterion, Power differentiates ''communication-driven DSS'', ''data-driven DSS'', ''document-driven DSS'', ''knowledge-driven DSS'', and ''model-driven DSS''.<ref name="Power 2002">Power, D. J. (2002). Decision support systems: concepts and resources for managers. Westport, Conn., Quorum Books.</ref>
⚫
It is clear that DSS belong to an environment with multidisciplinary foundations, including (but not exclusively) [[database]] research, [[artificial intelligence]], [[human-computer interaction]], [[simulation]] methods, [[software engineering]], and [[telecommunications]].
  
  ⚫
*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 <ref>Gachet, A. (2004). Building Model-Driven Decision Support Systems with Dicodess. Zurich, VDF. </ref>.
  ⚫
*A '''communication-driven DSS''' supports more than one person working on a shared task; examples include integrated tools like Microsoft's NetMeeting or [[Microsoft Groove|Groove]]<ref>Stanhope, P. (2002). Get in the Groove: building tools and peer-to-peer solutions with the Groove platform. New York, Hungry Minds</ref>
  ⚫
*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.<ref name="Power 2002" />
   
  ⚫
Using scope as the criterion, Power <ref name="Power 1997"> Power, D. J. (1997). What is a DSS? The On-Line Executive Journal for Data-Intensive Decision Support 1(3).</ref> 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.
⚫
DSS also has 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 helping decision makers in particular.
  
   
−
==Taxonomies==
 +
==Architectures==
  ⚫
Once again, different authors identify different components in a DSS. For example, Sprague and Carlson <ref name="Sprague and Carlson 1982"> Sprague, R. H. and E. D. Carlson (1982). Building effective decision support systems. Englewood Cliffs, N.J., Prentice-Hall. ISBN 0-130-86215-0</ref> identify three fundamental components of DSS: ''(a)'' the [[database management system]] (DBMS), ''(b)'' the model-base management system (MBMS), and ''(c)'' the dialog generation and management system (DGMS).
⚫
As with the definition, there is no all-inclusive [[taxonomy]] of DSS either. Different authors propose different classifications. At the user-level, Hättenschwiler (1999) 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 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.
  
   
  ⚫
* Haag ''et al.'' <ref> Haag, Cummings, McCubbrey, Pinsonneault, Donovan (2000). Management Information Systems: For The Information Age. McGraw-Hill Ryerson Limited: 136-140. ISBN 0-072-81947-2 </ref> describe these three components in more detail:
−
At the conceptual level, Power (2002) differentiates ''communication-driven DSS'', ''data-driven DSS'', ''document-driven DSS'', ''knowledge-driven DSS'', and ''model-driven DSS''.
 
  ⚫
The Data Management Component stores information (which can be further subdivided into that derived from an organization's traditional data repositories, from external sources such as the [[Internet]], or from the personal insights and experiences of individual users);
⚫
*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 DSS users to aid decision makers in analyzing a situation, but they are not necessarily data intensive. Dicodess is an example of an [[open source]], model-driven DSS generator (Gachet 2004).
 
  ⚫
the Model Management Component handles representations of events, facts, or situations (using various kinds of models, two examples being optimization models and goal-seeking models); and the User Interface Management Component is, of course, the component that allows a user to interact with the system.
⚫
*A '''communication-driven DSS''' supports more than one person working on a shared task; examples include integrated tools like Microsoft's NetMeeting or Groove (Stanhope 2002).
 
⚫
*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.
  
   
  ⚫
* According to Power <ref name="Power 2002" />, academics and practitioners have discussed building DSS in terms of four major components: ''(a)'' the [[user interface]], ''(b)'' the [[database]], ''(c)'' the model and analytical tools, and ''(d)'' the DSS architecture and network.
⚫
At the system level, Power (1997) differentiates ''enterprise-wide DSS'' and ''desktop DSS''. ''Enterprise-wide DSS'' are linked to large data warehouses and serve many managers in a company. ''Desktop, single-user DSS'' are small systems that reside on an individual manager's PC.
  
   
  ⚫
* Hättenschwiler <ref name="Haettenschwiler 1999">Haettenschwiler, P. (1999). Neues anwenderfreundliches Konzept der Entscheidungsunterstützung. Gutes Entscheiden in Wirtschaft, Politik und Gesellschaft. Zurich, vdf Hochschulverlag AG: 189-208.</ref> identifies five components of DSS:
−
When classifying DSS, it can be viewed as very broad or very narrow. Since it is difficult to classify DSS into only one classification, the taxonomy cannot exactly be pinpointed. However, if it is necessary, a DSS is certainly classified into precise, scientific organizational software that not only contributes, but also performs decision making steps in order to ease the pressure for its users. The fact is in a few words, DSS is an organizational decision making software.
  
   
  +
''(a)'' users with different roles or functions in the decision making process (decision maker, advisors, domain experts, system experts, data collectors),
   
  +
''(b)'' a specific and definable decision context,
   
  +
''(c)'' a target system describing the majority of the preferences,
−
Other authors, such as Alter, Holsapple and Whinston, Donovan and Madnick, Hackathorn and Keen, Golden, Hevner and Power, propose different taxonomies. Reading the first chapter of Power (2002) is recommended.
  
   
  +
''(d)'' a [[knowledge base]] made of external data sources, knowledge databases, working databases, data warehouses and meta-databases, mathematical models and methods, procedures, inference and search engines, administrative programs, and reporting systems, and
−
==Architectures==
  
  +
⚫
Once again, different authors identify different components in a DSS. Sprague and Carlson (1982) identify three fundamental components of DSS: ''(a)'' the [[database management system]] (DBMS), ''(b)'' the model-base management system (MBMS), and ''(c)'' the dialog generation and management system (DGMS).
  
  +
''(e)'' a working environment for the preparation, analysis, and documentation of decision alternatives.
−
Haag ''et al.'' (2000) describe these three components in more detail:
  
  +
⚫
the Data Management Component stores information (which can be further subdivided into that derived from an organization's traditional data repositories, from external sources such as the [[Internet]], or from the personal insights and experiences of individual users);
  
  ⚫
* arakas <ref>Marakas, G. M. (1999). Decision support systems in the twenty-first century. Upper Saddle River, N.J., Prentice Hall.</ref> proposes a generalized architecture made of five distinct parts:
⚫
the Model Management Component handles representations of events, facts, or situations (using various kinds of models, two examples being optimization models and goal-seeking models);
  
  +
−
and the User Interface Management Component is of course the component that allows a user to interact with the system.
  
  +
''(a)'' the data management system,
  +
  +
''(b)'' the model management system,
  +
  +
''(c)'' the knowledge engine,
  +
  +
''(d)'' the user interface, and
  +
  +
''(e)'' the user(s).
   
  +
=== Development Frameworks ===
⚫
According to Power (2002), academics and practitioners have discussed building DSS in terms of four major components: ''(a)'' the [[user interface]], ''(b)'' the [[database]], ''(c)'' the model and analytical tools, and ''(d)'' the DSS architecture and network.
  
  +
DSS systems are not entirely different from other systems and require a structured approach. A framework was provided by Sprague and Watson (1993). The framework has three main levels.
−
Hättenschwiler (1999) identifies five components of DSS: ''(a)'' users with different roles or functions in the decision making process (decision maker, advisors, domain experts, system experts, data collectors), ''(b)'' a specific and definable decision context, ''(c)'' a target system describing the majority of the preferences, ''(d)'' a [[knowledge base]] made of external data sources, knowledge databases, working databases, data warehouses and meta-databases, mathematical models and methods, procedures, inference and search engines, administrative programs, and reporting systems, and ''(e)'' a working environment for the preparation, analysis, and documentation of decision alternatives.
  
  +
1. Technology levels
  +
2. People involved
  +
3. The developmental approach
   
  +
# Technology Levels
−
Marakas (1999) proposes a generalized architecture made of five distinct parts: ''(a)'' the data management system, ''(b)'' the model management system, ''(c)'' the knowledge engine, ''(d)'' the user interface, and ''(e)'' the user(s).
  
  +
#:Sprague has suggested that there are three levels of hardware and software that has been proposed for DSS.
  +
#:a) Level 1 – Specific DSS
  +
#:This is the actual application that will be used to 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.
  +
#:b) Level 2 – DSS Generator
  +
#:This level 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]], iThink and Clementine.
  +
#:c) Level 3 – DSS Tools
  +
#:Contains lower level hardware/software. DSS generators including special languages, function libraries and linking modules
  +
# People Involved
  +
#:Sprague suggests there are 5 roles involved in a typical DSS development cycle.
  +
#:a) The end user.
  +
#:b) An intermediary.
  +
#:c) DSS developer
  +
#:d) Technical supporter
  +
#:e) Systems Expert
  +
# Developmental
  +
The developmental approach for a DSS system should be strongly iterative. This will allow for the application to be changed and redesigned at various intervals. The initial problem is used to design the system on and then tested and revised to ensure the desired outcome is achieved.
   
  +
== Classifying DSS ==
 
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.
  
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 (1996) 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.
 +
Holsapple and Whinston <ref name="Holsapple Whinston 1996">Holsapple, C.W., and A. B. Whinston. (1996). Decision Support Systems: A Knowledge-Based Approach. St. Paul: West Publishing. ISBN 0-324-03578-0</ref> 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 (1996).
 +
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 <ref name="Holsapple Whinston 1996" />.
   
−
The support given by DSS can be separated into three distinct. interrelated categories (Hackathorn and Keen, 1981): Personal Support, Group Support and Organizational Support.
 +
The support given by DSS can be separated into three distinct, interrelated categories <ref> 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.</ref>: Personal Support, Group Support, and Organizational Support.
   
−
Additionally, I classify DSS in a similar way. The build up of a DSS is classified into a few characteristics. 1) inputs: this is used so the DSS can have factors, numbers, and characteristics to analyze. 2) user knowledge and expertise: This allows the system to decide how much it is relied on, and exactly what inputs must be analyzed with or without the user. 3) Outputs/Feedback: This is used so the user of the system can analyze the decisions that may be made and then potentially 4) make a decision: This decision making is made by the DSS, however, it is ultimately made by the user in order to decide on which criteria it should use.
 +
Additionally, the build up of a DSS is also classified into a few characteristics. 1) inputs: this is used so the DSS can have factors, numbers, and characteristics to analyze. 2) user knowledge and expertise: This allows the system to decide how much it is relied on, and exactly what inputs must be analyzed with or without the user. 3) outputs: This is used so the user of the system can analyze the decisions that may be made and then potentially 4) make a decision: This decision making is made by the DSS, however, it is ultimately made by the user in order to decide on which criteria it should use.
  +
  +
DSSs which perform selected [[cognition|cognitive]] decision-making functions and are based on [[artificial intelligence]] or [[intelligent agent]]s technologies are called Intelligent Decision Support Systems (IDSS)<ref> Gadomski A.M. et al. (1998). Integrated Parallel Bottom-up and Top-down Approach to the Development of Agent-based Intelligent DSSs for Emergency Management,TIEMS98, Washington, [http://citeseerx.ist.psu.edu/viewdoc/summary;jsessionid=E0FBC31339B69898F066E3512C4F870F?doi=10.1.1.1.4313 CiteSeerx - alfa:] </ref>.
   
 
==Applications==
  
==Applications==
 
As mentioned above, there are theoretical possibilities of building such systems in any knowledge domain.
  
As mentioned above, there are theoretical possibilities of building such systems in any knowledge domain.
   
−
One of the examples is [[Clinical decision support system]] 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 costs.
 +
One example is the [[Clinical decision support system]] 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 costs.
   
  +
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.
⚫
A specific example concerns the [[Canadian National Railway]] system, which tests its equipment on a regular basis using a Decision Support System.
  
  +
  +
A growing area of DSS application, concepts, principles, and techniques is in agricultural production, marketing for sustainable development. For example, the DSSAT4 package<ref>[http://www.aglearn.net/resources/isfm/DSSAT.pdf DSSAT4 (pdf)]</ref><ref>[http://www.icasa.net/dssat/ The Decision Support System for Agrotechnology Transfer]</ref>, 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<ref> 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.</ref>.
  +
  ⚫
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, CN managed to decrease the incidence of derailments at the same time other companies were experiencing an increase.
  
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 increase.
   
−
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. DSS has endless possibilities that can be used anywhere and anytime, for its decision making needs.
 +
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.
   
  +
== Benefits of DSS ==
−
I hate this!
  
  +
# Improves personal efficiency
  +
# Expedites problem solving
  +
# 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 decision maker
  +
# Reveals new approaches to thinking about the problem space
  +
  +
  ⚫
== See also ==
  +
* [[Artificial intelligence]]
  +
* [[Computer applications]]
  +
* [[Databases]]
  ⚫
* [[Decision theory]]
  +
* [[Predictive analytics]]
  +
* [[Morphological Analysis]]
  +
* [[Clinical decision support system]]
   
 
== References ==
  
== References ==
  +
<references />
−
* Alter, S. L. (1980). Decision support systems : current practice and continuing challenges. Reading, Mass., Addison-Wesley Pub.
  
  +
−
* Druzdzel, M. J. and R. R. Flynn (1999). Decision Support Systems. Encyclopedia of Library and Information Science. A. Kent, Marcel Dekker, Inc.
  
  +
=== References not yet tagged in text ===
−
* Finlay, P. N. (1994). Introducing decision support systems. Oxford, UK Cambridge, Mass., NCC Blackwell; Blackwell Publishers.
  
  +
* Delic, K.A., Douillet,L. and Dayal, U. (2001) [http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=938098 "Towards an architecture for real-time decision support systems:challenges and solutions].
−
* Gachet, A. (2004). Building Model-Driven Decision Support Systems with Dicodess. Zurich, VDF.
  
  +
* Gadomski, A.M. et al.(2001) [http://citeseer.ist.psu.edu/701938.html "An Approach to the Intelligent Decision Advisor (IDA) for Emergency Managers].Int. J. Risk Assessment and Management, Vol. 2, Nos. 3/4.
 
* Gomes da Silva, Carlos; Clímaco, João; Figueira, José. European Journal of Operational Research.
  
* Gomes da Silva, Carlos; Clímaco, João; Figueira, José. European Journal of Operational Research.
  +
* Ender, Gabriela; E-Book (2005-2008) 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 http://www.openspace-online.com/OpenSpace-Online_eBook_en.pdf
⚫
* Haag, Cummings, McCubbrey, Pinsonneault, Donovan (2000). Management Information Systems: For The Information Age. McGraw-Hill Ryerson Limited: 136-140.
  
⚫
* Haettenschwiler, P. (1999). Neues anwenderfreundliches Konzept der Entscheidungsunterst¸tzung. Gutes Entscheiden in Wirtschaft, Politik und Gesellschaft. Zurich, vdf Hochschulverlag AG: 189-208.
  
−
* 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.
  
−
* Holsapple, C.W., and A. B. Whinston. (1996). Decision Support Systems: A Knowledge-Based Approach. St. Paul: West Publishing.
  
 
* Jiménez, Antonio; Ríos-Insua, Sixto; Mateos, Alfonso. Computers & Operations Research.
  
* Jiménez, Antonio; Ríos-Insua, Sixto; Mateos, Alfonso. Computers & Operations Research.
  +
* Jintrawet, Attachai (1995). A Decision Support System for Rapid Assessment of Lowland Rice-based Cropping Alternatives in Thailand. Agricultural Systems 47: 245-258.
−
* Keen, P. G. W. (1980). Decision support systems: a research perspective. Decision support systems : issues and challenges. G. Fick and R. H. Sprague. Oxford ; New York, Pergamon Press.
  
−
* Keen, P. G. W. and M. S. Scott Morton (1978). Decision support systems : an organizational perspective. Reading, Mass., Addison-Wesley Pub. Co.
 +
* Matsatsinis, N.F. and Y. Siskos (2002), Intelligent support systems for marketing decisions, Kluwer Academic Publishers.
⚫
* Marakas, G. M. (1999). Decision support systems in the twenty-first century. Upper Saddle River, N.J., Prentice Hall.
  
−
* Power, D. J. (1997). What is a DSS? The On-Line Executive Journal for Data-Intensive Decision Support 1(3).
  
 
* 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.
  
* 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.
−
* Power, D. J. (2002). Decision support systems : concepts and resources for managers. Westport, Conn., Quorum Books.
  
−
* Power, D.J. A Brief History of Decision Support Systems. DSSResources.COM, World Wide Web, http://DSSResources.COM/history/dsshistory.html, version 2.8, May 31, 2003.
 
 
* Reich, Yoram; Kapeliuk, Adi. Decision Support Systems., Nov2005, Vol. 41 Issue 1, p1-19, 19p.
 
* Reich, Yoram; Kapeliuk, Adi. Decision Support Systems., Nov2005, Vol. 41 Issue 1, p1-19, 19p.
−
* Sauter, V. L. (1997). Decision support systems : an applied managerial approach. New York, John Wiley.
 +
* Sauter, V. L. (1997). Decision support systems: an applied managerial approach. New York, John Wiley.
−
* Silver, M. (1991). Systems that support decision makers : description and analysis. Chichester ; New York, Wiley.
 +
* Silver, M. (1991). Systems that support decision makers: description and analysis. Chichester ; New York, Wiley.
−
* Sprague, R. H. and E. D. Carlson (1982). Building effective decision support systems. Englewood Cliffs, N.J., Prentice-Hall.
 +
* Sprague, R. H. and H. J. Watson (1993). Decision support systems: putting theory into practice. Englewood Clifts, N.J., Prentice Hall.
  +
−
* Sprague, R. H. and H. J. Watson (1993). Decision support systems : putting theory into practice. Englewood Clifts, N.J., Prentice Hall.
  
−
* Stanhope, P. (2002). Get in the Groove : building tools and peer-to-peer solutions with the Groove platform. New York, Hungry Minds.
  
−
* Turban, E. (1995). Decision support and expert systems : management support systems. Englewood Cliffs, N.J., Prentice Hall.
  
   
⚫
== See also ==
  
⚫
* [[Decision theory]]
  
⚫
* [[Land Allocation Decision Support System]]
  
−
* [[Online deliberation]]
  
   
 
==External links==
  
==External links==
−
* [http://www.macaulay.ac.uk/LADSS/ Land Allocation Decision Support System]
  
⚫
* [http://www.dssresources.com DSSResources.COM]
  
−
* [http://www.sis.pitt.edu/~genie/ Genie]
 
−
* [http://www.dicodess.org Dicodess]
  
−
* [http://www.GIDEONonline.com GIDEON] - Global Infectious Diseases and Epidemiology Network
  
−
* [http://www.iisy.com iisy AG] - intelligent information systems
  
−
* [http://www.celsi.ch/eval_eng.htm CelsiEval] - Decision Support Tool
  
−
* [http://www.swemorph.com/pdf/iccrts1.pdf Strategic Decision Support using Computerised Morphological Analysis] From the [http://www.swemorph.com Swedish Morphological Society]
  
   
  +
* [http://www.elsevier.com/wps/find/journaldescription.cws_home/505540/description#description Elsevier DSS Publications]
  +
* [http://www.icasa.net/ DSSAT4] - the University of Hawaii
  +
* [http://www.cet.sunderland.ac.uk/webedit/CET/research/dss.htm CET] - University of Sunderland
  +
* [http://www.cet.sunderland.ac.uk/chaman/ CHAMAN] - Integrated supply chain management system example
  ⚫
* [http://www.dssresources.com/ DSSResources.com] - A DSS knowledge repository maintained by Professor Dan Power
  +
* [http://dsslab.cs.unipi.gr/index.php?lang=en DSS Lab ] - Decision Support Systems Laboratory, Department of Informatics, University of Piraeus
   
   
−
[[Category:Information systems]]
 +
[[Category:Artificial intelligence]]
  +
[[Category:Computer software]]
  +
[[Category:Decision support systems]]
 
[[Category:Decision theory]]
  
[[Category:Decision theory]]
  +
[[category:Expert systems]]
  +
[[Category:Information systems]]
  +
[[Category:Knowledge engineering]]
   
  +
<!--
−  
  +
[[cs:Systémy pro podporu rozhodování]]
 
[[de:Entscheidungsunterstützungssystem]]
  
[[de:Entscheidungsunterstützungssystem]]
 
[[es:Sistemas de soporte a decisiones]]
  
[[es:Sistemas de soporte a decisiones]]
−
[[fr:Informatique décisionnelle]]
 +
[[fr:Aide à la décision]]
  +
[[id:Sistem pendukung keputusan]]
−
[[he:DSS]]
  
 
[[it:Decision support system]]
  
[[it:Decision support system]]
  +
[[he:מערכת תומכת החלטה]]
⚫
[[ru:Системы поддержки принятия решений]]
  
  ⚫
[[nl:Decision Support Systems]]
  +
[[ja:意思決定支援システム]]
  +
[[no:Beslutningsstøtteverktøy]]
  +
[[pt:Sistema de suporte à decisão]]
  ⚫
[[ru:Система поддержки принятия решений]]
  +
[[sr:Sistemi za podršku odlučivanju]]
  +
[[uk:Системи підтримки прийняття рішень]]
 
[[zh:决策支持系统]]
  
[[zh:决策支持系统]]
  +
-->
−  
 
{{enWP|Decision support system}}
  
{{enWP|Decision support system}}

Latest revision as of 10:57, 24 December 2011

Assessment | Biopsychology | Comparative | Cognitive | Developmental | Language | Individual differences | Personality | Philosophy | Social |
Methods | Statistics | Clinical | Educational | Industrial | Professional items | World psychology |

Statistics: Scientific method · Research methods · Experimental design · Undergraduate statistics courses · Statistical tests · Game theory · Decision theory

Decision support systems are a form of expert system, a computer software based information system including knowledge-based systems that support decision-making activities. They do this by facilitating the computer simulation of the outcomes of alternative decision choices,

Definition

Decision Support Systems (DSS) are a specific class of computerized information system that supports 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 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 be:

  • 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 brief history

In the absence of an all-inclusive definition, we focus on the history of DSS (see also Power[1]). According to Keen [2], 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 support systems (GDSS), and organizational decision support systems (ODSS) evolved from the single user and model-oriented DSS. 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.

It is clear that DSS belong to an environment with multidisciplinary foundations, including (but not exclusively) database research, artificial intelligence, human-computer interaction, simulation 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 environment.

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.

Taxonomies

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 [3] 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 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 communication-driven DSS, data-driven DSS, document-driven DSS, knowledge-driven DSS, and model-driven DSS.[4]

  • 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 [5].
  • A communication-driven DSS supports more than one person working on a shared task; examples include integrated tools like Microsoft's NetMeeting or Groove[6]
  • 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.[4]

Using scope as the criterion, Power [7] 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.

Architectures

Once again, different authors identify different components in a DSS. For example, Sprague and Carlson [8] identify three fundamental components of DSS: (a) the database management system (DBMS), (b) the model-base management system (MBMS), and (c) the dialog generation and management system (DGMS).

  • Haag et al. [9] describe these three components in more detail:

The Data Management Component stores information (which can be further subdivided into that derived from an organization's traditional data repositories, from external sources such as the Internet, or from the personal insights and experiences of individual users); the Model Management Component handles representations of events, facts, or situations (using various kinds of models, two examples being optimization models and goal-seeking models); and the User Interface Management Component is, of course, the component that allows a user to interact with the system.

  • According to Power [4], academics and practitioners have discussed building DSS in terms of four major components: (a) the user interface, (b) the database, (c) the model and analytical tools, and (d) the DSS architecture and network.
  • Hättenschwiler [3] identifies five components of DSS:

(a) users with different roles or functions in the decision making process (decision maker, advisors, domain experts, system experts, data collectors),

(b) a specific and definable decision context,

(c) a target system describing the majority of the preferences,

(d) a knowledge base made of external data sources, knowledge databases, working databases, data warehouses and meta-databases, mathematical models and methods, procedures, inference and search engines, administrative programs, and reporting systems, and

(e) a working environment for the preparation, analysis, and documentation of decision alternatives.

  • arakas [10] proposes a generalized architecture made of five distinct parts:

(a) the data management system,

(b) the model management system,

(c) the knowledge engine,

(d) the user interface, and

(e) the user(s).

Development Frameworks

DSS systems are not entirely different from other systems and require a structured approach. A framework was provided by Sprague and Watson (1993). The framework has three main levels. 1. Technology levels 2. People involved 3. The developmental approach

  1. Technology Levels
    Sprague has suggested that there are three levels of hardware and software that has been proposed for DSS.
    a) Level 1 – Specific DSS
    This is the actual application that will be used to 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.
    b) Level 2 – DSS Generator
    This level 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, iThink and Clementine.
    c) Level 3 – DSS Tools
    Contains lower level hardware/software. DSS generators including special languages, function libraries and linking modules
  2. People Involved
    Sprague suggests there are 5 roles involved in a typical DSS development cycle.
    a) The end user.
    b) An intermediary.
    c) DSS developer
    d) Technical supporter
    e) Systems Expert
  3. Developmental

The developmental approach for a DSS system should be strongly iterative. This will allow for the application to be changed and redesigned at various intervals. The initial problem is used to design the system on and then tested and revised to ensure the desired outcome is achieved.

Classifying DSS

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 [11] 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 [11].

The support given by DSS can be separated into three distinct, interrelated categories [12]: Personal Support, Group Support, and Organizational Support.

Additionally, the build up of a DSS is also classified into a few characteristics. 1) inputs: this is used so the DSS can have factors, numbers, and characteristics to analyze. 2) user knowledge and expertise: This allows the system to decide how much it is relied on, and exactly what inputs must be analyzed with or without the user. 3) outputs: This is used so the user of the system can analyze the decisions that may be made and then potentially 4) make a decision: This decision making is made by the DSS, however, it is ultimately made by the user in order to decide on which criteria it should use.

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)[13].

Applications

As mentioned above, there are theoretical possibilities of building such systems in any knowledge domain.

One example is the Clinical decision support system 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 costs.

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.

A growing area of DSS application, concepts, principles, and techniques is in agricultural production, marketing for sustainable development. For example, the DSSAT4 package[14][15], 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[16].

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, CN managed to decrease the incidence of derailments at the same time other companies were experiencing an increase.

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.

Benefits of DSS

  1. Improves personal efficiency
  2. Expedites problem solving
  3. Facilitates interpersonal communication
  4. Promotes learning or training
  5. Increases organizational control
  6. Generates new evidence in support of a decision
  7. Creates a competitive advantage over competition
  8. Encourages exploration and discovery on the part of the decision maker
  9. Reveals new approaches to thinking about the problem space


See also

References

  1. ↑ Power, D.J. A Brief History of Decision Support Systems DSSResources.COM, World Wide Web, version 2.8, May 31, 2003.
  2. ↑ Keen, P. G. W. (1978). Decision support systems: an organizational perspective. Reading, Mass., Addison-Wesley Pub. Co. ISBN 0-201-03667-3
  3. ↑ 3.0 3.1 Haettenschwiler, P. (1999). Neues anwenderfreundliches Konzept der Entscheidungsunterstützung. Gutes Entscheiden in Wirtschaft, Politik und Gesellschaft. Zurich, vdf Hochschulverlag AG: 189-208. Cite error: Invalid <ref> tag; name "Haettenschwiler 1999" defined multiple times with different content
  4. ↑ 4.0 4.1 4.2 Power, D. J. (2002). Decision support systems: concepts and resources for managers. Westport, Conn., Quorum Books.
  5. ↑ Gachet, A. (2004). Building Model-Driven Decision Support Systems with Dicodess. Zurich, VDF.
  6. ↑ Stanhope, P. (2002). Get in the Groove: building tools and peer-to-peer solutions with the Groove platform. New York, Hungry Minds
  7. ↑ Power, D. J. (1997). What is a DSS? The On-Line Executive Journal for Data-Intensive Decision Support 1(3).
  8. ↑ Sprague, R. H. and E. D. Carlson (1982). Building effective decision support systems. Englewood Cliffs, N.J., Prentice-Hall. ISBN 0-130-86215-0
  9. ↑ Haag, Cummings, McCubbrey, Pinsonneault, Donovan (2000). Management Information Systems: For The Information Age. McGraw-Hill Ryerson Limited: 136-140. ISBN 0-072-81947-2
  10. ↑ Marakas, G. M. (1999). Decision support systems in the twenty-first century. Upper Saddle River, N.J., Prentice Hall.
  11. ↑ 11.0 11.1 Holsapple, C.W., and A. B. Whinston. (1996). Decision Support Systems: A Knowledge-Based Approach. St. Paul: West Publishing. ISBN 0-324-03578-0
  12. ↑ 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.
  13. ↑ Gadomski A.M. et al. (1998). Integrated Parallel Bottom-up and Top-down Approach to the Development of Agent-based Intelligent DSSs for Emergency Management,TIEMS98, Washington, CiteSeerx - alfa:
  14. ↑ DSSAT4 (pdf)
  15. ↑ The Decision Support System for Agrotechnology Transfer
  16. ↑ 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.

References not yet tagged in text

  • Delic, K.A., Douillet,L. and Dayal, U. (2001) "Towards an architecture for real-time decision support systems:challenges and solutions.
  • Gadomski, A.M. et al.(2001) "An Approach to the Intelligent Decision Advisor (IDA) for Emergency Managers.Int. J. Risk Assessment and Management, Vol. 2, Nos. 3/4.
  • Gomes da Silva, Carlos; Clímaco, João; Figueira, José. European Journal of Operational Research.
  • Ender, Gabriela; E-Book (2005-2008) 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 http://www.openspace-online.com/OpenSpace-Online_eBook_en.pdf
  • Jiménez, Antonio; Ríos-Insua, Sixto; Mateos, Alfonso. Computers & Operations Research.
  • Jintrawet, Attachai (1995). A Decision Support System for Rapid Assessment of Lowland Rice-based Cropping Alternatives in Thailand. Agricultural Systems 47: 245-258.
  • Matsatsinis, N.F. and Y. Siskos (2002), Intelligent support systems for marketing decisions, Kluwer Academic Publishers.
  • 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. Decision Support Systems., Nov2005, Vol. 41 Issue 1, p1-19, 19p.
  • Sauter, V. L. (1997). Decision support systems: an applied managerial approach. New York, John Wiley.
  • Silver, M. (1991). Systems that support decision makers: description and analysis. Chichester ; New York, Wiley.
  • Sprague, R. H. and H. J. Watson (1993). Decision support systems: putting theory into practice. Englewood Clifts, N.J., Prentice Hall.


External links

  • Elsevier DSS Publications
  • DSSAT4 - the University of Hawaii
  • CET - University of Sunderland
  • CHAMAN - Integrated supply chain management system example
  • DSSResources.com - A DSS knowledge repository maintained by Professor Dan Power
  • DSS Lab - Decision Support Systems Laboratory, Department of Informatics, University of Piraeus
This page uses Creative Commons Licensed content from Wikipedia (view authors).
Community content is available under CC-BY-SA unless otherwise noted.