Decision support systems

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Decision support systems constitute a class of computer-based information systems including knowledge-based systems that support decision-making activities.

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). According to Keen , 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 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.

• 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 .
• 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 formats.
• A knowledge-driven DSS provides specialized problem-solving expertise stored as facts, rules, procedures, or in similar structures.

Using scope as the criterion, Power 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 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. 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 , 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 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 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 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 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).

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, 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.

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

• Artificial intelligence
• Computer applications
• Computer simulation
• Databases
• Decision making
• Decision theory
• Information systems
• Predictive analytics
• Morphological Analysis
• Clinical decision support system

References

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