Decision support system: Map

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

Overview

A Decision Support System (DSS) is a class of information systems (including but not limited to computerized systems) that support business and organizational decision-making activities. A properly designed DSS is an interactive software-based system intended to help decision makers compile useful information from a combination of raw data, documents, personal knowledge, or business models to identify and solve problems and make decisions.

Typical information that a decision support application might gather and present are:

an inventory of all of your current information assets (including legacy and relational data sources, cubes, data warehouses, and data marts),
comparative sales figures between one week and the next,
projected revenue figures based on new product sales assumptions.

History

According to Keen (1978), the concept of decision support has evolved from two main areas of research: The theoretical studies of organizational decision making done at the Carnegie Institute of Technology during the late 1950s and early 1960s, and the technical work on interactive computer systems, mainly carried out at the Massachusetts Institute of Technology

in the 1960s. It is considered that the concept of DSS became an area of research of its own in the middle of the 1970s, before gaining in intensity during the 1980s. In the middle and late 1980s, executive information systems (EIS), group decision support systems (GDSS), and organizational decision support systems (ODSS) evolved from the single user and model-oriented DSS.

According to Sol (1987) the definition and scope of DSS has been migrating over the years. In the 1970s DSS was described as "a computer based system to aid decision making". Late 1970s the the DSS movement started focussing on "interactive computer-based systems which help decision-makers utilize data bases and models to solve ill-structured problems". In the 1980s DSS should provide systems "using suitable and available technology to improve effectiveness of managerial and professional activities", and end 1980s DSS faced a new challenge towards the design of intelligent workstations.

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

.

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 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.
A model-driven DSS emphasizes access to and manipulation of a statistical, financial, optimization, or simulation model. Model-driven DSS use data and parameters provided by users to assist decision makers in analyzing a situation; they are not necessarily data-intensive. Dicodess is an example of an open source model-driven DSS generator.

Using scope as the criterion, Power differentiates 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.

Architecture

Three fundamental components of a DSS architecture are:

the database (or knowledge base),
the model (i.e., the decision context and user criteria), and
the user interface.

The users themselves are also important components of the architecture.

Development Frameworks

DSS systems are not entirely different from other systems and require a structured approach. Such a framework includes people, technology, and the development approach.

DSS technology levels (of hardware and software) may include:

The actual application that will be used by the user. This is the part of the application that allows the decision maker to make decisions in a particular problem area. The user can act upon that 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, AIMMS, 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 for the desired outcome.

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.

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).

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.

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.

DSS are also prevalent in forest management where the long planning time frame demands specific requirements. All aspects of Forest management, from log transportation, harvest scheduling to sustainability and ecosystem protection have been addressed by modern DSSs. A comprehensive list and discussion of all available systems in forest management is being compiled under the COST action Forsys

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.

CACI has begun integrating simulation and decision support systems. CACI defines three levels of simulation model maturity. “Level 1” models are traditional desktop simulation models that are executed within the native software package. These often require a simulation expert to implement modifications, run scenarios, and analyze results. “Level 2” models embed the modeling engine in a web application that allows the decision maker to make process and parameter changes without the assistance of an analyst. “Level 3” models are also embedded in a web-based application but are tied to real-time operational data. The execution of “level 3” models can be triggered automatically based on this real-time data and the corresponding results can be displayed on the manager’s desktop showing the prevailing trends and predictive analytics given the current processes and state of the system. The advantage of this approach is that “level 1” models developed for the FDA projects can migrate to “level 2 and 3” models in support of decision support, production/operations management, process/work flow management, and predictive analytics. This approach involves developing and maintaining reusable models that allow decision makers to easily define and extract business level information (e.g., process metrics). “Level 1” models are decomposed into their business objects and stored in a database. All process information is stored in the database, including activity, resource, and costing data. The database becomes a template library that users can access to build, change, and modify their own unique process flows and then use simulation to study their performance in an iterative manner.

Benefits of DSS

Improves personal efficiency
Expedites problem solving (speed up the progress of problems solving in an organization)
Facilitates interpersonal communication
Promotes learning or training
Increases organizational control
Generates new evidence in support of a decision
Creates a competitive advantage over competition
Encourages exploration and discovery on the part of the decision maker
Reveals new approaches to thinking about the problem space
Helps automate the managerial processes.

References

Keen, P. G. W. (1978). Decision support systems: an organizational perspective. Reading, Mass., Addison-Wesley Pub. Co. ISBN 0-201-03667-3
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 9027724377. p.1-2.
Haettenschwiler, P. (1999). Neues anwenderfreundliches Konzept der Entscheidungsunterstützung. Gutes Entscheiden in Wirtschaft, Politik und Gesellschaft. Zurich, vdf Hochschulverlag AG: 189-208.
Power, D. J. (2002). Decision support systems: concepts and resources for managers. Westport, Conn., Quorum Books.
Stanhope, P. (2002). Get in the Groove: building tools and peer-to-peer solutions with the Groove platform. New York, Hungry Minds
Gachet, A. (2004). Building Model-Driven Decision Support Systems with Dicodess. Zurich, VDF.
Power, D. J. (1997). What is a DSS? The On-Line Executive Journal for Data-Intensive Decision Support 1(3).
Sprague, R. H. and E. D. Carlson (1982). Building effective decision support systems. Englewood Cliffs, N.J., Prentice-Hall. ISBN 0-130-86215-0
Haag, Cummings, McCubbrey, Pinsonneault, Donovan (2000). Management Information Systems: For The Information Age. McGraw-Hill Ryerson Limited: 136-140. ISBN 0-072-81947-2
Marakas, G. M. (1999). Decision support systems in the twenty-first century. Upper Saddle River, N.J., Prentice Hall.
Holsapple, C.W., and A. B. Whinston. (1996). Decision Support Systems: A Knowledge-Based Approach. St. Paul: West Publishing. ISBN 0-324-03578-0
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.
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:
DSSAT4 (pdf)
The Decision Support System for Agrotechnology Transfer
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.

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