Decision support system

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

Contents

[edit] Definitions

Because there are many approaches to decision-making and because of the wide range of domains in which decisions are made, the concept of decision support system (DSS) is very broad. A DSS can take many different forms. In general, we can say that a DSS is a computerized system used for supporting rather than automating decisions. A decision is a choice between alternatives based on estimates of the values of those alternatives. Supporting a decision means helping people working alone or in a group gather intelligence, generate alternatives and make choices. Supporting the choice making process involves supporting the estimation, the evaluation and/or the comparison of alternatives. In practice, references to DSS are usually references to computer applications that perform such a supporting role.[1]

The term decision support system has been used in many different ways (Alter 1980, Power, 2002) and has been defined in various ways depending upon the author's point of view [2]. Finlay [3] and others define a DSS rather broadly as "a computer-based system that aids the process of decision making." Turban [4] defines it more specifically 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."

Other definitions fall between these two extremes. For Little [5], a DSS is a "model-based set of procedures for processing data and judgments to assist a manager in his decision-making." For Keen [6], a DSS couples 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"). Moore and Chang [7] define DSS as extendible systems capable of supporting ad hoc data analysis and decision modeling, oriented toward future planning, and used at irregular, unplanned intervals. For Sprague and Carlson [8], DSS are "interactive computer-based systems that help decision makers utilize data and models to solve unstructured problems." In contrast, Keen [9] 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 [10], 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. [11]

Recommended reading: Druzdzel and Flynn (1999), Power (2000), Sprague and Watson (1993), the first chapter of Power (2002), the first chapter of Marakas (1999), the first chapter of Silver (1991), the first two chapters of Sauter (1997), and Holsaple and Whinston (1996).

[edit] A brief history

In the absence of an all-inclusive definition, we focus on the history of DSS (see also Power[11]). According to Keen [6], 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.

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.

[edit] Characteristics and Capabilities of DSS

Because there is no exact definition of DSS, there is obviously no agreement on the standard characteristics and capabilities of DSS. Turban, E.,Aronson, J.E., and Liang, T.P. [12] constitute an ideal set of characteristics and capabilities of DSS. The key DSS characteristics and capabilities are as follows:

  1. Support for decision makers in semistructured and unstructured problems.
  2. Support managers at all levels.
  3. Support individuals and groups.
  4. Support for interdependent or sequential decisions.
  5. Support intelligence, design, choice, and implementation.
  6. Support variety of decision processes and styles.
  7. DSS should be adaptable and flexible.
  8. DSS should be interactive and provide ease of use.
  9. Effectiveness balanced with efficiency (benefit must exceed cost).
  10. Complete control by decision-makers.
  11. Ease of development by (modification to suit needs and changing environment) end users.
  12. Support modeling and analysis.
  13. Data access.
  14. Standalone, integration and Web-based.

[edit] 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 [13] 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.

Using the mode of assistance as the criterion, Power [14] 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 [15].
  • A communication-driven DSS supports more than one person working on a shared task; examples include integrated tools like Microsoft's NetMeeting or Groove[16]
  • 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.[14]

Using scope as the criterion, Power [10] 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.

[edit] Architectures

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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. [17] 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 [14], 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 [13] 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 [18] 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).

[edit] 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 like Crystal
    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.

[edit] 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 [19] 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 [19].

The support given by DSS can be separated into three distinct, interrelated categories [20]: 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)[21].

[edit] Applications

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

Some 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.

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[22][23], 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[24].

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.

[edit] Benefits of DSS

  1. Improving Personal Efficiency
  2. Expediting Problem Solving
  3. Facilitating Interpersonal Communication
  4. Promoting Learning or Training
  5. Increasing Organizational Control

[edit] References

  1. ^ Alter, S. L. (1980). Decision support systems: current practice and continuing challenges. Reading, Mass., Addison-Wesley Pub.
  2. ^ Druzdzel, M. J. and R. R. Flynn (1999). Decision Support Systems. Encyclopedia of Library and Information Science. A. Kent, Marcel Dekker, Inc.
  3. ^ Finlay, P. N. (1994). Introducing decision support systems. Oxford, UK Cambridge, Mass., NCC Blackwell; Blackwell Publishers.
  4. ^ Turban, E. (1995). Decision support and expert systems: management support systems. Englewood Cliffs, N.J., Prentice Hall. ISBN 0-024-21702-6
  5. ^ Little, J.D.C.(1970, April). "Models and Managers:The Concept of a Decision Calculus." Management Science, Vol.16,NO.8
  6. ^ a b Keen, P. G. W. (1978). Decision support systems: an organizational perspective. Reading, Mass., Addison-Wesley Pub. Co. ISBN 0-201-03667-3
  7. ^ Moore, J.H.,and M.G.Chang.(1980,Fall)."Design of Decision Support Systems." Data Base,Vol.12, Nos.1 and 2.
  8. ^ a b 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. ^ 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.
  10. ^ a b Power, D. J. (1997). What is a DSS? The On-Line Executive Journal for Data-Intensive Decision Support 1(3).
  11. ^ a b Power, D.J. A Brief History of Decision Support Systems DSSResources.COM, World Wide Web, version 2.8, May 31, 2003.
  12. ^ Turban, E., Aronson, J.E., and Liang, T.P.(2005). Decision Support Systems and Intelligent Systems. New Jersey, Pearson Education, Inc.
  13. ^ a b Haettenschwiler, P. (1999). Neues anwenderfreundliches Konzept der Entscheidungsunterstützung. Gutes Entscheiden in Wirtschaft, Politik und Gesellschaft. Zurich, vdf Hochschulverlag AG: 189-208.
  14. ^ a b c Power, D. J. (2002). Decision support systems: concepts and resources for managers. Westport, Conn., Quorum Books.
  15. ^ Gachet, A. (2004). Building Model-Driven Decision Support Systems with Dicodess. Zurich, VDF.
  16. ^ Stanhope, P. (2002). Get in the Groove: building tools and peer-to-peer solutions with the Groove platform. New York, Hungry Minds
  17. ^ Haag, Cummings, McCubbrey, Pinsonneault, Donovan (2000). Management Information Systems: For The Information Age. McGraw-Hill Ryerson Limited: 136-140. ISBN 0-072-81947-2
  18. ^ Marakas, G. M. (1999). Decision support systems in the twenty-first century. Upper Saddle River, N.J., Prentice Hall.
  19. ^ 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
  20. ^ 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.
  21. ^ 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:
  22. ^ DSSAT4 (pdf)
  23. ^ The Decision Support System for Agrotechnology Transfer
  24. ^ 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.

[edit] 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 (2005-2007) E-Book about the OpenSpace-Online Real-Time Methodology: Knowledge-sharing, problem solving and results-oriented group dialogs in real-time about topics that matter. 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.

[edit] See also

[edit] External links