Decision Support System


Decision Support System: A Boon to Technology

It is very important to make the right decisions in business to climb the ladders of success. Making such critical decisions depend on the quality of data and the ability to analyze them. Taking such decisions is easier, if you use the decision support system. The decision support system abbreviated as DSS is a data-modeling method that analyzes business statistics and helps the users to take business related decisions effortlessly. DSS, a computer program application, generally gathers and presents the information given below:

  • A list of all the existing information assets
  • Comparative sales figures on a weekly basis
  • Apparent revenue figures as predicted from new product sales
  • The consequences of various decision alternatives

A decision support system can be used to present the information textually as well as graphically. It may even incorporate an Artificial Intelligence abbreviated as AI but it finally depends on the humans how they execute these strategies.

The decision support systems have been serving the mankind since 40 years. in the late 50s and early 60s, the DSS concept came into existence from the studies of organizational judgment making at Carnegie Institute of Technology. Researches on computerized quantitative models to help in decision making had begun in 1960s. However, a major turning point came by the researches of Michael S. Scott Morton at Harvard University in 1967. His study consisted of constructing, implementing and then testing a model-driven management DSS.

The journey of decision support systems has been a long one. In the 1970s, interactive computer-based system was developed, which facilitated the users to access the databases and solve the structure-related problems. In 1977, DSS was classified into two parts- institutional and ad hoc. Institutional DSS supports recurring decisions while the ad hoc ones support querying one time request data. In 1980, Steven Alter categorized decision support systems into seven types based on the operations, on which they could perform. These seven types comprised of the following:

  1. File Drawer Systems: facilitate access to data
  2. Data Analysis Systems: support the management of data
  3. Analysis Information Systems: facilitate access to multiple decision-oriented databases
  4. Financial and Accounting Models: calculate the results of different actions
  5. Representational Models: predict the consequences of proceedings on the basis of simulating models
  6. Optimization Models: provide strategies for action
  7. Suggestion Models: process logically for well-structured tasks and result in a specific decision

In 1981, DSS was further divided into three categories- Personal, Group and Organizational DSS. The first conference on an international level on DSS was held in Atlanta, Georgia in 1981. This conference proved to be much helpful for the subject, as it provided a forum for sharing of intellect ideas and exchange of information. In 1982, a book written by Ralph Sprague and Eric Carlson turned out to be a milestone. It provided a practical overview of how DSS should be built in organizations. In 1980s, decision support system got a lot of attention in the field of research. This decade marked the evolution of ODSS (Organizational Decision Support Systems), GDSS (Group Discussion Support Systems) and EIS (Executive Information Systems) from a single user DSS. In 1987, Gate Assignment Display Systems (GADS) was developed by Texas Instruments. This DSS proved its worth by reducing the number of travel delays. In 1990s, DSS expanded its empire with the introduction of OLAP (Online Analytical Processing) and data warehousing. With further development in technology, DSS also came forward with its new services.

There is no particular customized taxonomy of a decision support system. Various kinds of classification have been proposed by different authors, some of which has already been mentioned. Haettenschwiler classified DSS in to the following three types based on their relation with the users:

  1. Passive Decision Support System: This system aids decision-making, but still cannot suggest clear decision solutions.
  2. Active Decision Support System: This system offers explicit decision solutions.
  3. Cooperative Decision Support System: It facilitates the decision makers for altering or completing the suggestions offered by the systems prior to finalizing the decision. The complete process restarts until a satisfactory solution is developed.

Daniel Power suggested a different categorization. On the basis of the mode of criterion, Power categorized DSS as model-driven, communication-driven, data-driven, document-driven and knowledge-driven decision support systems.

  • Model-driven DSS: In this system financial, statistical, financial or simulation models are use to get a particular solution. This system uses parameters provided by the users to reach up to a proper conclusion. An example of a model-driven DSS is Dicodess.
  • Communication-driven DSS: In this model, many collaborators work collectively to reach up to a series of decisions, so that a particular genuine strategy can be followed. An example of this system is Groove.
  • Data-driven DSS: It emphasizes on manipulating collected data to fulfill the requirements of a decision-maker. The data can be internal or external, and even in a diversity of formats.
  • Document-driven DSS: It uses documents in various formats like database records, spreadsheets and text documents to come up to a final decision. Moreover, it even allows manipulating the data to refine strategies.
  • Knowledge-driven DSS: This system follows certain rules present in the computer or those specified by human beings to determine if a conclusion should be reached. In such support systems, the knowledge about a particular field and the skills to solve problems in those fields should be there. These systems are also called Suggestion –DSS or Knowledge-based DSS.

On the basis of scope, Power also classified Decision Support Systems as enterprise-wide DSS & desktop DSS. Enterprise wide DSS is connected to huge data warehouses. It is usually used to hand out many managers in a company. On the other hand, a desktop DSS is meant for a personal computer. It is generally used on a manager’s PC.

Decision Support Systems have faced many challenges and successfully overcome all of them. They have served a lot in the field of computers, information technology and have made decision making a lot easier.

DSS Architecture

Decision support systems are described in three parts: database, model and user interface. Even, users also constitute a very important part of the architecture of DSS.

The database system is a part of computer based decision support system. It is separated in two parts: Integrated decision support system and database management system. Data extracted from internal and external sources which can be accessed when required is kept in integrated decision support system database. Relational and multidimensional database are utilized from database management system.

Model is subdivided into several parts to make you understand easily. It is initially classified as model base, containing quantitative models.

User interface is used to offer communication between decision support system and the user. In reality user covenants with user interface only, so it is a necessary thing to make its design perfectly.

Development Frameworks

A structured approach is necessary for any system. Similarly, DSS also has a structured approach which includes technology, people and development approach. DSS technology is comprised of both hardware and software. It includes tangible application which is used by user. It is very helpful for the decision maker for making decisions in any specific area. It has creators which is a blend of hardware and utility, which lends a hand in the development of DSS. Case tools are used by this level for the expansion of DSS. Lower level hardware or software is used by these tools. Special languages and functional libraries are alsol used by DSS generators.

You can change and redesign DSS at various stages by using iterative development approach. To get the specific output as desired by you, you should properly test it after designing.

Classifying DSS

DSS applications can be classified in a number of ways. Usually, DSS is considered in a blend of two or more categories. According to Holsapple and Whinston, DSS can be classified into six subdivisions.

  1. Text oriented DSS
  2. Database oriented DSS
  3. Spreadsheet oriented DSS
  4. Solver oriented DSS
  5. Rule oriented DSS
  6. Compound DSS

Among all these compound DSS can be regarded as the most desired DSS because it can be treated as a hybrid structure which is a blend of two or more structures.

DSS provides you with three different types of support: Personal, Group and Organizational.

DSS components have too much importance and they are subdivided in four parts. First are inputs which include factors and characteristics, which are there to be analyzed. Second is user knowledge with expertise in which input is present. Third is output in which transformed data generated by DSS are present. Fourth is decision which is the result generated by DSS based on some specific criteria.

Intelligent Decision Support System is described as the DSS which executes discriminatory cognitive decision making functions. Such DSS is on the basis of artificial intelligence technologies.

Applications

DSS has a number of applications in the field of environmental decision making and assessment, water resource management, agriculture, forestry, manufacturing, medicine, business and organizational support and infrastructure. The list of references is not extensive but it demonstrates both range and interest in practical research in DSS. In the field of business and management DSS plays a very important role. It enhances the marketing for sustainable growth. Business performance software enhances the decision making factor, recognition of negative trend and better allotment of business resources.

DSS concepts, applications and principles are increasing their hold in different fields. DSS has its application areas in accounting, finance, human resources management, international business, information systems which includes data communication, DSS generators, systems analysis and design development, production and operation management, education, Health care, military, natural resources and urban and community planning also.

You should follow many constraints to have the successful adoptions on agriculture DSS. Canadian railway system is very popular example of application of DSS. This system uses a decision support system to check its equipments at regular interval of time. DSS helps in reduction of accidents as its check the derailments per year. While if you analyze other systems you will see the rate of accidents is increasing in others.

CACI has been commenced integrating model and a decision support system. Three simulation model levels are described by CACI. First level models are considered as customary level desktop simulation models, these are implemented within native software packages. Such systems require simulation experts to do run scenarios, modification and scrutinize results. Second level models used to embed modeling engines in web applications. These web applications are very supporting and permit the decision maker for making processes and even now you can change the parameters without taking assistance of any analyst. Third level models are also very much similar to the second level. It is also embedded in web applications but these have a bind with real time operational data. It facilitates automatic triggering.

DSS has so many applications. Clinical DSS for the medical diagnosis use DSS for its organizational maintenance. Wherever proper organization is required DSS is there to serve you with the best. A DSS can be intended to help you to take decisions about stock market.

Benefits of DSS

DSS endows with perceptibly better decisions. DSS also offers you cost savings of a business processing system. DSS helps in doing monotonous tasks and allows a decision maker to investigate a problem more systematically and carefully. Person efficiency is augmented by DSS. It solves complex multi faceted problems. It enhances learning and training and thus increases organizational management. You can profitably meet resource management challenges. DSS makes interpersonal communication easier and generates a competitive advantage on the competition. DSS enhances client organization and creates new evidence in favor of decision. It persuades exploration and reveals new approaches of thinking. It is having transparent NEPA analysis and helps in automation of managerial processes.

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