Example of Decision Support System
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 etc.
History of Decision Support System
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 DSS movement started focusing 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.
Classification of Decision Support Systems
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
Components of a Decision Support System
DSS components may be classified as:
- Inputs: Factors, numbers, and characteristics to analyze. This is basically the raw data that you put to the system.
- 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.
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.
Some of the benefits of DSS are as follows
- 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