Because ther is no consensus on exactly what constitutes a Decision Support Systems, there obviously is no agreement on the characteristics and capabilities of DSSs. However, most DSSs at least have some of the attributes. DSSs also employ mathematical models and have a related, special capability, known as sensitivity analysis.
Capabilities of a DSS
1. A DSS provides support for decision makers at all management levels, whether individuals or groups, mainly in semistructured and unstructured situations, by bringing together human judgement and objective information.
2. A DSS supports several interdependent and/or sequential decisions.
3. A DSS supports all phases of the decision-making process-intelligence, design, choice, and implementation-as well as a variety of decision making processes and styles.
4. A DSS is adaptable by the user over time to deal with changing conditions.
5. A DSS is easy to construct and use in many cases.
6. A DSS promotes learning, which leads to new demands and refinement of the current application, which leads to additional learning, and so forth.
7. A DSS usually utilizes quantitative models (standard and/or custom made).
8. Advanced DSSs are equipped with a knowledge management component that allows the efficient and effective solution of very complex problems.
9. A DSS can be disseminated for use via the web.
10. A DSS allows the easy execution of sensitivity analyses.
Sensitivity Analysis: â€œWhat-Ifâ€ and Goal Seeking
Sensitivity analysis is the study of the impact that changes in one (or more) parts of a model have on other parts. Usually, we check the impact that changes in input variables have on result variables.
Sensitivity analysis a extremely valuable in Decision Support Systems because it makes the system flexible and adaptable to changing conditions and to the varying requirements of different decision-making situations. It allows users to enter their own data, including the most pessimistic data (worst scenario) and to view how systems will behave under varying circumstances. It provides a better understanding of the model and the problem it purports to describe. It may increase the users confidence in the model, especially when the model is not so sensitive to changes. A sensitive models means that small changes in conditions dictate a different solution. In a nonsensitive model, changes in conditions do not significantly change the recommended solution. This means that the chances for a solution to succeed are very high. Two popular types of sensitivity analyses are what-if and goal seeking