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Decision Support System
The objective of Decision Support System (DSS) is to support managers in their work, especially decision making.
Decision support system (DSS) tends to overlap both transaction processing systems and office support systems. It acquire much of its data from routine transaction processing and the results of analysis performed on such data may be included in reports prepared by the office support system, for example, word processor or spreadsheet.
Decision support system (DSS) tends to be used in planning, modeling, analyzing alternatives, and decision making.
Decision support system (DSS) are especially useful for semi-structured problems where problem solving is improved by interaction between manager and the computer system. The emphasis is on small, simple models which can easily be understood and used by the manager rather than complex integrated systems which need information specialist to operate them.
What distinguished MIS from a decision support system (DSS) is flexibility. The format and types of information on MIS are predefined, but the format and types of information in decision support system (DSS) are not.
In decision support system (DSS), users are provided with the capabilities to generate their own information usually in their own way.
A computer base system which is easy to use, that helps decision makers to confront ill-structured problems through direct interaction with data and analysis models.
A decision support system (DSS) might allow a manager to sit at an interactive terminal and browse through data, analyze them and create specially tailored reports rather than consisting of semi-frozen set of data or information's outputs as TPS and MIS do.
The decision support system (DSS) does not make a decision for manager, but provide tools for enhancing user decision making. The objective is to allow the manager to consider a number of alternatives and evaluate them under a variety of potential conditions.
Applications of Decision Support System (DSS):
DSS are men/machine systems and are suitable for semi-structured problems. The problems must be important to the manager and the decision required must be a key one. In addition, if an interactive computer based system is to be used then some of the following criteria should be met.
a. There Should Be A Large Database:
A database is an organized collection of structured data with a minimum duplication of data items. The database is common to all users of the system but is independent of the programs which use the data. If the database is too large for manual searching then a computer supported approach may be worthwhile.
b. Large Amount Of Computation Or Data Manipulation Required:
Where analysis of the problem requires considerable computation or data manipulation, computing power Is likely to be beneficial.
c. Complex Inter-Relationships:
Where there is a large database or where there are numerous factors involved. It is difficult to frequently asses all the possible inter-relationships without computer assistance.
d. Analysis By Stages:
Where the problem is an interactive one with stages for re examination and re-assessment, it becomes more difficult to deal with manually. The computer based model can answer the questions, quickly and effectively.
Where several people are involved in the problem solving process each contributing some special expertise, then the coordinating power of the computer can be helpful.
It follows from the above criteria that decision support system (DSS) are inappropriate for unstructured problems and unnecessary for completely structure problems, because, these can be dealt with wholly by the men/machine interaction.
Components of Decision Support System (DSS):
AS per the above discussion, DSS is basically the interaction of man and machine.
It means that there are three components of decision support system (DSS).
a. Decision maker (user/manager)
Functions of Decision Support System (DSS) Tools:
In this section we will look at some of the tasks commonly performed by decision support systems.
1. Information Retrieval:
Information retrieval in DSS environment refers to the act of extracting information from a database for the purpose of making decisions. Usually, the sequence of retrievals made by the user is unanticipated. For example, the manager may see a few startling pieces of information on the display and, as a result of these, suddenly produce a report that provides more detail about the situation.
2. Data Reconfiguration:
Often managers using a DSS want information in a form other that that in which the data are logically represented within the computer system. The ability to reconfigure data makes it possible for managers and other decision makers to look at existing data from alternative perspectives are, sorting, exchanging fields, joining, and presentation graphics.
Sorting data Involves rearranging records in a file or a subset of a file so that they appear in a specific order.
b. Exchanging Fields:
Exchanging fields or columns is another method available for reconfiguring data. You can hide or replace the position of the column so that they can appear in the order which you think is useful.
Joining enables users to cut and paste data from different existing logical files to form a new logical file.
d. Presentation Graphics:
Presentation graphic tools allow users to put data into a graphical form that can be easily understood. When working with graphics, users will typically have a choice of several types of graphs or charts, as well as coloring and pattern designs for each graphic element.
3. Calculator Activities:
Calculator activities refer to the set of tasks that normally can be done with a calculator. These activities are generally implemented either by heaving the user write out a complete formula, specifying all the variables involved and how arithmetic operations should be performed on them, or by having the set of functions resident in the DSS or DSS tools
Arithmetic & Statistical Functions:
Functions are pre-stored formulas that enable a user to perform a calculator type task as soon as the function is invoked, using a function, for instance, the user can add numbers in a column, calculate average, standard deviation, net present value, minimum & maximum values, square root, log, etc.
Analysis refer to using a decision support system (DSS) to review a set of facts and to assist in drawing conclusions based on there facts. Because the decision support environment is semi-structured, both the user and machine interact in this process. Four widely used types of decision support system (DSS) analysis tools, or techniques brought by users to the decision support system (DSS) environment are statistical tools, optimizing tools, what if analysis, and artificial intelligence routines.
c. Statistical Tools:
Statistical tools enable users to perform a variety of statistical operations on data on well as to do a number of other data handling tasks, such as, distributing data information categories of the user's own choice. Statistical analysis normally include regression analysis (make predictions from a set of data), correlation analysis (tools used to find the strength of association among data), and a variety of statistical inference methodologies (procedures such as analysis of variance, tests, and confidence limits; they can be used to determine whether a conclusion drawn from data is statistically significant).
Statistical methods are usually descriptive or predictive in nature; i.e. is they describe patterns among data or forecast events based on present or past happenings. Some of the most widely used statistical tools in business are the variety of regression routines used for forecasting purpose. These are particularly valuable when strategic plans are being formulated.
d. Optimizing Tools:
Optimizing tools are useful for deriving the best solution in certain structured decisions usually at tactical and operational levels. At the operational level, many decisions are structured and are frequently incorporated into computer based systems. Because the circumstances are well defined, optimizing techniques (Linear programming) are frequently used. Optimizing tools are used where it is required to optimize the value of a single objective (e.g. maximize contribution) where the factors .invoked (e.g. labor hours, machine capacity etc.) are subject to some constraints or limitation. It can be used to solve problems which:
Can be stated in numerical terms.
All factors have linear relationships.
Permit a choice between alternatives.
Have one or more restrictions on the factors involved.
e. What-if analysis: (Sensitivity Analysis)
Asses risk with the help of DSS tools is known as sensitivity analysis. At its simplest this means, holding all the variables, bar one, constant and altering that one variable step and noting the effect on the result. One object of sensitivity analysis is to identify the "Critical" or "Sensitive" variables, which are those variables which have a more than proportionate effect on the result.
For example, a simulation of an investment program might include factors such as; cost per unit, price per unit, volume sold, amount of investment and as on.
It might be useful for a bank manager to know how much change should be expected in the profitability of a project if mortgage rates are changed by 1/2% in the next month or a chemical producer might want to know that $60,000/- per month could be saved in production costs if only the availability of a raw material could be increased by 10 percent.
In short, sensitivity analysis or what it analysis is to find out how sensitive a solution suggested by a model, is to changes in the model parameters.
A great number of such "what if" questions can be asked and answered by DSS models quickly as:
What if sales growth per month is nil, 1/2%, 1 & 1/2%, 2% or minus 1% etc.
What if the debt-to-capital ratio is 10% or high?
What happens if sales projections are 10% or low?
What if bank borrowing rates increase by 2%?
What if product development is delayed by a year?
Refiguring calculations on the basis of all these what ifs can take weeks to do by hand, but they are standard in the decision support system (DSS) world. For each what-if query, all the manger need is to change an underlying assumption or the value of a parameter and re-run the model. At the end of the interactive session several of these runs can be used to make decisions and, perhaps, convince other management members of the appropriateness of the chosen action.
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