DECISION SUPPORT SYSTEMS

 A decision support system is a computer application that helps users analyze problems
and make business decisions more confidently. It uses data routinely collected in organizations
and special analysis tools to provide information support to complex decisions. For example, a
firm’s sales department may be interested in analyzing various sales decision options. The
decision support application might gather data, present the data graphically and help in
evaluating various options. It may use past sales figures, project sales based on sales
assumptions for each alternative considered and display information graphically. It may also use
artificial intelligence to enhance its decision support capability.

Decision Support System assists managers in making unstructured decisions. The system
enables them to interact with the database, model base and other software. It enables the users to
generate the information they need rather than depend on some reports produced according to
some anticipated information needs. DSS is more suited to handling unique and non-routine
decision problems. In many situations the problem itself may not be easily identified. Similarly,
identifying alternatives, identifying outcomes of each alternative considered, evaluation of
alternatives etc. pose problems to the decision maker. Each problem might require a different
approach to problem definition, analysis and resolution. Not only that it is difficult to solve such
problems, it is also possible that the decision process and solution vary with the decision maker.

DSS is designed to support managerial decision-making, usually, at middle and top levels
of management. Decisions made at the top level are mostly futuristic and non-repetitive in
nature. Such decision situations are highly uncertain and even specification of information
requirements for decisions are difficult. They are classified as non-programmable or
unstructured decision situations. The impact of such decisions will be seen throughout the
organization and cost of a wrong decision is usually very high, for example a decision to sell off
a line of business. This is in sharp contrast to programmable or structured decisions where the
decision procedure can be well defined and every information requirement can be pre-specified.
Most of the decisions taken at lower levels of management fall into this category. For example,
a decision to replenish stock of an inventory item is a highly structured decision taken at the
operational level. DSS is intended to help managers making unstructured decisions. ‘the system
includes a database, various models (Mathematical models for optimization etc.) and an interface
for the manager (usually a terminal) to interact with the system. The manager takes data from
the database, selects appropriate model or models and analyses the data using these models to
know the probable results of various actions.
DSS is thus an interactive computer system with many user-friendly features aimed at
helping non-computer specialist managers in making plans and decisions on their own. With the
recent advances in computing technology, particularly the powerful microcomputer and
interactive devices, the uses of DSS is expanding rapidly as these managers find it easy to access
databases and model base for retrieving and analyzing data.
DSS contains a database, models and data manipulation tools to help decision makers. It
is useful where decisions are semi-structured or unstructured. The decision rule for a structured
decision can be pre-specified. Hence, it is possible to automate such problem solving.

Intelligence activities are targeted at discovering problems of organizations. The
information reporting system can handle most of these information requirements. In the design
phase, alternative solutions to the problem identified are generated. This stage requires more
focused information and more intelligence based systems like DSS and Knowledge based
systems. Choice phase involves selecting the right alternative. This requires thorough
evaluation of the consequences of all the alternatives under consideration in terms of risk and
return, and its impact on problem area.

Decision implementation is a critical phase. Managers are anxious about the results of
decision implementation right from day one of implementation. In this phase, managers call for
information on implementation of decision such as stage of implementation, time and cost
involved, implementation constraints, and impact of implementation.
DSS can support repetitive or non-repetitive decision-making. It provides capabilities
for repetitive decision-making by defining procedures and formats. For example, an insurance
agent may use a DSS package to help clients in choosing insurance schemes. With the
privatization of insurance in the country, innovative insurance products are being introduced. An
investor will find it difficult to properly identify an insurance product matching his or her
requirements. The agent can carry a laptop with a DSS for insurance products to his clients. The
DSS can be used by the sales agent to demonstrate to the clients the details of each scheme in
terms of risk covered, bonus, maturity value, premiums etc. and help the clients arrive at their
decisions to purchase insurance policy.
DSS can also help non-routine decision-making. In fact its utility is high when nonrepetitive
decisions are made. For solving a non-repetitive problem, the DSS provides data,
models and interface methods to the user to select and analyze data. For example, a marketing
manager might want to analyze the potential demand for new products that the company is
planning to introduce. The marketing manager can use a DSS to forecast the demand using
relevant data about the market obtained form some database service firms like Centre for
Monitoring Indian Economy. The analysis will provide new insights into the market behaviour
and product performance that will help the manager in introducing new products into the market.

TYPES AND CHARACTERISTICS OF DSS

TYPES OF DSS

Basically, there are two types of DSS. One is data-driven DSS and the other is modeldriven
DSS. The data driven DSS, helps in generating useful information from huge quantity of

data in organizational databases, data warehouses and websites. Data mining techniques are
employed to generate useful information. The model-driven DSS contains mathematical models
to carry out “what if” analysis and sensitivity analysis.

CHARACTERISTICS OF DSS

1. It is designed and run by managers.
2. It contains a database drawn from internal files and external environment.
3. It focuses on decision processes rather than on transaction processing.
4. It is concerned with a small area of managerial activity or a small part of a large problem.
5. It permits managers to test the probable results of alternative decisions.
6. It supports decision-making, usually in solving semi-structured complex problems.
7. It helps in refining managerial judgment applied to problem solving.
8. It improves managerial decisions and thereby managerial effectiveness.
9. The decision maker retains control over decisions throughout the decision process.

COMPONENTS OF DSS

Components of DSS include the following
1. DSS Database:
DSS requires a database of its own. It can use data in organizational database. But this
will slow down the application. There is also a risk of DSS applications modifying data in
the organizational database. Hence, DSS generally uses data from its own database built up
from transaction data drawn from organizational database and other relevant data collected
from outside.
2. Model base:
It contains a collection of mathematical and analytical models that the DSS user may
want to analyze the data. Each DSS is designed for a specific purpose such as firefighting,
diagnostics, oil exploration etc. Hence, the models in the model base will vary from DSS to
DSS. Generally, it contains statistical models, optimization models, forecasting models etc.
3. The Hardware:
DSS requires hardware to provide the capability to the users to interact with the database
and models using DSS applications.
4. The User interface:
The user interface of DSS supports the interaction between the user and the data using the
models.
5. Analysis tools:
These include many tools which can be used at any level of management. The tools used
at the three levels of management in an organization are as follows:

• DSS Tools for operational Management

The operational management level requires assistance for day to day operations. The
areas where DSS can support operational management include:
· Material requirement planning
· Linear programming
· Queuing theory
· Analysis of variance
· Correlation analysis
· Descriptive statistics like mean, median etc.,

• DSS Tools for Tactical Planning and Management Control:

· Dynamic programming
· Regression analysis
· Correlation analysis
· Factor analysis
· Multidimensional Scaling
· Game theory
· Discriminate analysis
· Non-linear programming
· Network analysis

• DSS Tools for Strategic Planning:

· Expert systems
· Natural language systems

DSS CAPABILITIES

DSS has acquired many capabilities over the years. Some of the major capabilities are as
follows:
1. What if Analysis:
This helps in analyzing the cause-effect relationship of variables. For example, if
advertisement budget for a year is increased by 30% what will be its effect on total sales that
year?
2. Model Building:
Model building is a central task in most decision support systems. It involves
mathematically specifying the relationship between variables. For example, a sales forecasting
model based on advertisement expenditure will specify the relationship between sales and
advertisement in terms of mathematical statement as:
Sales = 10.94 X Advertisement expenditure
It means sales turnover will be 10.94 times the advertisement expenditure for the period.
3. Sensitivity Analysis:
This capability offers facility ot analyze the effect of multiple variables. For example,
what should be the cost of financing a project to get a return on investment of 20% after meeting
costs of interest and other operations.
4. Risk Analysis:
This facility provides a useful probability distribution to the decision maker to assess the
risk involved. For example, a probability distribution of profit helps the decision maker to
expect certain profit level with certain probability.
5. Goal Seeking Analysis:
This facility is the reverse of ‘what if’ analysis. It answers questions like what should be
the price per unit to generate a profit of Rs. 10, 00,000 from a project. This facility is normally
available in spreadsheets.
6. Graphic Capability:
This facility portrays data in the form of charts, graphs and diagrams to reveal underlying
trends and patterns.
7. Exception Reporting Analysis:
This facility tracks exceptions like over due accounts, production runs that result in more
power consumption than estimated, sales men who could not meet sales targets etc.
8. Hardware Capabilities:
It can be implemented on a wide rage of hardware configuration ranging form PC to
mainframes.
9. Access to Database:
DSS accesses data stored in databases also in external files. DSS tools have the
capability to maintain internal files once data is retrieved form other sources.

GROUP DECISION SUPPORT SYSTEM (GDSS)

GDSS is designed to support joint decision making by two or more individuals. The
decisions involved in GDSS are mostly unstructured. The group may make decisions in several
settings like board rooms, conferences, videoconferences etc. The decision makers are in
different places, yet the GDSS software brings them together in group decision making
environment. GDSS provides support by facilitating electronic exchange of comments, views,
suggestions and approval or disapproval. The system consists of advanced presentation devices,
access to database and facilities for the decision makers to communicate electronically. All the
participants in the group decision making are provided with computer-based support that
includes data management, retrieval, graphical presentation tools, decision analysis capability,
modeling etc.

TYPICAL GDSS CAPABILITIES INCLUDE

1. Display:
A work station screen or previously prepared presentation material.
2. Electronic Brainstorming:
Participants communicate comments electronically.
3. Topic Commenting:
Participants add comments to ideas suggested by others.
4. Issue Analysis: Participants
Identify and consolidate key items generated during electronic brainstorming.
5. Voting:
Participants use the computer to vote on topics with a choice or prioritization methods.
6. Alternative Evaluation:
The computer ranks alternative decisions based on preferences entered by participants.
DSS is a part of organizational MIS. MIS reports are still necessary for managers to
monitor the on going operations. DSS complements the reports by enabling managers to make
less structured decisions with greater confidence. DSS contains models, specialized database
and user interface. It helps the decision maker to interact with the data using the models and
generate information for solving semi-structured and unstructured problems. GDSS supports
joint decision-making by two or more individuals involving mostly unstructured problems in an
organizational setting.