Decision Support system

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Decision Support System (DSS)A decision support system (DSS) is a computer program application that analyzes business data and presents it so that users can make business decisions more easily. decision support system (DSS) is a computer program application that analyzes business data and presents it so that users can make business decisions more easily. It is an "informational application" (to distinguish it from an "operational application" that collects the data in the course of normal business operation).Typical information that a decision support application might gather and present would be:• Comparative sales figures between one week and the next• Projected revenue figures based on new product sales assumptions• The consequences of different decision alternatives, given past experience in a context that is described



Decision Support System (DSS)

A decision support system (DSS) is a computer program application that analyzes business data
and presents it so that users can make business decisions more easily.
decision support system (DSS) is a computer program application that analyzes business data
and presents it so that users can make business decisions more easily. It is an "informational
application" (to distinguish it from an "operational application" that collects the data in the
course of normal business operation).Typical information that a decision support application
might gather and present would be:

Comparative sales figures between one week and the next

Projected revenue figures based on new product sales assumptions

The consequences of different decision alternatives, given past experience in a context
that is described

A decision support system may present information graphically and may include an expert
system or artificial intelligence (AI). It may be aimed at business executives or some other group
of knowledge workers.
A Decision Support System (DSS) is a computer-based information system that supports
business or organizational decision-making activities. DSSs serve the management, operations,
and planning levels of an organization (usually mid and higher management) and help to make
decisions, which may be rapidly changing and not easily specified in advance (Unstructured and
Semi-Structured decision problems). Decision support systems can be either fully computerized,
human or a combination of both.
While academics have perceived DSS as a tool to support decision making process, DSS users
see DSS as a tool to facilitate organizational processes.[1] Some authors have extended the
definition of DSS to include any system that might support decision making.[2] Sprague (1980)
defines DSS by its characteristics:
1. DSS tends to be aimed at the less well structured, underspecified problem that upper
level managerstypically face;

2. DSS attempts to combine the use of models or analytic techniques with traditional data
access and retrieval functions;
3. DSS specifically focuses on features which make them easy to use by noncomputer
people in an interactive mode; and
4. DSS emphasizes flexibility and adaptability to accommodate changes in
the environment and the decision making approach of the user.
DSSs include knowledge-based systems. A properly designed DSS is an interactive softwarebased system intended to help decision makers compile useful information from a combination
of raw data, documents, and personal knowledge, or business models to identify and solve
problems and make decisions.
Typical information that a decision support application might gather and present includes:

inventories of information assets (including legacy and relational data sources,
cubes, data warehouses, and data marts),

comparative sales figures between one period and the next,

projected revenue figures based on product sales assumptions.

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 Technology in the 1960s.[3] 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)[4] 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". In the 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 towards the end of 1980s DSS faced a new
challenge towards the design of intelligent workstations.[4]

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 DenverColorado. 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.
The advent of better and better reporting technologies has seen DSS start to emerge as a critical
component of management design. Examples of this can be seen in the intense amount of
discussion of DSS in the education environment.
DSS also have a weak connection to the user interface paradigm of hypertext. Both
the University of Vermont PROMIS system (for medical decision making) and the Carnegie
Mellon ZOG/KMS system (for military and business decision making) were decision support
systems which also were major breakthroughs in user interface research. Furthermore,
although hypertext researchers have generally been concerned with information overload, certain
researchers, notably Douglas Engelbart, have been focused on decision makers in particular.
Using the relationship with the user as the criterion,
Haettenschwiler[6] differentiates passive, active, and cooperative DSS. A passive DSS is a system
that aids the process of decision making, but that cannot bring out explicit decision suggestions
or solutions. An active DSS can bring out such decision suggestions or solutions. A cooperative
DSSallows the decision maker (or its advisor) to modify, complete, or refine the decision
suggestions provided by the system, before sending them back to the system for validation. The
system again improves, completes, and refines the suggestions of the decision maker and sends
them back to them for validation. The whole process then starts again, until a consolidated
solution is generated.
Another taxonomy for DSS has been created by Daniel Power. Using the mode of assistance as
the criterion, Power differentiates communication-driven DSS, data-driven DSS,document-driven
DSS, knowledge-driven DSS, and model-driven DSS.

A communication-driven DSS supports more than one person working on a shared task;
examples include integrated tools like Google Docs or Groove

A data-driven DSS or data-oriented DSS emphasizes access to and manipulation of
a time series of internal company data and, sometimes, external data.

A document-driven DSS manages, retrieves, and manipulates unstructured information
in a variety of electronic formats.

A knowledge-driven DSS provides specialized problem-solving expertise stored as facts,
rules, procedures, or in similar structures.

A model-driven DSS emphasizes access to and manipulation of a statistical, financial,
optimization, or simulation model. Model-driven DSS use data and parameters provided by
users to assist decision makers in analyzing a situation; they are not necessarily dataintensive. Dicodess is an example of an open source model-driven DSS generator.[9]

Using scope as the criterion, Power[10] differentiates enterprise-wide DSS and desktop DSS.
An enterprise-wide DSS is linked to large data warehouses and serves many managers in the
company. A desktop, single-user DSS is a small system that runs on an individual manager's PC.
Three fundamental components of a DSS architecture are:
1. the database (or knowledge base),
2. the model (i.e., the decision context and user criteria), and
3. the user interface.
The users themselves are also important components of the architecture.
Development frameworks
DSS systems are not entirely different from other systems and require a structured approach.
Such a framework includes people, technology, and the development approach.
The Early Framework of Decision Support System consists of four phases:
Intelligence Searching for conditions that call for decision.
Design Developing and analyzing possible alternative actions of solution.
Choice Selecting a course of action among those.
Implementation Adopting the selected course of action in decision situation.

DSS technology levels (of hardware and software) may include:
1. The actual application that will be used by the user. This is the part of the application that
allows the decision maker to make decisions in a particular problem area. The user can
act upon that particular problem.
2. Generator contains Hardware/software environment that allows people to easily develop
specific DSS applications. This level makes use of case tools or systems such as
Crystal, Analytica and iThink.
3. Tools include lower level hardware/software. DSS generators including special
languages, function libraries and linking modules
An iterative developmental approach allows for the DSS to be changed and redesigned at various
intervals. Once the system is designed, it will need to be tested and revised where necessary for
the desired outcome.
There are several ways to classify DSS applications. Not every DSS fits neatly into one of the
categories, but may be a mix of two or more architectures.
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.
DSS components may be classified as:
1. Inputs: Factors, numbers, and characteristics to analyze
2. User Knowledge and Expertise: Inputs requiring manual analysis by the user
3. Outputs: Transformed data from which DSS "decisions" are generated
4. 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)[16]
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.
As mentioned above, there are theoretical possibilities of building such systems in any
knowledge domain.
One is the clinical decision support system for medical diagnosis. There are four stages in the
evolution of clinical decision support system (CDSS). The primitive version is standalone which
does not support integration. The second generation of CDSS supports integration with other
medical systems. The third generation is standard-based while the fourth is service model-based.

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. Due to DSS all the information from any organization is
represented in the form of charts, graphs i.e. in a summarized way, which helps the management
to take strategic decision.
A growing area of DSS application, concepts, principles, and techniques is in agricultural
production, marketing for sustainable development. For example, the DSSAT4package,[18]
developed through financial support of USAID during the 80s and 90s, has allowed rapid
assessment of several agricultural production systems around the world to facilitate decisionmaking at the farm and policy levels. There are, however, many constraints to the successful
adoption on DSS in agriculture.[20]
DSS are also prevalent in forest management where the long planning horizon and the spatial
dimension of planning problems demands specific requirements. All aspects of Forest
management, from log transportation, harvest scheduling to sustainability and ecosystem

protection have been addressed by modern DSSs. In this context the consideration of single or
multiple management objectives related to the provision of goods and services that traded or
non-traded and often subject to resource constraints and decision problems. The Community of
Practice of Forest Management Decision Support Systems provides a large repository on
knowledge about the construction and use of forest Decision Support Systems.[21]
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 anyrailroad 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.
1. Improves personal efficiency
2. Speed up the process of decision making
3. Increases organizational control
4. Encourages exploration and discovery on the part of the decision maker
5. Speeds up problem solving in an organization
6. Facilitates interpersonal communication
7. Promotes learning or training
8. Generates new evidence in support of a decision
9. Creates a competitive advantage over competition
10.Reveals new approaches to thinking about the problem space
11.Helps automate managerial processes
12.Create Innovative ideas to speed up the performance
1. Solve semi-structured and unstructured problems

2. Support managers at all levels
3. Support individuals and groups
4. Interdependence and sequence of decisions
5. Support Intelligence, Design, Choice
6. Adaptable and flexible
7. Interactive and ease of use
8. Interactive and efficiency
9. Human control of the process
10.Ease of development by end user
11.Modeling and analysis
12.Data access
13.Standalone and web-based integration
14.Support varieties of decision processes
15.Support varieties of decision trees
16.Quick response
Types of Decision Support Systems (DSS)

by Dan Power
Decision Support Systems (DSS) are a class of computerized information system that
support decision-making activities. DSS are interactive computer-based systems and

subsystems intended to help decision makers use communications technologies, data,
documents, knowledge and/or models to complete decision process tasks.
A decision support system may present information graphically and may include an
expert system or artificial intelligence (AI). It may be aimed at business executives or
some other group of knowledge workers.
Typical information that a decision support application might gather and present
would be, (a) Accessing all information assets, including legacy and relational data
sources; (b) Comparative data figures; (c) Projected figures based on new data or
assumptions; (d) Consequences of different decision alternatives, given past
experience in a specific context.
There are a number of Decision Support Systems. These can be categorized into five
 Communication-driven DSS
Most communications-driven DSSs are targetted at internal teams, including
partners. Its purpose are to help conduct a meeting, or for users to collaborate.
The most common technology used to deploy the DSS is a web or client server.
Examples: chats and instant messaging softwares, online collaboration and netmeeting systems.
 Data-driven DSS
Most data-driven DSSs are targeted at managers, staff and also product/service
suppliers. It is used to query a database or data warehouse to seek specific
answers for specific purposes. It is deployed via a main frame system,
client/server link, or via the web. Examples: computer-based databases that
have a query system to check (including the incorporation of data to add value
to existing databases.
 Document-driven DSS
Document-driven DSSs are more common, targeted at a broad base of user
groups. The purpose of such a DSS is to search web pages and find documents
on a specific set of keywords or search terms. The usual technology used to set
up such DSSs are via the web or a client/server system. Examples:
 Knowledge-driven DSS:
Knowledge-driven DSSs or 'knowledgebase' are they are known, are a catch-all
category covering a broad range of systems covering users within the
organization seting it up, but may also include others interacting with the

organization - for example, consumers of a business. It is essentially used to
provide management advice or to choose products/services. The typical
deployment technology used to set up such systems could be slient/server
systems, the web, or software runnung on stand-alone PCs.
 Model-driven DSS
Model-driven DSSs are complex systems that help analyse decisions or choose
between different options. These are used by managers and staff members of a
business, or people who interact with the organization, for a number of
purposes depending on how the model is set up - scheduling, decision analyses
etc. These DSSs can be deployed via software/hardware in stand-alone PCs,
client/server systems, or the web.

MIS - Decision Support System
Decision support systems (DSS) are interactive software-based systems intended to help
managers in decision-making by accessing large volumes of information generated from various
related information systems involved in organizational business processes, such as office
automation system, transaction processing system, etc.
DSS uses the summary information, exceptions, patterns, and trends using the analytical models. A
decision support system helps in decision-making but does not necessarily give a decision itself.
The decision makers compile useful information from raw data, documents, personal knowledge,
and/or business models to identify and solve problems and make decisions.

Programmed and Non-programmed Decisions
There are two types of decisions - programmed and non-programmed decisions.
Programmed decisions are basically automated processes, general routine work, where:

These decisions have been taken several times.

These decisions follow some guidelines or rules.

For example, selecting a reorder level for inventories, is a programmed decision.

Non-programmed decisions occur in unusual and non-addressed situations, so:

It would be a new decision.

There will not be any rules to follow.

These decisions are made based on the available information.

These decisions are based on the manger's discretion, instinct, perception and judgment.

For example, investing in a new technology is a non-programmed decision.
Decision support systems generally involve non-programmed decisions. Therefore, there will be no
exact report, content, or format for these systems. Reports are generated on the fly.

Attributes of a DSS

Adaptability and flexibility

High level of Interactivity

Ease of use

Efficiency and effectiveness

Complete control by decision-makers

Ease of development


Support for modeling and analysis

Support for data access

Standalone, integrated, and Web-based

Characteristics of a DSS

Support for decision-makers in semi-structured and unstructured problems.

Support for managers at various managerial levels, ranging from top executive to line

Support for individuals and groups. Less structured problems often requires the involvement
of several individuals from different departments and organization level.

Support for interdependent or sequential decisions.

Support for intelligence, design, choice, and implementation.

Support for variety of decision processes and styles.

DSSs are adaptive over time.

Benefits of DSS

Improves efficiency and speed of decision-making activities.

Increases the control, competitiveness and capability of futuristic decision-making of the

Facilitates interpersonal communication.

Encourages learning or training.

Since it is mostly used in non-programmed decisions, it reveals new approaches and sets
up new evidences for an unusual decision.

Helps automate managerial processes.

Components of a DSS
Following are the components of the Decision Support System:

Database Management System (DBMS): To solve a problem the necessary data may
come from internal or external database. In an organization, internal data are generated by
a system such as TPS and MIS. External data come from a variety of sources such as
newspapers, online data services, databases (financial, marketing, human resources).

Model Management System: It stores and accesses models that managers use to make
decisions. Such models are used for designing manufacturing facility, analyzing the financial
health of an organization, forecasting demand of a product or service, etc.
Support Tools: Support tools like online help; pulls down menus, user interfaces, graphical
analysis, error correction mechanism, facilitates the user interactions with the system.

Classification of DSS
There are several ways to classify DSS. Hoi Apple and Whinstone classifies DSS as follows:

Text Oriented DSS: It contains textually represented information that could have a bearing
on decision. It allows documents to be electronically created, revised and viewed as

Database Oriented DSS: Database plays a major role here; it contains organized and
highly structured data.

Spreadsheet Oriented DSS: It contains information in spread sheets that allows create,
view, modify procedural knowledge and also instructs the system to execute self-contained
instructions. The most popular tool is Excel and Lotus 1-2-3.

Solver Oriented DSS: It is based on a solver, which is an algorithm or procedure written for
performing certain calculations and particular program type.

Rules Oriented DSS: It follows certain procedures adopted as rules.

Rules Oriented DSS: Procedures are adopted in rules oriented DSS. Export system is the

Compound DSS: It is built by using two or more of the five structures explained above.

Types of DSS
Following are some typical DSSs:

Status Inquiry System: It helps in taking operational, management level, or middle level
management decisions, for example daily schedules of jobs to machines or machines to

Data Analysis System: It needs comparative analysis and makes use of formula or an
algorithm, for example cash flow analysis, inventory analysis etc.

Information Analysis System: In this system data is analyzed and the information report is
generated. For example, sales analysis, accounts receivable systems, market analysis etc.

Accounting System: It keeps track of accounting and finance related information, for
example, final account, accounts receivables, accounts payables, etc. that keep track of the
major aspects of the business.

Model Based System: Simulation models or optimization models used for decision-making
are used infrequently and creates general guidelines for operation or management.

Decision Support System

o Making decisions concerning complex systems often strains our cognitive
o Predicting how the system will react to an external manipulation such as a policy
decision is often difficult.
5. Decision support system
o Decision Support Systems (DSS) are a specific class of computerized information
system that supports business and organizational decision-making activities .
o A properly designed DSS is an interactive software-based system intended to help
decision makers compile useful information from raw data, documents, personal
knowledge, and/or business models to identify and solve problems and make
decisions .
o Because decision-making involves a complex sequence of activities over time, it
implies there are at least three functions that should be assigned to DSSs:

o 1) The capability of capturing and saving information from previous activities;
o 2) Data processing capability;
o 3) Data retrieval capability.
o There are three fundamental components of DSS :
9. Types of DSS
o DS systems can be separated into seven broad categories, each aiding decision
making by different methods:
o Communication Driven DSS.
o On-line Analytical Processing (OLAP)
14. Intangible payoffs DSS
o Improved internal control.
o Enhances long term profitability by improving quality of decision making.
o Faster response to changes in business environment .
15. Cont ….
o Better management awareness of internal strength and weaknesses and external
threats and opportunities.

o Allows managers’ to record experience and use it in future for similar scenarios
that may arise within the organization.

What are characteristics of a decision support system?
by Dan Power
How do you know a computerized system is a decision support system (DSS)? This question is important
because knowledge transfer is more meaningful when we classify and categorize. Identifying characteristics,
distinguishable features, attributes or aspects of all DSS helps distinguish such systems from other systems.
Once accurate classification occurs, we are more likely to identify patterns and generalizations. In general,
information systems that provide decision relevant information and results are decision support systems. DSS
are interactive computer-based systems and subsystems intended to help decision-makers. These definitions
include a number of characteristics. My approach has been to use a "big tent" definition and include business
intelligence systems, some workflow systems, groupware, conferencing software, management expert
systems and model-based analytic systems as decision support systems. So what are the major
characteristics of DSS?
Alter (1980) identified three major characteristics of DSS:
1. DSS are designed specifically to facilitate decision processes,
2. DSS should support rather than automate decision making, and
3. DSS should be able to respond quickly to the changing needs of decision makers.
Clyde Holsapple and Andrew Whinston (1996) identified four characteristics one should expect to observe in
a DSS (see pages 144-145). Their list is very general and provides an even broader perspective on the DSS
concept. Holsapple and Whinston specify that a DSS must have a body of knowledge, a record-keeping
capability that can present knowledge on an ad hoc basis in various customized ways as well as in
standardized reports, a capability for selecting a desired subset of stored knowledge for either presentation
or for deriving new knowledge, and must be designed to interact directly with a decision maker in such a
way that the user has a flexible choice and sequence of knowledge-management activities.
Turban and Aronson (1995) and others try to narrow the "population of systems" called DSS. Turban and
Aronson define DSS as “an interactive, flexible, and adaptable CBIS specially developed for supporting the
solution of a nonstructured management problem for improved decision making (p. 77)”. A few paragraphs
later, they broaden the definition and define 13 characteristics and capabilities of DSS. Their first
characteristic is “DSS provide support for decision makers mainly in semistructured and unstructured

situations by bringing together human judgment and computerized information. Such problems cannot be
solved (or cannot be solved conveniently) by other computerized systems or by standard quantitative
methods or tools”. Their list is a useful starting point.
Turban and Aronson note their list is an ideal set. They state “Because there is no consensus on exactly what
a DSS is, there is obviously no agreement on standard characteristics and capabilities of DSS”. This
conceptual confusion and lack of consensus on a well defined DSS concept originally prompted me in 1995 to
try to more systematically define and categorize DSS. It seems impossible to conduct meaningful scientific
research about systems that can't be consistently identified and categorized. A more consistent definition of
DSS and set of “characteristics” should also improve communications about these important computerized
systems with students and DSS practioners.
The following is my list of the characteristics of a DSS.

Facilitation. DSS facilitate and support specific decision-making activities and/or decision


Interaction. DSS are computer-based systems designed for interactive use by decision makers or
staff users who control the sequence of interaction and the operations performed.


Ancillary. DSS can support decision makers at any level in an organization. They are NOT intended
to replace decision makers.


Repeated Use. DSS are intended for repeated use. A specific DSS may be used routinely or used as
needed for ad hoc decision support tasks.


Task-oriented. DSS provide specific capabilities that support one or more tasks related to decisionmaking, including: intelligence and data analysis; identification and design of alternatives; choice among
alternatives; and decision implementation.


Identifiable. DSS may be independent systems that collect or replicate data from other information
systems OR subsystems of a larger, more integrated information system.


Decision Impact. DSS are intended to improve the accuracy, timeliness, quality and overall
effectiveness of a specific decision or a set of related decisions.

Alter, S. Decision Support Systems: Current Practice and Continuing Challenges. Reading, Mass.: AddisonWesley, Inc., 1980.
Holsapple, C. W. and A. B. Whinston. Decision Support Systems: A Knowledge Based Approach. Minneapolis,

MN.: West Publishing, Inc., 1996.
Power, D. J., Decision Support Systems: Concepts and Resources for Managers, Westport, CT:
Greenwood/Quorum Books, 2002.
Sprague, R. H. and E. D. Carlson. Building Effective Decision Support Systems Englewood Cliffs, N.J.:
Prentice-Hall, Inc.: 1982.
Turban, E. and J. E. Aronson Decision Support and Intelligent Systems. (5th edition) Upper Saddle River, N.J.:
Prentice-Hall, Inc.: 1995
The above response is based upon Power, D., What are the characteristics of a Decision Support System? DSS
News, Vol. 4, No. 7, March 30, 2003.

Decision Support System Aims
Information Technology Essay
When it comes to making decisions regarding complex systems such as managing organizational
operations, industrial processes, or investment portfolios; command and control of military units; or
the operational control of nuclear power generating plants our cognitive capabilities get pushed to
the limit. Individual interactions amongst a system's variables may be well understood, but predicting
how the system will react to an external manipulation such as a policy decision is often quite a
daunting task. What might be, for example, the impact of starting an extra shift in a factory? This
action might be expected to increase the plant's output by 50%. Additional consideration will be
required for factors such as additional wages, machine wear down, maintenance breaks, raw
material usage, supply logistics, and future demand; as they all will have some impact on the total
financial outcome of this decision. A lot of variables are involved in complex and quite often subtle
interdependencies and foreseeing the total outcome may prove to be a daunting task.

Find out more from UK Essays here:
There is a lot of practical evidence that shows that our conclusions, judgments and decisions are far
from perfect and they deteriorate even further when complexity and stress are involved. In many
situations, the quality of our decisions is of the utmost importance and that is why this area has
always been a top priority of scientists. Disciplines such as statistics, economics and operations
research have emerged which help us in making the right choice. Recently, these disciplines, in
collaboration with information technology, cognitive psychology and artificial intelligence, have
equipped us with computer softwares. These softwares can act as stand-alone programs or in

conjunction with other softwares in the form of an integrated computing environment which helps in
complex decision making. These environments are more commonly known as decision support
systems (DSSs). The DSS concept covers a very broad spectrum and various authors have defined
it in their own particular way. If we wanted to define the DSSs in such a way as to roughly cover all
the current definitions, we could say that a decision support system is a computer oriented system
that interacts with its users and provides them help in making more informed, accurate decisions and
to make the most rational choice amongst different scenarios. DSSs are sometimes also referred to
as knowledge-based systems. This reference is in accordance with the actions being taken to make
the domain knowledge susceptible to mechanized reasoning.
Decision support systems are gaining widespread popularity in domains such as businesses,
engineering, military, and medicine. They are of paramount importance in situations where the
variables involved exceed greatly the decision making capabilities of an un-aided human decision
maker. When precision is of the utmost importance, DSSs come in with all the help we require.
Decision support systems help us in overcoming our limitations by integrating various sources of
information so that intelligible knowledge can be extracted from them. On the basis of this
knowledge, the system helps us to make the right choice from a pool of viable alternatives. Methods
such as engineering, economics, operations research, statistics, and decision theory make the
backbone of these systems. These systems have gone further to employ artificial intelligence which
has enabled these systems to apply heuristic methods where the formal ones fail to give results.
Equipped with such a strong arsenal at their disposal, decision makers of today have helped to
increase productivity, efficiency and effectiveness of various businesses and have thus provided
them with a comparative advantage over their competitors. This advantage allows them to go the
extra mile in making the right choices regarding their operations, research and development and

Problem Statement
In Pakistan Businesses are not used to Technology, so the ability of making better decisions
effectually and timely in not up to the mark. But when we start taking help of technology lke in our
case use of Decision Support System in decision making process. The process become easy and
effective and then ultimately increase in Organization performance achieved.
In Telecom sector of Pakistan DSS is used up to some extend. The benefit of using it result in Better
Decision making or Organization Performance.
So the Problem statement of this Thesis is on the use of DSS and its positive impact on the decision
making and organization performance

Literature Review
The purpose of this Literature review is to provide bases for the later study on the topic of DSS and
Decision Making which then lead to improvement in the Organization Performance, for this purpose
Articles of well known Professors were studied.
Human Judgment and Decision Making; "It has been rather convincingly demonstrated in numerous
empirical studies that human judgment and decision making is based on intuitive strategies as
opposed to theoretically sound reasoning rules. These intuitive strategies, referred to as judgmental
heuristics in the context of decision making, help us in reducing the cognitive load, but alas at the
expense of optimal decision making. Effectively, our unaided judgment and choice exhibit
systematic violations of probability axioms" (Marek J. Druzdzel and Roger R. Flynn, University of
The desire to improve human decision making provided motivation for the development of a variety
of modeling tools in disciplines of economics, operational research, decision theory, decision
analysis, and statistics. Each of these tools, knowledge is represented by the use of algebraic,
logical or variables. Interactions between these are shown with the help of equations or order logical
rules, Once a model has been formulated, a variety of mathematical methods can be used to
analyze it. Decision making under certainty has been addressed by economic and operations
research methods, such as cash flow analysis, break-even analysis, scenario analysis,
mathematical programming, inventory techniques, and a variety of optimization algorithms for
scheduling and logistics. Decision making under uncertainty enhances the above methods with
statistical approaches, such as reliability analysis, simulation, and statistical decision making.

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"AÂ Decision Support System (DSS)Â is a class of information systems (including but not limited to
computerized systems) that support business and organizational decision-making activities. A
properly designed DSS is an interactive software-based system intended to help decision makers
compile useful information from a combination of raw data, documents, personal knowledge, or
business models to identify and solve problems and make decisions."( John Day Reservoir)
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
Hogue and Watson (1983) reported the most important reason managers cited for using a DSS was
to obtain accurate information. Studies of model-driven DSS have examined this outcome more than
research on other types of DSS (cf., Sharda, Barr, and McDonnell, 1988). Advocates of building data
warehouses identify the possibility of more and better analyses.
In today's decision-making, it is necessary to reach for information. However, it is knowledge that
has to be mainly looked for. Knowledge provides foundations for effective business activities.
Procedural knowledge (explaining how to perform tasks and follow procedures) should be
accompanied by declarative knowledge (indicating what has to be done), semantic knowledge
(depicting relations between facts) and casuistic knowledge (that refers to some cases from the
past). So-called tacit knowledge is a large part of knowledge in an organization. Organizations that
are interested to use knowledge in decision-making are forced to work out procedures that enable
them to transform tacit knowledge into explicit knowledge. In this situation, organizations find it
necessary to create repositories of knowledge and knowledge management systems,
simultaneously finding the way to match them with decision support systems.
In large enterprises, huge volumes of data are generated and consumed, and substantial fractions of
the data change rapidly. Business managers need up-to-date information to make timely and sound
business decisions. Unfortunately, conventional decision support systems do not provide the low
latencies needed for decision making in this rapidly changing environment. So importance of using a
computer base system which helps in decision making increases and DSS is one mode.

Research Methodology
Research Design
The method of research used for this study was descriptive. To define the descriptive type of
research, Creswell (1994) stated that the descriptive method of research is to gather information
about the present existing condition. Emphasis is laid on the descriptions rather than on judgements
or interpretations. By using the descriptive method, we aim to achieve a verification of the formulated
hypothesis in reference to the present situation so that it may be elucidated. This approach allows
more flexibility for the introduction of new questions and issues into the study as they arise. This
helps in conducting further investigations regarding important matters.
This method focuses on describing the nature of the situation. The researcher tries to gain
knowledge about the current situation. He does this by profiling people, events and situations. For
this kind of research, the researcher tries to gather first hand raw data. This can be collected from
various sources such as respondents. This approach allows him to formulate his own opinions and

conclusions which are not affected by any other factor. Therefore the results produced are unbiased,
free from any external influence and represent solely the views of the researcher in their purest form.
For the purpose of this study, the descriptive method of research was used in showing that a positive
co-relation exists between organisation performance and the use of the decision support system.
This method was chosen for its flexibility in providing the researcher with the option to work with both
quantitative and qualitative data. This opens up a whole new world of possibilities for the researcher
to gather first hand information using an array of data gathering tools. The study further aims to
provide the merits and demerits of using a DSS. As the researcher wishes to make knowledgeable
conclusions using first hand data therefore the descriptive research method is best equipped to meet
his needs.

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Employees from 3 telecom companies in Lahore are being used as respondents. The idea is to
identify the similarities and dissimilarities in the answers provided by the respondents. To benefit fully
from the choice of research methodology, it was decided to work with both quantitative and
qualitative data. This would not only help to eliminate the discrepancies in each but also provide the
researcher with the full merits of both types of data. The primary sources of data were the
respondents which took part in the survey. Secondary data was
collected from published annual results of the telecom companies working with and without an
effective DSS.
Quantitative methods used in this research relied solely on the figures provided in the published
documents. The relationship between variables was studied without any context and conclusions
were reached which were unbiased and achieved with the help of techniques such as measurement,
analysis of numerical data and the use of statistical methods.
This approach was helpful as it allowed the researcher to compile and present data in an unbiased
manner. Subjectivity was avoided in measuring the relationships between the isolated pre-chosen
The control over observations, the large number of surveys and allowed research manipulation
make the compiled qualitative data more reliable. But to avoid subjectivity of opinion, one must make
use of the quantitative methods. This leads to the conclusions and discussion being more objective.
Both types of variables (dependant and independent) are made use of and are stated clearly in the
study. It was observed by Fryer (1991) that researchers using qualitative methods aim to decipher,
explain, scrutinize and infer precisely the meaning of a certain phenomenon happening in their
traditional social contexts.

Dissimilar to the quantitative method, qualitative method creates verbal information rather than
numerical values (Polgar & Thomas, 1995). As an alternative to using statistical analysis, the
qualitative method makes use of content or holistic analysis; to clarify and understand the research
conclusions, inductive reasoning is used. The concept behind quantitative research method is that
measurement is well-founded, dependable and can be comprehensive with its clear expectation of
reason and consequence (Cassell & Symon, 1994). The scientific assumption of a quantitative
approach carries no value. This implies that the researcher's own views, slanted preferences and
biases are not pertinent to this type of research methodology.
The researcher chose to incorporate the qualitative method in this study due to its noteworthy
advantages. The use of qualitative data compiling method is beneficial as they are more open to
changes and enhancement of research information as the study progresses; this implies that
qualitative data compiling instruments are extremely flexible. Furthermore, no manipulation of the
research setting is required with this technique; Instead of employing various research controls such
as in untried approaches, the qualitative data compiling ways are only focused on understanding the
phenomenon taking place in their naturally occurring states. Apart from these merits, researchers
use qualitative data-gathering methods as some preceding researchers believe that qualitative data
are particularly attractive as they provide rich and well-grounded depictions and understanding as
well as unpredicted results for new theory
creation. One of the distinguished merits of the qualitative tools is that they provide a more practical
feeling of the research setting which otherwise cannot be obtained from statistical analysis.

In order to determine whether DSS aim to support better decision making which lead to better
Organisation performance, a total of 30 to 40 respondents were asked to participate. To achieve
relevant information, specific inclusion criteria were forced. The participants eligible for sample
selection were supposed to be employees of their particular companies. This condition ensured that
the participants understand the nature of the DSS questionnaire and its use for the company, making
the survey items easy for them to complete. The respondents were selected from 3 companies in
Lahore, thus, a total of 13 employees were selected for every company
Simple random sampling was used for sample selection. This sampling method ensured that each
member of the population had an equal opportunity to become part of the sample. Since all
members of the population had an equivalent chance of becoming a research partaker, this turned
out to be the most efficient sampling procedure. To accomplish this sampling strategy, firstly the
researcher defined the population, listed down all the members of the population and then selected
members using random sampling to make the sample. The lottery sampling or the fish bowl method
was used for the purpose of this procedure. This method involved the use of random numbers for
selection of the sample. The employees were assigned specific numbers and then their numbers
were drawn from a box. The entire sample was drawn using this method.

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The questionnaire for the survey was the main data-gathering tool for this study. The questionnaire
was sub-divided into two main parts: there was a profile and then the survey proper. The profile
section contained socio-demographic characteristics of the members such as age, sex, job positions
and years of service for the company. The main part of the survey investigated the awareness of
employees on DSS questionnaire. The questions were formed on the basis of the Likert format. Four
options were given for every question or statement. The
options represented the level of agreement each member had on the given question. The following
scale was used to infer the total responses of all the members for each question by calculating the
weighted mean:

Range                  Â
3.01 - 4.00Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â
Strongly Agree
2.01 - 3.00Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Agree
1.01 - 2.00Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â
Disagree      Â
0.00 - 1.00Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â
Strongly Disagree

The Likert survey was selected as it allowed the respondents to answer the survey quite easily. In
addition to that, this research instrument further allowed the researcher to carry out the quantitative
method effectively by using statistics for data interpretation. Before distributing the questionnaire to
the participants, it was tested by giving it to 10 random people. Feedback was collected from them

regarding the relevancy and clarity of the questions. Changes were made according to the feedback
to make the questionnaire easier to understand and answer.

Data Processing and Analysis
The completed questionnaires were collected and the total responses from each questionnaire were
tabulated. For using the interpretation of the Linkert-scale, weighted means representing each
question were calculated. In this process, weights are assigned to each quantity. These weights give
representation to the significance of the quantities in the average. Each value is multiplied by its
weight to calculate the weighted mean. The multiplied results are then added. The weights are also
added. The sum of the products is then divided by the sum of the weights to obtain the final
weighted average.

Use of Dss has many more benefits but due to the limitation of time and resources this thesis will on
focus on Telecom sector of Pakistan and use of Dss resultion in Better Decision Making and
Organization Performance.
Other benefit of use Dss are as follows
Time savings. For all categories of decision support systems, research has demonstrated and
substantiated reduced decision cycle time, increased employee productivity and more timely
information for decision making. The time savings that have been documented from using
computerized decision support are often substantial. Researchers have not however always
demonstrated that decision quality remained the same or actually improved.
Enhance effectiveness. A second category of advantage that has been widely discussed and
examined is improved decision making effectiveness and better decisions. Decision quality and
decision making effectiveness are however hard to document and measure. Most research has
examined soft measures like perceived decision quality rather than objective measures.
Improve interpersonal communication. DSS can improve communication and collaboration among
decision makers. In appropriate circumstances, communications-driven and group DSS have had
this impact. Model-driven DSS provide a means for sharing facts and assumptions. Data-driven DSS
make "one version of the truth" about company operations available to managers and hence can
encourage fact-based decision making. Improved data accessibility is often a major motivation for
building a data-driven DSS. This advantage has not been adequately demonstrated for most types
of DSS.

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Competitive advantage. Vendors frequently cite this advantage for business intelligence systems,
performance management systems, and web-based DSS. Although it is possible to gain a
competitive advantage from computerized decision support, this is not a likely outcome. Vendors
routinely sell the same product to competitors and even help with the installation. Organizations are
most likely to gain this advantage from novel, high risk, enterprise-wide, inward facing decision
support systems. Measuring this is and will continue to be difficult.
Cost reduction. Some research and especially case studies have documented DSS cost saving from
labor savings in making decisions and from lower infrastructure or technology costs. This is not
always a goal of building DSS.
Increase decision maker satisfaction. The novelty of using computers has and may continue to
confound analysis of this outcome. DSS may reduce frustrations of decision makers, create
perceptions that better information is being used and/or create perceptions that the individual is a
"better" decision maker. Satisfaction is a complex measure and often researchers measure
satisfaction with the DSS rather than satisfaction with using a DSS in decision making. Some studies
have compared satisfaction with and without computerized decision aids. Those studies suggest the
complexity and "love/hate" tension of using computers for decision support.
Promote learning. Learning can occur as a by-product of initial and ongoing use of a DSS. Two types
of learning seem to occur: learning of new concepts and the development of a better factual
understanding of the business and decision making environment. Some DSS serve as "de facto"
training tools for new employees. This potential advantage has not been adequately examined.
Increase organizational control. Data-driven DSS often make business transaction data available for
performance monitoring and ad hoc querying. Such systems can enhance management
understanding of business operations and managers perceive that this is useful. What is not always
evident is the financial benefit from increasingly detailed data. Regulations like Sarbanes-Oxley often
dictate reporting requirements and hence heavily influence the control information that is made
available to managers. On a more ominous note, some DSS provide summary data about decisions
made, usage of the systems, and recommendations of the system. Managers need to be very
careful about how decision-related information is collected and then used for organizational control
purposes. If employees feel threatened or spied upon when using a DSS, the benefits of the DSS
can be reduced. More research is needed on these questions.

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