unit - 3 decision support systems

7/29/2019 Unit - 3 Decision Support Systems

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UNIT-3Decision Support Systems


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Topics to be covered

Definition of DSS

Features of DSS

Components of DSS

Database

Data Warehouse

MBMS

KBS User Interface / DSS Software

GDSS


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Definitions of DSS

Decision Support System is an interactive computer

based systems, which helps decision makers utilize data

and models to solve unstructured problems.

DSS couples the intellectual resources of individuals with

the capabilities of the computer to improve the quality of

the decisions. It is a computer based support system for

management decision makers who deal with the semi

structured problem.


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Definitions of DSS

DSS is an effective blend of human intelligence,

information technology and software which interact

closely to solve complex programs.

It is an information system that utilizes decision models,

a database, and a decision makers own insights in an ad

hoc (unplanned) basis, interactive analytical modeling

process to reach specific decision by a specific decision

maker.


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Definitions of DSS

A DSS is an interactive, flexible, and adaptable CBIS,

specially developed for supporting the solution of a non-

structured management problem for improved decision

making. It utilizes data, it provides easy user interface,

and it allows for the decision makers own insights

DSS may utilize models, is built by an interactive process

(frequently by end-users), supports all the phases of the

decision making, and may include a knowledge

component


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Structured versus UnstructuredDecisions

Structured decision are decision made for programmed and

pre-planned situation yet unstructured are decisions made for

emergent they are not pre-planned. For example, if fire break

there and then manager can make decision unplanned.Structured decisions are those types of decision which are

taken following a pre-established model (abstract

representation of reality). No brain storming is required in

taking decision rather establishing the model. Generally, line

managers take these types of decisions according the models

prepared by the top level managers to perform there job.


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Structured versus UnstructuredDecisions

Structured decisions are programmed where the

organization has already faced such situations. And the

employees are used to solving such problems. For instance

the hiring of new IT specialists in a firm. These decisions are

generally made for routine tasks.

Unstructureddecisions are made for a sudden one-shot kind

of situations. The situations are uncertain and unclear. These

decisions are the non-programmed decisions that arehappening for the first time. For instance, dealing with a

labour strike in a factory.


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Features of DSS

The key characteristics and capabilities of DSS :

The philosophy of DSS is to give the users the tools

necessary to analyze different types of data by using

easy sophisticated models. DSS is designed to delivercapabilities, not simply to respond to information needs.

DSS are aimed at middle and top management with

emphasis on change, flexibility, and quick response.

There is less emphasis on dealing with structuredinformation which generally prevails in lower level

management.


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Features of DSS

DSS designers must not only technically competent but

also be able to observe and understand the different

functional areas of business organization.

The technology required by DSS is based on easy and

flexible access. Thereby, reliable communication network,

availability of computer terminals are more important

than large scale data processing systems.


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Features of DSS

Support for decision makers, mainly in semi-structured

and unstructured situations, by bringing together human

judgment and computerized information.

Support for all managerial levels, ranging from top

executives to line managers.

Can be integrated with other DSS and/or applications,

and it can be distributed internally and externally, using

networking and Web technologies.


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Features of DSS

DSS is flexible, so users can add, delete, combine,

change, or rearrange basic elements; DSS can be

readily modified to solve other, similar problems. User-

friendliness, strong graphical capabilities, and a natural

language interactive humanmachine interface can

greatly increase the effectiveness of DSS.


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Features of DSS

The decision maker has complete control over all steps

of the decision-making process in solving a problem. End

users are able to develop and modify simple systems by

themselves.

Can be employed as a standalone tool used by an

individual decision maker in one location or distributed

throughout an organization and in several organizations

along the supply chain.


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Components of DSS

The five important components of a typical DSS cannot

altogether be isolated from other information because all of

them use common database. Thus, DSS has four major

components :

Data Warehouse

Database

Model Base

User interface

Knowledge Base


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Components of DSS

External

Data

Internal

Data

Database

User Interface

Component

Model

Component

Knowledge Base

Users

D

A

T

A

W

A

R

E

H

OU

E


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Data Warehouse

A data warehouse is a relational database that is designed

for query and analysis rather than for transaction

processing. It usually contains historical data derived from

transaction data, but it can include data from other sources.

It separates analysis workload from transaction workload

and enables an organization to consolidate data from several

sources.


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Data Warehouse

A data warehouse is a repository of an organization's

electronically stored data. Data warehouses are designed

to facilitate reporting and analysis.

A data warehouse consists of a computer databases

responsible for the collection and storage of information

for a specific organization. This collection of information

is then used to manage information efficiently andanalyze the collected data.


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Data Warehouse

The data provided by the data warehouse for analysis

provides information on a specific subject, rather than

the functions of the company and is collected from

varying sources into one unit having time-variant.

Once entered into the warehouse, data should not

change. This is logical because the purpose of a

warehouse is to enable you to analyze what hasoccurred.


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Data Warehouse

Data warehouses have become more important in the

Information Age, and they are a necessity for many large

corporations, as well as some medium sized businesses.

They are much more elaborate than a mere database, andthey can find connections in data that cannot be readily

found within most databases.


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Database

Database is a pre requisite for developing any type of

information system. Database in an information system is a

collection of historical and current data and these data are

organized for easy access by a range of applications. Since DSS

may require data from various functional areas of an organization

production, marketing, finance, human resource, database must

contain data for all these functional areas. Although DSS has no

responsibility to create or update data in the database, but it

demands that data within the database must be updated so that

decisions can be taken on the actual data.


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Database

Some large organization do not provide direct access to the

central database to DSS because of two reasons.

First, organization want to protect data from accidental or a

inappropriate changes in the database. Secondly, it is a slow

and a expensive process for the DSS to search through the

large database. The process affects not only the performance of

the DSS but also all other systems using the database.

Therefore, such organizations create separate DSS database for

extracting relevant data.


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Database and Data Warehouse

The biggest difference between the two is that most

databases place an emphasis on a single application, and

this application will generally be one that is based on

transactions. In data warehouse, the data is analyzed, it

will be done within a single domain as well as multiple

domains.

Some of the separate units that may be comprised

within a database include payroll or inventory. Eachsystem will place an emphasis on one subject, and it will

not deal with other areas. In contrast, data warehouses

deal with multiple domains simultaneously.


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As it deals with multiple subject areas, the data

warehouse finds connections between them. This allows

the data warehouse to show how the company is

performing as a whole, rather than in individual areas.

Another powerful aspect of data warehouses is their

ability to support the analysis of trends. They are not

volatile, and the information stored in them doesn't

change as much as it would in a common database.


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There are two types of data that we become familiar with.

The first one is, operational data and the other is,

decision support data. The purpose, format, and

structure of these two data types are quite different. The

operational data will be placed in a relational database.

Database system is highly efficient to calibrate

operational data whereas, Data Warehouse places

decision support data is are often useful for making

effective organizational decisions by managers.


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In the relational database, tables are frequently used,

and they may be normalized. The operational data will

be calibrated in a way that allows it to deal with

transactions that are made on a daily basis. Every time

an item is sold to a customer by the company, a record

must be made of it. As can be expected, this data will be

updated on a frequent basis. To ensure the efficiency of

the system, the data must be placed in a certain number

of tables, and the tables must have fields.



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The time span deals with transactions that are atomic

and current. These transactions will deal with things

such as the inventory movement, or the purchase of an

order. Generally, operational data will deal with a short

time frame. However, decision support data tends to

deal with long time frames. Many company managers

are interested in transactions that occurred over a

certain time period. Instead of dealing with the purchase

of one customer, managers are often more interested inthe buying patterns of a group of customers. If a sale

has just been made, it will not be found in a decision

support data warehouse.



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Granularity is the third concept that separates

operational data from decision support data. Operational

data will deal with transactions that have occurred within

a certain period of time. However, the decision support

data must be broken down into different parts of

aggregation. While it may be summarized, it may also be

more current. The managers within an organization will

need information that is summarized at various degrees.



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Model Base Management

A model base is a collection of mathematical and

analytical models that can be made accessible to the DSS

users.

A model is an abstract representation that illustrate the

various components or relationship of a phenomenon.

Each DSS is based on a specific type of a model and

makes different models depending on those purposes.


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Models are preprogrammed software elements that can be

used to build computerized models. For example, a random-

number generator can be employed in the construction of a

simulation model. Models in DSS are ubiquitous and relates to mathematical

modelling which simulates real world systems and cover a

very broad range of applications. These applications arise in

all branches of Economics, Finance, Chemistry, MaterialsScience, Astronomy, Physics, Structural and Molecular

Biology, Engineering, Computer Science, and Medicine.


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The general procedure that can be used to implement a

model to solve a decision problem follows a process cycle:

(1)describe the problem,

(2)prescribe a solution,

(3) control the problem by assessing/updating the

optimal solution continuously, and

(4)Feedback and Control.


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1.Describe the Problem

As soon as a problem is detected, we think about and

understand it in order to adequately describe the problem in

writing. Develop a mathematical model or framework to re-present reality in order to devise/use an optimization

solution algorithm. The problem formulation must be

validated before it is offered a solution. A good

mathematical formulation for optimization must be bothinclusive (i.e., it includes what belongs to the problem) and

exclusive (i.e., shaved-off what does not belong to the

problem).


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2. Managerial Interpretations of the Optimal Solution

Once you recognize the algorithm and determine the

appropriate module of software to apply, utilize software to

obtain the optimal strategy. Next, the solution will bepresented to the decision-maker in the same style and

language used by the decision-maker. This means providing

managerial interpretations of the strategic solution in

layman's terms, not just handing the decision-maker acomputer printout.


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3. Control the problem by assessing/updating the

optimal solution

These activities include updating the optimal solution in

order to control the problem. In this ever-changing world, itis crucial to periodically update the optimal solution to any

given optimization problem. A model that was valid may

lose validity due to changing conditions, thus becoming an

inaccurate representation of reality and adversely affectingthe ability of the decision-maker to make good decisions.

The optimization model you create should be able to cope

with changes.


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4. The Importance of Feedback and Control

It is necessary to place heavy emphasis on the importance of

thinking about the feedback and control aspects of an MBMS. It

would be a mistake to discuss the context of the optimization-modelling process and ignore the fact that one can never

expect to find a never-changing, immutable solution to a

decision problem. The very nature of the optimal strategy's

environment is changing, and therefore feedback and control

are an important part of the model base management system.


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Describe the problem, Prescribe the solution, Updatethe solution.



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Decision Making and Problem Solving


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A problem occurs when a system does not meet its established

goals or does not work as planned. Problem solving may also deal

with identifying new opportunities. Problem solving is the most

critical activity a business organization undertakes. Problem solving

begins with decision making.The Decision making process starts with the intelligence

phase, where, potential problems and /or opportunities are

identified and defined. In the design stage, alternative solutions to

the problem are developed. In the choice stage, a course of action

is selected. In the implementation stage, action is taken to put thesolution into effect. In the monitoring stage, the implementation of

the solution is evaluated to determine if the anticipated results

were achieved and modify the process.

Decision Making and Problem Solving


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Types of Models

Most common models available in a model base are the library

of statistical functions. Such a library contains a full range of

expected statistical functions including mean, median, mode,

deviation, scatter plot and so on. The DSS software has the

ability to protect future outcomes by analyzing a series of data.

Some of the models which are based on forecasting andanalyzing data are optimizing models, forecasting models

and sensitivity analysis models.


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Optimization Models

Optimization, also called mathematical programming, helps

find the answer that yields the best result-the one that

attains the highest profit, output, or satisfaction, or the one

that achieves the lowest cost, waste, or discomfort. Often

these problems involve making the most efficient use of

resources- including money, time, machinery, staff,

inventory, and more.


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Optimization Models

Optimization models which are base on mathematical

programming solve problems of determining the optimal

allocations of limited resources required to meet a given

objective. The objective must represent the goal of the

decision-maker. For example, the resources may correspond

to people, materials, money, or land. Out of all permissible

allocations of the resources, it is desired to find the one orones that maximize or minimize some numerical quantity

such as profit or cost.


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Optimization Models

It provides guidelines for action by generating optimal

solution consistent with a series of constraints. An optimal

solution is one that optimizes returns to the organization as

a whole either in the form ofmaximization of revenues or

minimization of costs or both. Optimization models, by using

linear and non linear programming, determine optimal

resource allocation to maximize or minimize specifiedvariables, such as cost or time.


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Forecasting Models

Forecasting models are used to forecast an organization

business prospects, particularly in terms of sales. Forecasting

models use historical data and project the likely behaviour of

these data in future based on the data. If the future conditions

are projected to show some changes like entry of a new

competitor, or an emergence of a substitute product, necessary

adjustments are made in the projected data. Organization often

use forecasting software (what if analysis tools) to predict

the likely actions of competitors.


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Sensitivity Analysis

Sensitivity analysis models study the impact of distinct

changes. A discrete change is one that happens on irregular

basis, for example, entry of a new powerful competitor,

change in excise duty structure of the government, ban on

import/export of the product, etc. Sensitivity analysis, works

from known or assumed conditions and allows the users to

vary certain values to test results in order to achieve betterpredict outcomes. Sensitivity analysis generates outcomes

when probability of occurrences are known.


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Knowledge Based System

Knowledge-based system focuses on systems that use

knowledge-based techniques to support human decision-

making, learning and action. It is a computer system that is

programmed to imitate human problem-solving by means of

artificial intelligence and reference to a database of

knowledge on a particular subject. Also it based on the

methods and techniques of artificial intelligence and their

core components are the knowledge base and the inference

mechanisms.


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In knowledge-based system the computer program uses an

encoding of human knowledge to help solve problems. KBS

are computer systems, contain stored knowledge, solve

problems like humans would.And also KBS are AI programs with program structure of

new type knowledge-base (rules, facts, meta-knowledge),

inference engine (reasoning and search strategy for solution,

other services). While in Management Information System.


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It is a planned system of collecting, processing, storing and

disseminating data in the form of information needed to

carry out the functions of management or in other words the

MIS collects or encode data, analyze and convert it ininformation to be used by the top management in decision

making or information management methods tied to the

automation or support of human decision making.


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Knowledge-based system enabled programs to tackle problems

that were otherwise difficult to solve by the usual techniques of

computer science. It imitate human problem solving by means

of artificial intelligence and reference to a database of

knowledge on a particular subject or it uses data, facts, and

reasons about them in order to do something. Also Knowledge-

based system improve the company's competitive advantage

and organizational performance because it solve problems like

humans would and it has inference engine or reasoning and

search strategy for solution and it has intelligent information

processing systems.


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User Interface

User interface in DSS permits easy interaction between the users

and the database and model base. DSS software system manages

the creation, storage, and retrieval of functions from model base

and integrates them with data in the database. DSS software

provides GUI, easy to use flexible user interface that supports the

interaction between the user and DSS. Since DSS are meant for

relatively higher level managers who are not experts in computer

handling, the DSS interface must be easy so that relevant

information is extracted without much difficulty.


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Group Decisions Support Systems

In the early phase of development of DSS, focus was

primarily on supporting individual decision making.

However, the DSS software developers realized that the

most of the time, the important decisions are made by a

group consisting many decision makers. Therefore, they

focused their attention to develop systems that would help

group decision making. Such systems were named group

decisions support systems.


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Concept of GDSS

A GDSS system can be defined as a CBIS that supports the

group of decision makers engaged in a common task and

that provides interface to a shared environment.

It is a modern way of conducting group discussions by

applying new techniques like electronic meetings, brain

storming, criticism free idea generation etc.


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What is Brain Storming?

Brainstorming is the name given to a situation when a group

of people meet to generate new ideas around a specific area

of interest. Using rules which remove inhibitions, people are

able to think more freely and move into new areas of

thought and so create numerous new ideas and solutions. All

the ideas are noted down and are not criticized. Only when

the brainstorming session is over are the ideas evaluated.


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What is electronic meeting?

An electronic meeting process guides through the various

meeting stages using meeting productivity software. This

software is designed to assist with the preparation and

follow-up stages of your meeting as well as the meeting

itself.


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Features of GDSS

There are number of features of a GDSS because of which it

can be differentiated from DSS. These features are as

follows:

1.There is high level of interaction among decision makers

who work collectively on a problem. This interaction is

generally through computer system. The interaction is open

an takes place in collaborative meeting atmosphere in whichattendees from various organizational levels feel freedom to

contribute positively to solve the problem.


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Features of GDSS

2. Emphasis is put on criticism free idea generation

creating an atmosphere where an idea will be evaluated

on its merits rather than on the basis of the source of

idea. Idea organization and evaluation require keeping

the focus on the meeting objectives, finding efficient

ways to organize ideas that can be generated, and

evaluating those ideas keeping in the mind theappropriate time constraints.


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Features of GDSS

3. Priorities are set and decisions are made which required

finding ways to encompass the thinking of all the

members in making these decisions.

4. Each member of the decision making group has

access to relevant internal and external information

which allows the members to emphasize there own

views, appreciate the views of others, and settle theirdifferences in order to arrive at an acceptable decision

within a given time frame.


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Features of GDSS

5. While writing an agenda and distributing it to the team,

GDSS software take this process one step further and

allow to store meeting goals, agenda items and relevant

files in an electronic workspace. This workspace can

then be sent to participants via e-mail to review prior to

the meeting. During the meeting, one can access the

meeting workspace to go through agenda items.


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Components of GDSS

Like a DSS a GDSS has several components which can be

seen from the model of group decision support systems The

principal components of GDSS consists of the following

column:

Decision makers

Database

Model base

Groupware


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Decision Makers

A GDSS has a number of decision makers who work collectively on

a specific problem with the objective that the problem will be

solved by the collective wisdom of all the decision makers. In a

group rather than on the basis of their contributions takenindividually, each of these decision makers has access to the

database including model base from which each of them extract

relevant data. Each of them can use report writing software for

report preparation on individual basis as well as can use groupware

to understand the ideas of others on the problem concerned.


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Database and Model Base

Like a DSS a GDSS also uses database and model base to

extract relevant data and models for analysing these data in

order to arrive at decision. While model base for both DSS

and GDSS may remain the same as both use almost similar

models in decision making, there may be difference in the

organization of database in some cases. Where decision

makers are placed at different locations, it is preferable tohave distributed databases to facilitate easy access.


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Groupware

Software that is used in GDSS is generally called as a

groupware. In 1988, Lotus Development Corporation

developed a Groupware known as Notes, keeping in view the

role of communication in GDSS. Notes included a number of

features such as electronic mail, fax, voice messaging,

internet access, bulletin board systems, personal

calendaring, group calendaring, video conferencing, taskmanagement etc.


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Groupware

It incorporates brain storming software which supports the

definition phase of a problem by identifying its components.

Members of the decision making group generate their own

ideas, exchange their ideas with others, and evaluate those

ideas. The output of these exercise is a structured report

containing the pros and cons of various ideas and how they

are relevant for problem solution.


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Groupware

Decision makers use a variety of alternatives and rate them

on the basis of certain criteria or rank them on the basis of

these criteria. Alternative rating and ranking software

undertakes the evaluation work and combines various

alternatives in the form of a table or graph. The software

supports design phase of decision making.


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Groupware

Consensus building is necessary in group decision making so

that deadlock is broken and members arrive at an agreed

decision. Consensus building software informs the decision

makers about the degree of uniformity in their alternative

solutions. When there is no consensus the decision makers

can engage in further discussion. The software points out

the issues on which decision makers disagree and supports

them to have a common decision.

unit - 3 decision support systems

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