Download 10.1 Classification of Support systems

LECTURE 09:
SUPPORT SYSTEMS
Seeing the benefits of MIS for middle level managers, Computerised systems have been
devised for other employees in the organization to help them complete their work efficiently
and effectively.
10.1 Classification of Support systems
1. Office automation systems
2. Decision support systems
10.1.1 Office Automation Systems
Office automation system includes formal and informal electronic systems primarily
concerned with the communication of information to and from persons both inside and
outside the firm. It supports data workers in an organization.
For Instance

Word processing

Desktop publishing

Imaging & Web publishing

Electronic calendars – manager’s appt. calendars

Email

Audio & video conferencing – establishing communication between geographically
dispersed persons.
10.1.2 Decision Support Systems
Before moving forward with the concept of decision support system, we would take a look at
the definition of MIS
“An integrated man-machine system for providing information to support the operations,
management and decision making functions in an organization.”
(Prof. Gordon Davis University of Minnesota)
Four Criteria for designing models and systems to support management decisions making
were laid down by J.D.C. Little. These were
1. Robustness
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2. Ease of Control
3. Simplicity
4. Completeness of relevant detail
Decision Support Systems was defined by Bill Inmon, father of data warehouse, as “a system
used to support managerial decisions. Usually DSS involves the analysis of many units of
data in a heuristic fashion. As a rule, DSS processing does not involve the update of data”.
Heuristic simply means a particular technique of directing one’s attention in learning,
discovery or problem solving. It assists in non-routine decision making process due to
powerful analytical abilities.
For Instance

For any scenario all the related factors with their ranges of variability are entered into
DSS, which helps guide managers for any new scenario that emerges.

DSS can stimulate innovation in decision making by helping managers to existing
decision making procedures.
An example of Decision Support System
An outfit store maintains ready made garments and stitched clothes for various classes of
society. Due to fluctuating changes in fashion trends, pre-seasonal planning becomes critical.

A Planning and forecasting software can be used by management to

Measure customer reactions to re-pricing

When to initiate clearance sales for old stock

Deciding about discount percentages

When to order new stock for the season
10.2 Functionalities of MIS and DSS
Sr. No.
MIS
DSS
1
Helps in non routine decision making.
Helps in non routine decision making.
2
Fixed and regular reports are
Users are not linked with the structured
generated from data kept in
Information flows.
TPS.
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Report formats are predefined.
3
Greater emphasis on models, display
Graphics & ad hoc queries.
DSS is a small part of users’ actions.
4
User is part of the system
5
Directly used by middle level managers Directly used by middle level managers
10.3 Types of DSS
DSS, may either be
1. Model Driven DSS
2. Data Driven DSS
10.3.1 Model Driven DSS
Model driven DSS uses following techniques

What-If analysis
Attempt to check the impact of a change in the assumptions (input data) on the
Proposed solution e.g. what will happen to the market share if the advertising budget
increases by 5 % or 10%?

Goal Seek Analysis
Attempt to find the value of the inputs necessary to achieve a desired level of output.
It uses “backward” solution approach e.g. a DSS solution yielded a profit of $2M.
What will be the necessary sales volume to generate a profit of $2.2M?
These are primarily stand alone systems isolated from major organizational information
systems (finance, manufacturing, HR, etc). They are developed by end users and are not
reliant on central information systems control.
These systems combine

Use of a strong model, and

Good user interface to maximise model utility
They are not usually data intensive, hat is very large data bases are usually not need for
model-driven DSS.
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They use data and parameters usually provided by decision makers to aid in analyzing a
situation.
10.3.2 Data Driven DSS
As opposed to model driven DSS, these systems use large pools of data found in major
organizational systems. They help to extract information from the large quantities of data
stored. These systems rely on Data Warehouses created from Transaction Processing
systems.
They use following techniques for data analysis
1. Online analytical processing, and
2. Data mining
10.4 Components of DSS
There are two major components
1. DSS data base – is a collection of current and historical data from internal external
sources. It can be a massive data warehouse.
2. Decision Support Software system – is the set of software tools used for data analysis.
For instance

Online analytical processing (OLAP) tools

Data mining tools

Models
10.5 Data Warehouse

A data warehouse is a logical collection of information.

It is gathered from many different operational databases used to create business
intelligence that supports business analysis activities and decision-making tasks.

It is primarily, a record of an enterprise's past transactional and operational
information, stored in a database designed to favour efficient data analysis and
reporting.

The term data warehouse generally refers to the combination of many different
databases across an entire enterprise.

Data warehouses contain a wide variety of data that present a coherent picture of
business conditions at a single point in time.
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
Data warehouses are generally batch updated at the end of the day, week or some
period. Its contents are typically historical and static and may also contain numerous
summaries.
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