Page 16 - DCAP208_Management Support Systems

Unit 1: Support Systems

being made about a medical diagnosis, or even in terms which we would encounter each and Notes
every day, decisions about money or our own wealth simply cannot be wrong!
Decision support even extends into the justice system and is therefore another area where
mistakes simply cannot be made. (Imagine a judgment being made on an individual, assisted by
DSS. If the judgment goes against that individual, the evidence for such a conviction has to be
beyond reproach! Fortunately, it is still only a Decision Support System, not a Decision-Making
System as yet.)

We should be aware though that, in situations where there are pressures to get something
delivered on time, where there are higher stakes, or numerous ambiguities, experts use intuitive
decision making rather than structured approaches.
History of Decision Support Systems: It is generally considered that the concept of DSS became
an area of research in the mid-1970s, before it gained intensity in the 1980s. Around 1990, data
warehousing and OLAP (Online Analytical Processing) began widening the realm of DSS.

1.4.1 Types of Decision Support Systems

Different types of Decision Support Systems are as follows:
Model-driven DSS puts emphasis on manipulation of a statistical, financial, or simulation
model. This type of DSS uses data and parameters provided by users to assist decision
makers in analyzing a situation; (they are not necessarily data intensive.) Parameters are
provided by users for the analysis of a situation.

Example: Dicodess is an example of an open source model-driven DSS generator. It runs
on the Sun Microsystems Java-based Jini-2 engine.
Communication-driven DSS supports more collaboration on a shared task.

Example: Include integrated tools like Microsoft’s NetMeeting or Groove.

Data-driven DSS emphasizes manipulation of a chronological series of corporate internal
data or occasionally, external data.
Document-driven DSS manages and manipulates unstructured information in from a
variety of electronic formats.
Knowledge-driven DSS provides specialized problem solving expertise stored as facts,
rules, procedures, or in similar structures.

Key DSS characteristics and capabilities are:
Supports decision makers in semi-structured and unstructured problems.
Supports managers at all levels.

Supports individuals and groups.
Supports interdependent or sequential decisions.
Support intelligence, design, choice and implementation.
Supports a variety of decision processes and styles.

Should be adaptable and flexible.
Should be interactive and easy to use.

LOVELY PROFESSIONAL UNIVERSITY 9