DSS

Technical challenges

Improve and evolve every 18-24 months

Dynamic

High-speed wireless remote access

Automatic data collection

Processing

Organizational challenges

Coping with a lack of human resources

Deciding priorities

Maintaining perspective on the needed organization changes

Uncertain consequences

Social challenges

workplaces

information pushed to employees for action taking

Greater situational awareness while performing tasks

File drawer systems

provide access to data items

decisionATM Machine

Data analysis systems

access to dataAllows data manipulation capabilities

Airline Reservation

Analysis information systems

The information from one file, table, can becombined with information from other filesto answer a specific query.

Accounting and financial model-based DSS

Use internal accounting dataProvide accounting modeling capabilitiesCan not handle uncertaintyUse Bill of Material

Calculate production cost

Make pricing decisions

Representational model-based DSS

solve decision problemusing forecastsCan be used to augment the capabilities ofAccounting models

inventory decisions

Optimization model-based DSS

estimate the effects of different decision alternativeBased on optimization models

incorporate uncertaintyAssign sales force to territoryProvide the best assignment schedule

Suggestion DSS based on logic models

suggest to thedecision maker the best actionA prescriptive model

suggest to the decision maker the best action May incorporate an Expert SystemUse the system to recommend a decision

Applicant applies for loans

Database

DSS Database

Internal data from Organization

Data generated by different Application

External data mined form the Internet

Application

Datawarehouse

Contains Data from Transaction systems

Data Mining

Web Browser data access

Multimedia Database

Web Database servers

Data Directory

An inventory that specifies the source, of the data elements that are stored in a database.

Query Facility

Handles Structured Data

Handles Unstructured Data

Database Management System

Software

DBMS software itself

Operating system

Application programs

Hardware

Input Device

Keyboard

Mouse

Output Device

Screen

Printer

Data

Operational Data

Metadata

Procedures

Procedure to install the new DBMS.

To log on to the DBMS.

To make backup copies of database

To generate the reports of data retrieved from database.

Database Access Language

SQL

User

Staff Specialist

Intermediaries

Business Analyst

Expert Tool User

Staff Assistant

User Interface

Structural Elements

Windows

Menus

Icons

Controls

Tabs

Interaction Elements

Cursor

Pointer

Insertion Pointer

Selection

Adjustment Handle

Model Base

iconic

Physical Representations

Analog

Trend Analysis

Regression

Corelation

Mathematical

Economics

Knowledge Base

Provide Expertise to solve semi-structured and unstructured problems

Datamining

DSS Software System

Statistical Models

Median Function

Deviation Function

Sensitivity Analysis Models

provide answers to what-if situations

Optimization Analysis Models

Used for making decisions related to optimum utilization of resources.

Forecasting Models

Regression models

Time series analysis

Market research methods

Backward Analysis Sensitivity Models

Sets a target value for a variable and then repeatedly changes other variables until the target value is achieved

alter's output classification

degree of action implication of system of system outputs

File drawer systems

This type of DSS primarily provides access to data stores/data related items.

monitoring systems

Data analysis systems

This type of DSS supports the manipulation of data through the use of specific or generic computerized settings or tools.

data warehouse applications

budget analysis

Analysis information systems

This type of DSS provides access to sets of decision oriented databases and simple small models

sales forecasting based on a marketing database

product planning and analysis

Accounting and financial model-based DSS

estimating profitability of a new product

generating estimates of income statements and balance sheets

Representational model-based DSS

This type of DSS can also perform 'what if analysis' and calculate the outcomes of different decision paths, based on simulated models

risk analysis models

equipment and production simulations

Optimization model-based DSS

This kind of DSS provides solutions through the use of optimization models which have mathematical solutions

scheduling systems

material usage optimization

resource allocation

Suggestion DSS based on logic models

This kind of DSS works when the decision to be taken is based on well-structured tasks

insurance renewal rate calculation

credit scoring

Holsapple and whinston`s Classification

Text-Oriented DSS

It contain textually represented information that could have a bearing of information

Database-Oriented DSS

It contains organised and highly structured data

Spreadsheet-oriented DSS

Modify procedural knowledge and also instruct the system to execute self-contained instructions

Solver-Oriented DSS

is an algorithm or procedural written for performing certain calculations and particular program type

Rule-Oriented DSS

It follows certain procedures adopted as rules.It follows certain procedures adopted as rules.

Compound DSS

It is built by using two or more of the five structures

Modern Classifications

Data Driven DSS

is a DSS that gives access to time-series internal data. Data ware houses that have tools that provide facility to manipulate such data are examples of advances systems.

OLAP

Data mining

Communications-driven DSS

is a DSS that uses network and communications technologies to support decision-relevant collaboration and communication

audio conferencing

document sharing

electronic mail

interactive video

Document-driven DSS

is a DSS that uses computer storage and processing to provide document retrieval and analysis.

Scanned documents

Hypertext documents

Images

Sounds

Videos

Knowledge-driven DSS

is a DSS that collects and stores 'expertise' so that it can be used for decision-making when required.

Knowledge-based design support systems (intelligent DSS)

The World Wide Web and group/organizational/global DSS

Single user decision support systems

Research On Interactions Between Individuals And Groups

Enhancement Of DSS Applications With Values And Ethics

Organizational Impacts Of DSS

The DSS Software Market Continues To Develop And Mature

Clinical decision support system

medical diagnosis

Business and management.

Agricultural production

Marketing

Sales process

Human resource management

Production and Operations Managment

Planning For Demand

Master Scheduling

Manufacturing Industry

Resource management

Inventory Planning

Intelligence

steps

Problem Identification

identification of organizational goals and objectives

Problem Classification

Place the problem in a definable category

Problem Decomposition

Complex problems can be divided into sub-problems

Problem Ownership

Assignment of authority to solve the
problem

Problems

Data are not available

Obtaining data may be expensive

Data may not be accurate or precise enough

Data estimation is often subjective

Information overload

Design

Develop Alternative Course of work

alternative that realizes the highest level goal

alternative with the highest ratio of goal attainment to cost

alternative with the lowest cost that will meet an acceptable level of goals

Test for Feasibility

Select a principle of choice

Select Choice Models

Descriptive

Narrative

Simulation

What-if Scenarios

Normative

Choice

Analaytical Technic

use mathematical formulas

Algorithms

step-by-step search

Heuristics

Judgmental knowledge of an application area that constitutes the rules of good judgment in the field

Blind searches

shooting in the dark

Imolementation

Making the Decision Happen

Issues

Manage Change

Resistance to Change

Monitoring

Monitoring the results of implementation for feedback

DSS improves the effectiveness of the decision rather than its efficiency

DSS combines the use of models and analytical techniques with conventional data access and retrieval functions

DSS has enough flexibility to accommodate changes in the environment, the approach and the needs of the users

DSS is user initiated and user controlled

DDSS supports the personal decision making styles of individual managers

Solve semi-structured and unstructured problems

Support individuals and groups

Interdependence and sequence of decisions

Support Intelligence, Design, Choice

Adaptable and flexible

Interactive and ease of use

Interactive and efficiency

Human control of the process

Ease of development by end user

Modeling and analysis

Data access

Standalone and web-based integration

Support varieties of decision processes

Support varieties of decision trees

Quick response

Helps in saving time

Research has demonstrated that decision support systems help to reduce decision cycle time for an organization

DSS provides timely information

used for decision making and results in enhanced employee productivity.

Improves efficiency

DSS is efficient decision making, resulting in better decisions

because use of DSS results in quick transfer of information, better data analyses, thus resulting in efficient decisions.

Provides competitive advantage

Use of decision support system in an organization provides a competitive advantage over other organizations which do not use DSS.

Helps in reducing cost

Research and case studies reveal that use of DSS in an organization helps in making quicker decisions and reduce cost

High satisfaction among decision makers

In DSS computers and latest technology aids the decision making process

They gain a confidence and satisfaction that they are good decision makers.

Supports learning

The use of DSS in an organization results in two type of learning

First managers themselves learn new concepts

Secondly, there is better factual understanding of business as well as the decision making environment.

capabilities

data management

data mining

case-based reasoning

cluster analysis

data visualization

fuzzy query and analysis

neural networks

data exchange capability

user management

combine expert knowledge with info

Contributory expertise

ability to contribute new knowledge or to teach

Interactional expertise

Knowledge gained from learning the language of specialist
groups

Primary source knowledge

basic technical competence

Popular understanding

Knowledge from the media

Specific instruction

Formulaic

rule-based knowledge

typically simple

context specific

business rules

define rule

modify rule

apply rules to models

interfaces

asset management system

Brand asset management systems

product imagery

logos

marketing collateral or fonts

Library asset management systems

video or photo archiving.

Production asset management systems

video game

3D feature film

animation

visual-effects shots

Cloud-based Digital Asset Management systems

emerge to complement on-premise systems.

decision services

points assists

linear assits

dynamic segmentation

GIS exchange

modeling

business intelligence

cubes

OLAP

summarise financial data by product

relational data warehouse

special-purpose data management system

financial modeling

IRR

Capital budgeting: Investment decision tool

Tariff optimisation

NPV

ROI

whole life costing

risk modelling

market risk

value at risk (VaR)

historical simulation (HS)

extreme value theory (EVT)

asset modelling

degradation modeling

renewals planning

financial models

design views/configuration

asset degradation

failure performance

statistical modeling

regression

sensitivity analysis

uncertainty analysis

monto-carlo simulation

scenario manager

forecasting

predictive analysts

logistics regression

multivariate regression

decision trees

cluster analysis

neural networks

what-if analysis

tending

business rule based decisions

sensitivity analysis