DSS

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ZARA

luonut 기 장

Submenu 2 Marilyn daily

luonut michael buoniconti

ORAL COMMUNICATION

luonut Linda Gonzalez

Organizing and Representing Data: Visual Representations

luonut Katie Morrison

Tools

modeling

statistical modeling

business rule based decisions

tending

what-if analysis

predictive analysts

decision trees

multivariate regression

logistics regression

forecasting

scenario manager

uncertainty analysis

monto-carlo simulation

sensitivity analysis

regression

asset modelling

failure performance

asset degradation

design views/configuration

financial models

renewals planning

degradation modeling

financial modeling

risk modelling

extreme value theory (EVT)

historical simulation (HS)

value at risk (VaR)

market risk

whole life costing

ROI

NPV

IRR

Tariff optimisation

Capital budgeting: Investment decision tool

business intelligence

cubes

special-purpose data management system

relational data warehouse

summarise financial data by product

decision services

linear assits

dynamic segmentation

GIS exchange

points assists

capabilities

interfaces

asset management system

Cloud-based Digital Asset Management systems

emerge to complement on-premise systems.

Production asset management systems

visual-effects shots

animation

3D feature film

video game

Library asset management systems

video or photo archiving.

Brand asset management systems

marketing collateral or fonts

logos

product imagery

business rules

apply rules to models

modify rule

define rule

user management

combine expert knowledge with info

Specific instruction

context specific

typically simple

rule-based knowledge

Formulaic

Popular understanding

Knowledge from the media

Primary source knowledge

basic technical competence

Interactional expertise

Knowledge gained from learning the language of specialist groups

Contributory expertise

ability to contribute new knowledge or to teach

data management

data exchange capability

data mining

neural networks

fuzzy query and analysis

data visualization

cluster analysis

case-based reasoning

Benefits

Supports learning

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

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

First managers themselves learn new concepts

High satisfaction among decision makers

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

In DSS computers and latest technology aids the decision making process

Helps in reducing cost

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

Provides competitive advantage

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

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.

Helps in saving time

DSS provides timely information

used for decision making and results in enhanced employee productivity.

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

Features

Quick response

Support varieties of decision trees

Support varieties of decision processes

Standalone and web-based integration

Data access

Modeling and analysis

Ease of development by end user

Human control of the process

Interactive and efficiency

Interactive and ease of use

Adaptable and flexible

Support Intelligence, Design, Choice

Interdependence and sequence of decisions

Support individuals and groups

Solve semi-structured and unstructured problems

DDSS supports the personal decision making styles of individual managers

DSS is user initiated and user controlled

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

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

DSS improves the effectiveness of the decision rather than its efficiency

Phases

Monitoring

Monitoring the results of implementation for feedback

Imolementation

Issues

Resistance to Change

Manage Change

Making the Decision Happen

Choice

Blind searches

shooting in the dark

Heuristics

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

Algorithms

step-by-step search

Analaytical Technic

use mathematical formulas

Design

Select Choice Models

Normative

Descriptive

What-if Scenarios

Simulation

Narrative

Select a principle of choice

Test for Feasibility

Develop Alternative Course of work

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

alternative with the highest ratio of goal attainment to cost

alternative that realizes the highest level goal

Intelligence

Problems

Information overload

Data estimation is often subjective

Data may not be accurate or precise enough

Obtaining data may be expensive

Data are not available

steps

Problem Ownership

Assignment of authority to solve the problem

Problem Decomposition

Complex problems can be divided into sub-problems

Problem Classification

Place the problem in a definable category

Problem Identification

identification of organizational goals and objectives

applications

Production and Operations Managment

Inventory Planning

Resource management

Manufacturing Industry

Master Scheduling

Planning For Demand

Human resource management

Sales process

Marketing

Agricultural production

Business and management.

Clinical decision support system

medical diagnosis

future trends

The DSS Software Market Continues To Develop And Mature

Organizational Impacts Of DSS

Enhancement Of DSS Applications With Values And Ethics

Research On Interactions Between Individuals And Groups

Single user decision support systems

The World Wide Web and group/organizational/global DSS

Knowledge-based design support systems (intelligent DSS)

Classification

Modern Classifications

Knowledge-driven DSS

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

Document-driven DSS

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

Videos

Sounds

Images

Hypertext documents

Scanned documents

Communications-driven DSS

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

interactive video

electronic mail

document sharing

audio conferencing

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.

Data mining

OLAP

Holsapple and whinston`s Classification

Compound DSS

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

Rule-Oriented DSS

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

Solver-Oriented DSS

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

Spreadsheet-oriented DSS

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

Database-Oriented DSS

It contains organised and highly structured data

Text-Oriented DSS

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

alter's output classification

degree of action implication of system of system outputs

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

credit scoring

insurance renewal rate calculation

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

resource allocation

material usage optimization

scheduling systems

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

equipment and production simulations

risk analysis models

generating estimates of income statements and balance sheets

estimating profitability of a new product

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

product planning and analysis

sales forecasting based on a marketing database

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

budget analysis

data warehouse applications

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

monitoring systems

Components

DSS Software System

Backward Analysis Sensitivity Models

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

Forecasting Models

Market research methods

Time series analysis

Regression models

Optimization Analysis Models

Used for making decisions related to optimum utilization of resources.

Sensitivity Analysis Models

provide answers to what-if situations

Statistical Models

Deviation Function

Median Function

Knowledge Base

Provide Expertise to solve semi-structured and unstructured problems

Datamining

Model Base

Mathematical

Economics

Analog

Corelation

Regression

Trend Analysis

iconic

Physical Representations

User Interface

Interaction Elements

Adjustment Handle

Selection

Cursor

Insertion Pointer

Pointer

Structural Elements

Tabs

Controls

Icons

Menus

Windows

User

Intermediaries

Staff Assistant

Expert Tool User

Business Analyst

Staff Specialist

Database

Database Management System

Database Access Language

SQL

Procedures

To generate the reports of data retrieved from database.

To make backup copies of database

To log on to the DBMS.

Procedure to install the new DBMS.

Data

Metadata

Operational Data

Hardware

Output Device

Printer

Screen

Input Device

Mouse

Keyboard

Software

Application programs

Operating system

DBMS software itself

Query Facility

Handles Unstructured Data

Handles Structured Data

Data Directory

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

Application

Web Database servers

Multimedia Database

Web Browser data access

Data Mining

Datawarehouse

Contains Data from Transaction systems

DSS Database

External data mined form the Internet

Data generated by different Application

Internal data from Organization

Taxonomy

Suggestion DSS based on logic models

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

Applicant applies for loans

suggest to thedecision maker the best actionA prescriptive model

Optimization model-based DSS

incorporate uncertaintyAssign sales force to territoryProvide the best assignment schedule

estimate the effects of different decision alternativeBased on optimization models

Representational model-based DSS

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

inventory decisions

Accounting and financial model-based DSS

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

Make pricing decisions

Calculate production cost

DSS

Decision Support Systems (DSS) are evolving with the integration of values and ethics, contributing to more responsible and effective decision-making processes. The development of the DSS software market reflects ongoing maturation and sophistication in technology.