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Lecture-3:

Decision Support SystemsConcepts, Methodologies, and

Technologies: An Overview

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Prof. Pawan Kumar MBA-IV SEM (SEC-B)2-2

DSS Characteristics and Capabilities

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DSS Characteristics and Capabilities

Business analytics implies the use of modelsand data to improve an organization'sperformance and/or competitive posture

Web analytics implies using business analyticson real-time Web information to assist indecision making; often related to e-Commerce

Predictive analytics describes the businessanalytics method of forecasting problems andopportunities rather than simply reportingthem as they occur

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Classification of DSS

Communication-driven DSS

Model-driven DSS

Data-driven DSS

Document-driven DSs

Knowledge-driven DSS, datamining and management ESapplications

(AI SIGDSS-Association for Information Systems SpecialInterest Group On Decision Support Systems)

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Communication-driven andGroup DSS (GSS)

Most communications-driven DSSs aretargeted at internal teams, including partners.

Its purpose are to help conduct a meeting, or

for users to collaborate. The most common technology used to deploy

the DSS is a web or client server.

Examples: chats and instant messagingsoftware, online collaboration and net-meeting systems.

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Model-driven DSS Data-driven DSS

User interacts primarily with a(mathematical) model and its results

User interacts primarily with thedata(DW)

Helps to solve well-defined andstructured problem (what-if-analysis)

Helps to solve mainly unstructuredproblems

Contains in general various and complexmodels

Contains in general simple models

Large amounts of data are not necessary Large amounts of data are crucial

Helps to understand the impact ofdecisions on organizations

Helps to prepare decisions by showingdevelopments in the past and byidentifying relations or patterns

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Document-driven DSS

The main objective of DD DSS is to provide support fordecision making using documents in various forms: Oral,written, and multimedia

DD DSS rely on knowledge coding,analysis,search and

retrieval for decision support.

The Text Based DSS and most KMS fall into this category.

The purpose of such a DSS is to search web pages andfind documents on a specific set of keywords or search

terms.

The usual technology used to set up such DSSs are via theweb or a client/server system. Examples: Most KMS

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Knowledge-driven DSS:

Involves application of knowledge technologies toaddress specific decision support needs.

All AI based DSS fall into this category.ANN and ESare included here.

It is also called Intelligent DSS or Knowledge basedDSS.

These DSS are utilized in the creation of automateddecision making systems (ADSS).

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DSS Classifications

Holsapple and Whinston's Classification1. The text-oriented DSS are the same as document-driven

DSS.

2. The database-oriented DSS are the data driven DSS.

3. The spreadsheet-oriented DSS is another form ofmodel-driven DSS.

4. The solver-oriented DSS maps directly into the model-driven DSS.

5. The rule-oriented DSS (include most knowledge-drivenDSS, data mining, management, and ES applications)

6. The compound DSS integrates two or more of thosecited above.

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DSS Classifications

Alter's Output ClassificationOrientation Category Type of Operation

Data File drawer systems Access data items

Data analysis systems Ad hoc analysis of data files

Data or

models

Analysis information

systems

Ad hoc analysis involving

multiple databases and small

models

Models Accounting models Standard calculations thatestimate future results on the

basis of accounting definitions

Optimization models Calculating an optimal solution to

a combinatorial problem

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Institutional and ad-hoc DSS

Institutional :It deals with decisions of arecurring nature. Ex. Portfolio ManagementSystem(PMS).It is used repeatedly to solve

identical or similar problems or recurringproblem.

Ad hoc DSS: It deals with specific

problems that are usually neitheranticipated nor recurring.It involvesstrategic planning issues.

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DSS Configurations

Many configurations exist; based on

management-decision situation

specific technologies used for support

DSS have three basic components

1. Data

2. Model

3. User interface

4. (+ optional) Knowledge

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DSS Configurations

Each component

has severalvariations; are

typically deployedonline

Managed by acommercial of

custom software Typical types:

Model-oriented DSS

Data-oriented DSS

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A Web-Based DSS Architecture

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DSS Components and Web Impacts

Impacts of Web to DSS

Data management via Web servers

Easy access to variety of models, tools

Consistent user interface (browsers)

Deployment to PDAs, cell phones, etc.

DSS impact on Web

Intelligent e-Business/e-Commerce

Better management of Web resources andsecurity,

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Components of DSS

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Components of DSS

Data Management Subsystem Includes the database that contains the data

Database management system (DBMS)

Can be connected to a data warehouse

Model Management Subsystem

Model base management system (MBMS)

User Interface Subsystem

Knowledgebase Management Subsystem

Organizational knowledge base

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DSS Components1-Data Management Subsystem

DSS database

DBMS

Data directory Query facility

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Database Management SubsystemKey Data Issues

Data quality

Garbage in/garbage out" (GIGO)

Data integration

Creating a single version of the truth

Scalability

Data Security

Timeliness

Completeness,

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10 Key Ingredients of Data(Information) Quality Management

1. Data quality is a business problem, not onlya systems problem

2. Focus on information about customers and

suppliers, not just data3. Focus on all components of data: definition,

content, and presentation

4. Implement data/information qualitymanagement processes, not just software tohandle them

5. Measure data accuracy as well as validity

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10 Key Ingredients of Data(Information) Quality Management

6. Measure real costs (not just the percentage)of poor quality data/information

7. Emphasize process improvement/preventive

maintenance, not just data cleansing8. Improve processes (and hence data quality)

at the source

9. Educate managers about the impacts ofpoor data quality and how to improve it

10.Actively transform the culture to one thatvalues data quality

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DSS Components2-Model Management Subsystem

Model base

MBMS

Modeling

language

Model directory

Model execution,integration, and

commandprocessor

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DSS ComponentsModel Management Subsystem

Model base (= database ?)

Model Types

Strategic models

Tactical models

Operational models

Analytic models

Model building blocks

Modeling tools

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DSS ComponentsModel Management Subsystem

The four (4) functions

1. Model creation, using programminglanguages, DSS tools and/or subroutines,

and other building blocks2. Generation of new routines and reports

3. Model updating and changing

4. Model data manipulation Model directory

Model execution, integration and command

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DSS Components3-User Interface (Dialog) Subsystem

Interface

Application interface

User Interface

Graphical User Interface(GUI)

DSS User Interface

Portal

Graphical icons

Dashboard

Color coding

Interfacing with PDAs,cell phones, etc.

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DSS ComponentsKnowledgebase Management System

Incorporation of intelligence and expertise

Knowledge components:

Expert systems,

Knowledge management systems, Neural networks,

Intelligent agents,

Fuzzy logic,

Case-based reasoning systems, and so on

Often used to better manage the other DSScomponents

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DSS ComponentsFuture/current DSS Developments

Hardware enhancements

Smaller, faster, cheaper,

Software/hardware advancements

data warehousing, data mining, OLAP,Web technologies, integration anddissemination technologies (XML, Web

services, SOA, grid computing, cloudcomputing, )

Integration of AI -> smart systems

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DSS User

One faced with a decision that an MSS isdesigned to support

Manager, decision maker, problem solver,

The users differ greatly from each other Different organizational positions they occupy;

cognitive preferences/abilities; the ways ofarriving at a decision (i.e., decision styles)

User = Individual versus Group Managers versus Staff Specialists [staff

assistants, expert tool users, business(system) analysts, facilitators (in a GSS)]

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DSS Hardware

Typically, MSS run on standard hardware

Can be composed of mainframe computerswith legacy DBMS, workstations, personal

computers, or client/server systems Nowadays, usually implemented as a

distributed/integrated, loosely-coupledWeb-based systems through cloud computing

Can be acquired from

A single vendor

Many vendors (best-of-breed)

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GDSS

(

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GDSS(Group Decision SupportSystem)

It is an interactive computer basedsystem that facilitates the solution ofsemi structured and unstructured

problems by a group of decisionmakers.

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Characteristics of GDSS

To support process of decision makers.

To address variety of group levelorganizational decisions.

Encourages generation ofideas,resolution of conflicts andfreedom of expression.

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Collaboration

What is it?

making joint effort towardachieving an agreed upon goal.

Meeting is a common form ofcollaboration

Why collaborate?

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Why Collaborate?

Review

Share Work

Share the Vision

SocializeBuild Consensus

Solve Problems

Make Decisions

Synergy

Share Information

Build Trust

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Collaboration is Difficult

Waiting to speak

Domination

Fear of Speaking

MisunderstandingInattention

Lack of Focus

Inadequate Criteria

Premature DecisionsMissing Information

Distractions

Wrong People

Groupthink

Poor Grasp of Problem

Ignored AlternativesLack of Consensus

Poor Planning

Hidden Agendas

ConflictInadequate Resources

Poorly Defined Goals

Ineffective

Collaboration

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Collaboration is Essential

No one has all the Experience

Knowledge

Resources

Insight, and

Inspiration

to do the job alone

Bottom line:Collaboration is difficult, expensive, and yetessential for todays organizations

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How Do People Collaborate?

Level 1 Collected Work :

Uncoordinated Individual Efforts

Level 2 Coordinated Work:

Coordinated Individual Efforts

Level 3 Concerted Work:

Concerted Team Effort

Sprinters

Relay

3 Levels of Collaboration Capability

Crew

High

Low

Degree of

Collaborative

Effort

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Joint activity Equal or near equal status

Outcome depends on participantsknowledge, etc.

Outcome depends on group composition

Outcome depends on decision-makingprocess

Disagreement settled by rank or negotiation

Meetings (a form of collaboration)

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Dozens of people attends

Everyone

talks at oncehears everything

understands

remembers

The impossible dream?

The Ideal Meeting

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Traditional Meetings

Only ONE person can speak at a time

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GSS Meetings

By using the computer everyone canSPEAK and be understood simultaneously

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Communication Support

Vital

Needed for collaboration

Modern information technologiesprovide inexpensive, fast, capable,reliable means of supportingcommunication

Internet / Web

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Synchronous Products(Same-Time) Asynchronous Products(Different-Time)

Groupware Tools

Groupware Tools: Synchronous

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IM Videoconferencing, multimedia conferencing

Audio conferencing

Instant Video

Brainstorming

Screen sharing

Groupware Tools: SynchronousProducts

Groupware Tools: Asynchronous

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Groupware Tools: AsynchronousProducts

E-Mail

SMS

Chat session log

Blogs

A Time/Place

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A Time/PlaceCommunication Framework

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Lotus Notes / Domino ServerIncludes Learning Space

Netscape Collabra Server

Microsoft NetMeeting

Novell Groupwise

GroupSystems

TCBWorks

WebEx

Groupware

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Goal: to support groupwork Increase benefits / decrease losses of

collaboration

Based on traditional methods Nominal Group Technique

Individuals work alone to generate ideas which are pooledunder guidance of a trained facilitator

Delphi Method

A structured process for collecting and distilling knowledgefrom a group of experts by means of questionnaires

Electronic Meeting System (EMS)

Group Support Systems

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Process Gains: Parallelism ( simultaneous contributions )

Larger groups can participate

Anonymity ( promotes equal participation ) Focus on content not personalities

Triggering ( stimulates thinking ) Synergy ( integrates ideas ) Structure ( facilitates problem solving ) Record keeping ( promotes organizational memory )

GSS Important Features

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Decision room

Multiple use facility

Web-based

GSS Enabling Technologies

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12 to 30 networked personal computers

Usually recessed into the desktop

Server PC

Large-screen projection system Breakout rooms

Need a Trained Facilitator for Success

The Decision (Electronic Meeting) Room

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GSS Meeting Process

Iterate untilthe solution isreached
http://www.groupsystems.com/demos/tools_sv.htmhttp://www.groupsystems.com/demos/tools_eb.htmhttp://www.groupsystems.com/demos/tools_aa.htmhttp://www.groupsystems.com/demos/tools_tc.htmhttp://www.groupsystems.com/demos/tools_go.htmhttp://www.groupsystems.com/demos/tools_vo.htmhttp://www.groupsystems.com/demos/tools_ca.htm

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Visit a GSS Meeting

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Step 1: Prepare an Agenda

Prepare anagenda

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Step 2: Collect Information

think

about therisks to the

company ifthey launcha new line ofsportsdrinks

BrainstormRisk

Think aboutthe risks tocompany ifthey launcha new line

of products

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Step 3: Refine Information

GatherAdditionalInformation

Captureimportantissues forthe listed

items

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Step 4: Prioritize Options

Prioritize RiskBased onLikelihood andImpact

Use ofAlternativeAnalysis Ballot

for twoCriteria

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Step 5: Review Prioritized Options

View andDiscussResults ofVoting

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Chose Risksfor FurtherAnalysis

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CollectAdditionalInput On Risks

Collectadditionalcomments ontop three

risks

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ReviewComments onRisks

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Step 6: Create an Action Plan

Create anAction Plan

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Step 7: Distribute Session Transcripts

Create andDistribute aFinal Report

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Why Successful? Parallelism

Anonymity

Synergy

Structure Record keeping

Needs Organizational commitment

Executive sponsor

Dedicated well-trained facilitator

Good planning

Last Words about GSS?

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Collaborative Networks

Integrated supply-chain Collaborative planning, forecasting, and

replenishment (CPFR)

Collaborative design and productdevelopment

Vendor Managed Inventories

Wal-Mart, Collective Intelligence

Animal Intelligence (swarm intelligence)

Collaborative Planning, Forecasting,

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Collaborative Planning, Forecasting,and Replenishment (CPFR)

An industry-wide project inwhich suppliers and retailerscollaborate in planning and

demand forecasting in orderto ensure that members ofthe supply chain will have theright amount of raw materialsand finished goods when theyneed them

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Collective Intelligence

A shared intelligence that emerges from theintentional cooperation, collaboration, and/orcoordination of many individuals.

Examples: Wikipedia, video games, online

advertising, learner-generated context, In order for CI to happen:

Openness

Peering Sharing

Acting globally

For more info see

Center for CollectiveIntelligence at MIT

(cci.mit.edu)

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A Taxonomy of Collective Intelligence

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Creativity

Is it a fundamental human trait or somethingthat can be learned?

Definition: Creativity is a characteristic of aperson that leads to production of acts, items

and/or instances of novelty Creativity is the product of

a genius vs. an idea generation environment

Creative people tend to have creative lives CREATIVITY INNOVATION

Idea Generation via Electronic Brainstorming

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Creativity

What variables affects creativity1. Cognitive variables: intelligence, knowledge,

skills, etc.

2. Environmental variables: cultural and

socioeconomic factors, working conditions, etc.3. Personality variables: motivation, confidence,

sense of freedom, etc.

Creativity is fostered by

Freedom Permission-to-fail

Allow and Enable rather than Structure andControl

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Creativity

Software that shows creativity Intelligent Agents (Softbots)

Creativity is an intelligent behavior

Software that facilitates human creativity ThoughtPath: promotes outside-the-box thinking

Creative WhackPack (Creative Think): whack youout of your habitual thought process

IdeaFisher: provides language specific

universality - thesaurus

Freedom, Collaboration, Prototyping

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End of the Chapter

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