topicvi_busines_intelligent_systems.pdf

7/28/2019 TopicVI_Busines_Intelligent_Systems.pdf

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INS 601 MANAGEMENTINFORMATION SYSTEMS

Topic VIopic VIBusiness Intelligent Systemsusiness Intelligent Systems


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Decision Support systems

Business Intelligence Systems

Level of management Decision making Information Quality DimensionsManagement Information systemsDSS

DSS Types DSS Components DSS Analyses

A.I in Business Neural Networks Fuzzy Logic Virtual Reality Expert System

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Business Intelligence SystemsApplications & Technologies that focus on:-

Gathering

Storing

Analysing

Providing Access to data

From many different sources to help users make better businessdecisions.

eg MIS, DSS, Artificial Intelligence based systems

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Decisions in the EDecisions in the E--BusinessBusiness

ExcutiveLevel

ManagementLevel

OperationalLevel

Decis

ions

Info

rm

ation

Decision Characteristics

Unstructured

Semi-

structured

Structured

Ad-Hoc, Unscheduled,Wide Scope

Pre-specified, DetailedNarrow Scope

InformationCharacteristic


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Decisions in the E-Business

Strategic Management:

Board of Directors, CEOs etc

They develop overall organisational goals, strategies, policies as part

of strategic planning process.

Tactical Management:

General Manager, Divisional Director

They develop short & medium range plans. They allocate resourcesand monitor performance of their subunits.

Operational Management:

Operational Managers, Supervisors etcThey direct the use of resources and performance of tasks accordingto procedures and within budgets & schedules.

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Attributes of Decision Making

INS 601 23rd May 2011 VishweshAkre

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Information, Decisions & Management

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Form

Time

Conte

nt

Three Dimensions of Information Quality

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Information Quality DimensionsTime Dimension :

TimelinessFrequency

Currency

Time Period

Content Dimension :Accuracy

RelevanceCompleteness

Performance

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Information Quality DimensionsForm Dimension :

Clarity

Detail

Order

Presentation

Media

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Management Information Systems

[MIS]MIS primarily provide information on the firmsperformance to help managers in monitoring & controlling

the business.They typically produce fixed, regularly scheduled reportsbased on the data extracted and summarized from the

organisations underlying TPS.

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Management Information System

Reports

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Scheduled ReportsScheduled Reports

Exception ReportsException Reports

Demand ReportsDemand Reports

Push ReportsPush Reports

Major

ManagementInformationSystems Reports

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MIS Reports:Periodic Scheduled Reports:

This traditional form of providing information to managers uses a pre-specified format designed to provide managers with information on aregular basis.

eg: Daily or weekly Sales Analysis Reports, Monthly Financial

Statements.

Exception Reports:

These reports are produced only when exception occurs. In some

cases, reports are produced periodically, but contain information onlyabout these exceptional conditions.

eg: Credit Managers can be provided with a report that containsonly information on customers who exceed their credit limits.

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MIS Reports:Demand Report:

Information is available only when a manager demands it.

eg : Query Languages &Report Generators enable managers at PCworkstations to get immediate responses or obtain customisedreports as a result of their requests for the information they need.

Push Report:Information is PUSHED to a managers networked workstation.

eg: Many companies are using web casting software to selectivelybroadcast reports and other information to the networked PCs of

managers and specialists over their corporate Intranets.

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MIS Examples:-Organisation

CaliforniaPizza Kitchen

MIS Application

Inventory Express applicationsremembereach restaurantsordering patterns and compares theamount of ingredients used per menuitem to predefined portionmeasurements established bymanagement. The system identifies

restaurants with out-of-line portionsand notifies their management so thatcorrective action can be taken.

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Decision Support Systems

DSS are computer based Information Systems

that provide interactive information support tomanagers & business professionals during thedecision making process.

DSS systems are :-1. Analytical Models.

2. Specialized databases.

3. An Interactive computer based modeling process tosupport the making of semi structured &unstructured business decisions.

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Components of a DSS

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MODEL Driven DSS

Model Driven DSS were primarily stand alonesystems isolated from major organisationalInformation systems that used some type of model toperformWhat-ifanalysis or Sensitivityanalysis.

Such systems were often developed by end userdivisions or groups not under central Information

system control. Their analysis capabilities werebased on a strong theory or model combined with agood user interface that made the model easy to use.


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Model Driven DSS


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What If ? AnalysisThis analysis involves observing how changesto selected variables, or relationships amongvariables result into changes in the values ofother variables.

Eg If the Advertising Budget is cut by 10% - Whatwould be the affect on Sales?

This type of analysis would be repeated until the

manager is satisfied with what results arerevealed about affects of various possibledecisions.

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Sensitivity AnalysisThis analysis observes how repeatedchanges to a single variable affect other

variables?Sensitivity analysis is a special case ofWhat if?analysis where typically the value of only one

variable is changed repeatedly and the resultingchanges on other variables are observed.e.g. Lets cut advertising budget repeatedly by

$100 so we can see its impact on sales?

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Goal Seeking Analysis

(How Can? Analysis)This involves making repeated changes to

select variables until a chosen variable reaches atarget value.

This reverses the direction of the analysis done in

What if& Sensitivityanalysis.eg. Lets try increasing the advertising budget untilSales reach $ 1 Million.


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Online Analytical Processing

OLAP enables managers and analysts tointeractively examine & manipulate large amountsof detailed & consolidated data from many

perspectives.OLAP involves analyzing complex relationshipsamong 1000s or even millions ofdata items

stored in multi-dimensional databases to discoverpatterns, trends & exception conditions.

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Online Analytical Processing

OLAPServer

OLAPServer

Multi-

dimensional

database

Corporate

Databases

Client PC

Operational DBData MartsData WarehouseWeb-enabled OLAP

Software

Data is retrieved from corporate databases

and staged in an OLAP multi-dimensional

database


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OLAP analytical operations:-


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OLAP analytical operations:-

Consolidation:

Consolidation involves the aggregation of data. This caninvolve simple roll-ups or complex groupings.

eg SALES Data can be rolled up to DISTRICT level from

OFFICE level.Drill Down:

OLAP can go in the reverse direction and automatically

display detail data that comprise consolidated data. Thisprocess is called Drill Down.

eg SALES figures of Individual products.

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OLAP analytical operations:-Slicing & Dicing:

Slicing & Dicing refers to the ability to look at the

database from different viewpoints.eg

One Slice: of Sales database would be all sales of product type

within all regions.Another Slice: of Sales database might show all sales channelwithin each product type.

Slicing & Dicing is often performed along a time axis inorder to analyse trends & find time based patterns in thedata.

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Components of a DSS

DSS DatabaseDSS Database is a collection of current orhistorical data from a number of applications.

It may be a small database residing on a PC,combined with external data or a huge datawarehouse.

The data in DSS database are copies ofProduction databases so that using the DSS does

not interfere with critical operational systems.

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Components of a DSS

DSS Software SystemIt contains software tools that are used for data analysis.

It contains :-

OLAP tools

Data mining Tools

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Components of a DSS

DSS User InterfaceThe DSS user interface permits easy interaction betweenusers of the system and the DSS software Tools.

A Graphical, Easy-to-use, flexible user interface supportsa dialogue between the user & the DSS.

DSS requires a high level of user participation to makesure that the system provides the information needed bythe managers.

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Group Decision Support SystemsDSS focus primarily on Individual Decision Making.

But much of the work is accomplished in Groups withingroups, hence a special category of DSS has beendeveloped to support group decision making GDSS.

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GDSSA GDSS is an interactive computer based systemto facilitate the solution of unstructured problemsby a set of decision makers working together as agroup.

GDSS make meetings more productive byproviding tools to facilitate planning, generating,organizing and evaluating ideas, establishing

priorities & documenting meeting proceeding forothers in organisations.


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GDSS Components


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GDSS ComponentsHardware: It includes :-

Electronic Hardware:- Electronic Display Boards

Audio Visuals

Computers

Networking EquipmentsConference Facilities:

ROOM

CHAIRS

TABLES

Laid out in a manner that supports GroupCollaboration

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GDSS ComponentsSoftware Tools:-

Although groupware tools for collaborative work canbe used to support Group Decision Making, there arespecific GDSS tools for supporting Group Meetings.

These tools were originally developed for meetings inwhich all participants are in the same room, but they

can be used for networked meetings in whichparticipants are in different locations.

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GDSS Software TOOLSElectronic Questionnaires

Electronic Brain Storming Tools Idea Organizers

Questionnaire tools

Tools for voting or setting prioritiesStakeholder Identification & Analysis Tools

Policy Formation Tools

Group Dictionaries

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Collaboration LaboratoriesIn a collaboration laboratory, individuals work on their owndesktop PCs or workstations. Their Input is integrated on

a file server and is viewable on a common screen to allparticipants.

In most systems the integrated Input is also viewable onthe individual participant's screen

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Electronic Meeting SystemIn an EMS :-

Each attendee has a workstation.The workstations are networked and connected to thefacilitators console, which acts as Control panel for themeeting.

Attendees have full control over their own desktopsystems can work on PC tools or on the screenassociated with current meeting agenda & tools.

During the meeting, all input to the integrated screens issaved on the file server & the participants work is keptconfidential.

On completion of meeting, the full record of the meeting isavailable to anyone in need for access

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Overview of AI

Artificial Intelligence (AI) is a field of Science &Technology based on disciplines such as

Computer Science Biology

Linguistics

Mathematics

Engineering

The Goal of(AI) is to develop computers that can Think

Hear

See

Walk Talk

Feel

Like Humans

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Overview of AI

A Major thrust in is to develop computer functionsnormally associated with Human Intelligence:-

ReasoningLearningProblem Solving

Turing Test (Alan Turing 1950)According to this test, a computer coulddemonstrate intelligence if a human interviewer

conversing with an unseen human or an unseencomputer could not tell which is which.

AI Application Areas in Business

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AI Application Areas in Business

Neural NetworksNeural Networks

Virtual RealityVirtual Reality

Expert SystemsExpert Systems

AI Application

Areas inBusiness

AI Application

Areas inBusiness

Fuzzy Logic SystemsFuzzy Logic Systems

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Neural Networks

Neural Networks are computing systems modeledafter the brains mesh-like network ofinterconnected processing elements called asneurons.

Like the brain, the interconnected processors in aneural network operate in parallel andinterconnect dynamically with each other,

enabling the network to learnfrom data itprocesses.

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Neural Networks

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Neural Networks

A neural network can be trained to learn whichcredit characteristics can lead to :

Good Loan

Bad Loan

In the above example the developers of theneural network can provide it with data from manyexamples of credit applications and loan results

to process the knowledge in Neurons

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Fuzzy Logic Systems

Fuzzy Logic systems represents a small but graduallygrowing application of AI in business.

Fuzzy Logic resembles Human reasoning as it allowsapproximate values and inferences and incomplete orambiguous data instead of relying on crisp datasuch asYes/Nochoices.


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Fuzzy Logic Systems


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Fuzzy Logic systems consist of:-

F L i S t

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Fuzzy Logic Systems

Credit Analysis Application

Fuzzy Logic Rules

IfINCOME is INCREASINGThen RISK is somewhat

DECREASING

IfCASH RESERVES areLOW

Then RISK is veryINCREASED

Fuzzy Logic SQL Query

SELECT companies

FROM database

WHERERevenues are very HIGH

AND

(Income/Total Employee)Ratio is Reasonable.

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Fuzzy Logic Systems

Notice that Fuzzy Logic uses terminology such as VeryHigh, Increasing, Somewhat Decreasing.

This enables Fuzzy Systems to process incomplete dataand quickly provide approximate but acceptable solutionsto problems that are difficult for other methods to solve.

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Virtual Reality

VR is a computer simulated reality, and is a fastgrowing area of AI that had its origins in efforts to

build more natural, multi sensory human computer interfaces.VR relies on multi sensory I/O devices such as

Tracking Headset with Video Goggles & Stereo phones.Data glove with fiber optic sensors that track yourbody movements.

A Walker that monitors the movement of your feet.

Virtual Reality

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Virtual Reality

Virtual Reality

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Virtual Reality


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Virtual Reality - Applications


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E t S t

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Expert Systems

A Knowledge Based Information System (KBIS) adds aknowledge base to the major components found in other

types of CBIS.An Expert System (ES) is a KBIS that uses its knowledgeabout a specific complex application area to act as anexpert consultant to end users.

Expert Systems provide answers to questions in a veryspecific problem area by making human like inferencesabout knowledge contained in a specialized knowledge

base.

Components of Expert Systems

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Components of Expert Systems

The Expert SystemThe Expert System

Knowledge

Base

User Workstation

ExpertAdvice User

Interface

Programs

UserInterface

Programs

InferenceEngine

Program

InferenceEngine

Program

Expert System DevelopmentExpert System Development

Workstation

KnowledgeEngineering

KnowledgeAcquisition

Program

KnowledgeAcquisition

Program

Expert and/orKnowledge Engineer

E t S t C t

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Expert Systems - Components

Knowledge Base1. Facts about a specific area

2. Heuristics (rules of thumb) that expresses the reasoningprocedures of an Expert on the subject.

Software Resources1. Inference Engine

2. Software User Interface

3. Knowledge acquisition programs


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E t S t b d t li h b i t k

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Expert Systems can be used to accomplish many business tasks:

Decision Management. This includes systemsthat appraise situations or consider alternativesand make recommendations based on criteria

supplied during the discovery process. Examplesinclude loan portfolio analysis, employeeevaluation, insurance underwriting, demographic

forecasts.

Expert Systems can be used to accomplish many business

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p y p y

tasks:Diagnostic/Troubleshooting. This is the use ofsystems that infer underlying causes from

reported symptoms and history. Examplesinclude equipment calibration, help deskoperations, software debugging, medicaldiagnosis.

Maintenance/Scheduling. This includes systemsthat prioritize and schedule limited or time-criticalresources. Examples include maintenance

scheduling, production scheduling, educationscheduling, project management.


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p y p y

tasks:Design/Configuration. This is the use of systems that helpconfigure equipment components, given existing

constraints that must be taken into account. Examplesinclude computer option installation, manufacturabilitystudies, communications networks, optimum assemblyplan.

Selection/Classification. These are systems that helpusers choose products or processes from among large orcomplex sets of alternatives. Examples include material

selection, delinquent account identification, informationclassification, suspect identification.


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p y p ytasks:

Process Monitoring/Control. This includes systems thatmonitor and control procedures or processes. Examplesinclude machine control (including robotics), inventorycontrol, production monitoring, chemical testing.

Expert systems provide a business with faster, consistentexpertise. They also help preserve organizationalknowledge. However, they are not without limitations. ESare not suitable for every problem situation. They excelonly in solving specific types of problems in a limiteddomain of knowledge. They fail to solve problemsrequiring a broad knowledge base. Expert Systems arealso difficult and costly to develop and maintain.

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