bi ccm unit i

8/2/2019 BI CCM Unit I

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Contents

Business Intelligence and Business Decisions:

Decision Support Systems; Group Decision

Support and Groupware Technologies, Expert

Systems.


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Mintzbergs 10 Management Roles

Interpersonal

Figurehead

Leader

Liaison

Informational

Monitor

Disseminator

Spokesperson

Decisional

Entrepreneur

Disturbance Handler

Resource Allocation

Negotiator


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Productivity

The ratio of outputs to inputs that measures

the degree of success of an organization and

its individual parts


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Factors Affecting Decision-Making

New technologies and better information distribution haveresulted in more alternatives for management.

Complex operations have increased the costs of errors,causing a chain reaction throughout the organization.

Rapidly changing global economies and markets are producinggreater uncertainty and requiring faster response in order tomaintain competitive advantages.

Increasing governmental regulation coupled with politicaldestabilization have caused great uncertainty.


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What do Decision Support SystemsOffer?

Quick computations at a lower cost

Group collaboration and communication

Increased productivity

Ready access to information stored in multiple databases anddata warehouse

Ability to analyze multiple alternatives and apply risk

management

Enterprise resource management Tools to obtain and maintain competitive advantage


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Cognitive Limits

The human mind has limited processing and storage

capabilities.

Any single person is therefore limited in their decision making

abilities. Collaboration with others allows for a wider range of possible

answers, but will often be faced with communications

problems.

Computers improve the coordination of these activities.

This knowledge sharing is enhanced through the use of GSS,

KMS, and EIS.


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Management Support Systems

The support of management tasks by the

application of technologies

Sometimes called Decision Support Systems orBusiness Intelligence


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Management Support Systems Tools

DSS

Management Science

Business Analytics

Data Mining Data Warehouse

Business Intelligence

OLAP

CASE tools

GSS

EIS

EIP

ERM

ERP

CRM SCM

KMS

KMP

ES

ANN

Intelligent Agents

E-commerce DSS


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Decision Support Frameworks

Type of Control

Type of

Decision:

Operational

Control

Managerial

Control

Strategic Planning

Structured(Programmed)

Accounts

receivable,

accounts payable,

order entry

Budget analysis,

short-term

forecasting,

personnel reports

Investments,

warehouse

locations,

distribution centers

Semistructured Production

scheduling,

inventory control

Credit evaluation,

budget

preparation,

project

scheduling,rewards systems

Mergers and

acquisitions, new

product planning,

compensation, QA,

HR policy planning

Unstructured

(Unprogrammed)

Buying software,

approving loans,

help desk

Negotiations,

recruitment,

hardware

purchasing

R&D planning,

technology

development, social

responsibility plans


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Technologies for Decision-MakingProcesses

Type of Decision Technology Support Needed

Structured

(Programmed)

MIS, Management Science

Models, TransactionProcessing

Semistructured DSS, KMS, GSS, CRM, SCM

Unstructured(Unprogrammed)

GSS, KMS, ES, Neuralnetworks


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Technology Support Based onAnthonys Taxonomy

Type of Control

Operational

Control

Managerial

Control

Strategic

Planning

Technology

Support

Needed

MIS,

Management

Science

Management

Science, DSS,

ES, EIS, SCM,

CRM, GSS,

SCM

GSS, CRM,

EIS, ES,

neural

networks,

KMS


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Management Science/OperationsResearch

Adopts systematic approach

Define problem

Classify into standard category

Construct mathematical model

Evaluate alternative solutions

Select solution


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Enterprise Information Systems

Evolved from Executive Information Systems

combined with Web technologies

EIPs view information across entire organizations

Provide rapid access to detailed information through

drill-down.

Provide user-friendly interfaces through portals.

Identifies opportunities and threats


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Enterprise Information Systems

Specialized systems include ERM, ERP, CRM,

and SCM

Provides timely and effective corporate leveltracking and control.

Filter, compress, and track critical data and

information.


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Knowledge Management Systems

Knowledge that is organized and stored in a

repository for use by an organization

Can be used to solve similar or identical problems in

the future

ROIs as high as a factor of 25 within one to two years


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Expert Systems

Technologies that apply reasoning methodologies in a specific

domain

Attempts to mimic human experts problem solving

Examples include: Artificial Intelligence Systems

Artificial Neural Networks (neural computing)

Genetic Algorithms

Fuzzy Logic

Intelligent Agents


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Hybrid Support Systems

Integration of different computer system tools to resolve

problems

Tools perform different tasks, but support each other

Together, produce more sophisticated answers Work together to produce smarter answers


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Emerging Technologies

Grid computing

Improved GUIs

Model-driven architectures with code reuse

M-based and L-based wireless computing Intelligent agents

Genetic algorithms

Heuristics and new problem-solving techniques


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Decision Making

Process of choosing amongst alternativecourses of action for the purpose of attaininga goal or goals.

The four phases of the decision process are: Intelligence

Design

Choice implementation


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Systems

Structure Inputs

Processes

Outputs Feedback from output to decision maker

Separated from environment by boundary

Surrounded by environment

Input Processes Output

boundary

Environment


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System Types

Closed system

Independent

Takes no inputs

Delivers no outputs to the environment

Black Box

Open system

Accepts inputs

Delivers outputs to environment


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Models Used for DSS

Iconic

Small physical replication of system

Analog Behavioral representation of system

May not look like system

Quantitative (mathematical)

Demonstrates relationships between systems


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Phases of Decision-Making

Simons original three phases:

Intelligence

Design

Choice

He added fourth phase later:

Implementation

Book adds fifth stage:

Monitoring


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Decision-Making Intelligence Phase

Scan the environment

Analyze organizational goals

Collect data

Identify problem

Categorize problem

Programmed and non-programmed

Decomposed into smaller parts

Assess ownership and responsibility for problem

resolution


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Decision-Making Design Phase

Develop alternative courses of action

Analyze potential solutions

Create model

Test for feasibility Validate results

Select a principle of choice

Establish objectives

Incorporate into models Risk assessment and acceptance

Criteria and constraints


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Descriptive Models

Describe how things are believed to be

Typically, mathematically based

Applies single set of alternatives

Examples:

Simulations

What-if scenarios

Cognitive map Narratives


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Developing Alternatives

Generation of alternatives

May be automatic or manual

May be legion, leading to information overload

Scenarios

Evaluate with heuristics

Outcome measured by goal attainment


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Problems

Satisficing is the willingness to settle for less

than ideal.

Form of suboptimization

Bounded rationality

Limited human capacity

Limited by individual differences and biases

Too many choices


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Decision-Making Choice Phase

Decision making with commitment to act

Determine courses of action

Analytical techniques Algorithms

Heuristics

Blind searches

Analyze for robustness


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Decision-Making Implementation

Phase

Putting solution to work

Vague boundaries which include:

Dealing with resistance to change User training

Upper management support


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Source: Based on Sprague, R.H., Jr., A Framework for the Development of DSS. MIS Quarterly, Dec. 1980, Fig. 5, p. 13.


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Decision Support Systems

Intelligence Phase

Automatic

Data Mining

Expert systems, CRM, neural networks

Manual

OLAP

KMS

Reporting

Routine and ad hoc


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Design Phase

Financial and forecasting models

Generation of alternatives by expert system

Relationship identification through OLAP and data

mining

Recognition through KMS

Business process models from CRM, RMS, ERP,and SCM


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Choice Phase

Identification of best alternative

Identification of good enough alternative

What-if analysis

Goal-seeking analysis

May use KMS, GSS, CRM, ERP, and SCM systems


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Implementation Phase

Improved communications

Collaboration

Training

Supported by KMS, expert systems, GSS


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Decision-Making In Humans

Temperament

Hippocrates personality types

Myers-Briggs Type Indicator

Kiersey and Bates Types and Motivations

Birkmans True Colours

Gender


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Decision-Making In Humans

Cognitive styles

What is perceived?

How is it organized?

Subjective

Decision styles

How do people think?

How do they react? Heuristic, analytical, autocratic, democratic,

consultative


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Groupwork

Groupwork Collaboration and communication

Members can be located in different places and work atdifferent times

Information may be located external to the project Allows for rapid solutions

May exhibit normal team problems of synergy or conflict

Often Internet based

Groupware tools support groupwork

Work called computer-supported cooperative work Collaborative computing


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Communication Support

No collaboration without communication

Internet supplies fast, reliable, inexpensive

support

Groups need not only communication, but

information and knowledge


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Time/Place Communication Framework

Effectiveness ofcollaborativegroup depends on

Time

synchronous orasynchronoustransmission ofinformation

Place

location ofparticipants


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Groupware

Software providing collaborative support to groups

Different time/place applications

Most use Internet technologies

Most offer one or more capabilities Electronic brainstorming

Free flow of ideas and comments Electronic conferencing or videoconferencing

Group scheduling and calendars

Conflict resolution

Model building

Electronic document sharing

Voting services Electronic meeting services also available

Enterprise-wide systems expensive in cost and human resources


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Popular Groupware

Lotus Notes/Domino

Microsoft Netmeeting

Groove Workspace GroupSystems MeetingRoom and OnLine

WebEx


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Benefits and Problems

Benefits of groupwork Process gains

Nominal group technique

Delphi method

Technology applied as GSS Hardware and software combined to enhance groupwork

Collaborative computing

Problems in groupwork

Process losses inefficient


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GSS

Common group activities with computer assistance Information retrieval

Information sharing Parallelism

Anonymity Information use

Support participants Improve productivity and effectiveness of meetings

More efficient decision-making

Increase effectiveness of decisions


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GSS Technology Deployment

Special purpose decision room Electronic meeting rooms

Software operates across LAN

Allowed for face-to-face meetings

Trained facilitator coordinates meeting

Group leader structures meeting with facilitator Multiple use facility

General purpose computer lab

Effective way to lower costs

Trained facilitator coordinates meeting

Group leader structures meeting with facilitator

Web-based groupware with clients Anytime/anyplace meetings with deadlines established

Software bought or leased

No facility costs

Flexible


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GSS Meeting Process

Group leader meets with facilitator to plan meeting structure.

Participants meet on computers.

Group leader or facilitator poses question.

Participants brainstorm by entering comments into computer.

Facilitator employs idea organization software to sort comments into

common themes. Results are displayed.

Facilitator or group leader leads discussion.

Themes are prioritized.

Highest priority topics are either sent through the process again forfurther discussion or a vote is taken.


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GSS Meeting Process

Standard Process

Exploratory idea generation

Idea organization tool

Prioritization New idea generation

Selection of final idea

Success based upon effectiveness, reductionin costs, better decisions, increasedproductivity


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GSS and Distance Education

Classroom collaborative computing advantages Brainstorming, chat, discussion boards

Distribution of information, lectures Publishes to course site

Videoconferenced

Consistent materials Textbooks can be bound or electronic

E-mails and listservs One-on-one interaction

Allows for global classrooms

Anytime/anyplace with fixed deadlines Flexible time frame

Doesnt interfere with work shift

Low delivery costs with large audiences


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GSS and Distance Education, continued

Disadvantages: Fewer social interactions

Communication problems

Students must be self-starters and highly disciplined

Classes require major technical and administrative support

Technical infrastructure must be reliable Courses may need to be redesigned for online

Special training

Corporate training online: Allows anytime/anyplace training

Lowers costs

Decreases time away from jobs Shortens learning process

Delivered via Intranet, intranets, extranets, audio and video conferencing


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Creativity Support System

Creativity Fundamental human trait

Level of achievement

Can be learned

Organizations recognize value in innovation

Stimulated by electronic brainstorming software Free flow idea generation

Creative computer programs Smartbots function as facilitators

Identify analogies in letter patterns

Draw art

Write poems

Computer programs stimulate human productivity


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Experts

Experts Have special knowledge, judgment, and experience

Can apply these to solve problems Higher performance level than average person

Relative Faster solutions

Recognize patterns

Expertise Task specific knowledge of experts

Acquired from reading, training, practice


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Expert Systems Features

Expertise Capable of making expert level decisions

Symbolic reasoning

Knowledge represented symbolically Reasoning mechanism symbolic

Deep knowledge Knowledge base contains complex knowledge

Self-knowledge Able to examine own reasoning

Explain why conclusion reached


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Applications of Expert Systems

DENDRAL project Applied knowledge or rule-based reasoning commands

Deduced likely molecular structure of compounds

MYCIN Rule-based system for diagnosing bacterial infections

XCON Rule-based system to determine optimal systems configuration

Credit analysis Ruled-based systems for commercial lenders

Pension fund adviser Knowledge-based system analyzing impact of regulation andconformance requirements on fund status


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Applications

Finance Insurance evaluation, credit analysis, tax planning, financial planning and

reporting, performance evaluation

Data processing Systems planning, equipment maintenance, vendor evaluation, network

management

Marketing Customer-relationship management, market analysis, product planning

Human resources HR planning, performance evaluation, scheduling, pension management, legal

advising

Manufacturing Production planning, quality management, product design, plant site

selection, equipment maintenance and repair


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Environments

Consultation (runtime)

Development


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Major Components of Expert Systems

Major components

Knowledge base Facts

Special heuristics to direct use of knowledge Inference engine

Brain

Control structure

Rule interpreter User interface

Language processor


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Additional Components of Expert Systems

Additional components Knowledge acquisition subsystem

Accumulates, transfers, and transforms expertise to computer

Workplace Blackboard

Area of working memory Decisions

Plan, agenda, solution

Justifier Explanation subsystem

Traces responsibility for conclusions

Knowledge refinement system Analyzes knowledge and use for learning and improvements


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Knowledge Presentation

Production rules

IF-THEN rules combine with conditions to produce

conclusions

Easy to understand

New rules easily added

Uncertainty

Semantic networks Logic statements


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Inference Engine

Forward chaining

Looks for the IF part of rule first

Selects path based upon meeting all of the IF requirements

Backward chaining Starts from conclusion and hypothesizes that it is true

Identifies IF conditions and tests their veracity

If they are all true, it accepts conclusion

If they fail, then discards conclusion


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General Problems Suitable for Expert

Systems

Interpretation systems Surveillance, image analysis, signal interpretation

Prediction systems Weather forecasting, traffic predictions, demographics

Diagnostic systems Medical, mechanical, electronic, software diagnosis

Design systems Circuit layouts, building design, plant layout

Planning systems Project management, routing, communications, financial plans


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General Problems Suitable for Expert

Systems

Monitoring systems Air traffic control, fiscal management tasks

Debugging systems Mechanical and software

Repair systems Incorporate debugging, planning, and execution capabilities

Instruction systems Identify weaknesses in knowledge and appropriate remedies

Control systems Life support, artificial environment


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Benefits of Expert Systems

Increased outputs

Increased productivity

Decreased decision-making time

Increased process and product quality

Reduced downtime

Capture of scarce expertise

Flexibility

Ease of complex equipment operation

Elimination of expensive monitoring equipment

Operation in hazardous environments

Access to knowledge and help desks


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Benefits of Expert Systems

Ability to work with incomplete, imprecise, uncertain data

Provides training

Enhanced problem solving and decision-making

Rapid feedback

Facilitate communications

Reliable decision quality

Ability to solve complex problems

Ease of knowledge transfer to remote locations

Provides intelligent capabilities to other information systems


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Limitations

Knowledge not always readily available

Difficult to extract expertise from humans Approaches vary

Natural cognitive limitations Vocabulary limited

Wrong recommendations

Lack of end-user trust

Knowledge subject to biases Systems may not be able to arrive at conclusions


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Success Factors

Management champion

User involvement

Training

Expertise from cooperative experts

Qualitative, not quantitative, problem

User-friendly interface

Experts level of knowledge must be high


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Types of Expert Systems

Rule-based Systems Knowledge represented by series of rules

Frame-based Systems Knowledge represented by frames

Hybrid Systems

Several approaches are combined, usually rules and frames Model-based Systems

Models simulate structure and functions of systems

Off-the-shelf Systems Ready made packages for general use

Custom-made Systems

Meet specific need Real-time Systems

Strict limits set on system response times

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