bi ccm unit i
TRANSCRIPT
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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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Decision Support Systems
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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Decision Support Systems
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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Decision Support Systems
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