1 lecture 5 amare michael desta decision support & executive information systems:
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LECTURE 5
Amare Michael Desta
Decision Support & Executive Information
Systems:
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Decision Support Systems
- Systems designed to support managerial decision-making in unstructured problems
- More recently, emphasis has shifted to inputs from outputs
- Mechanism for interaction between user and components
- Usually built to support solution or evaluate opportunities
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Role of Systems in DSS- Structure
- Inputs - Processes - Outputs - Feedback from output to decision
maker- Separated from environment by boundary- Surrounded by environment
Input Processes Outputboundary
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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Decision-Making (Certainty)
- Assume complete knowledge- All potential outcomes known- Easy to develop- Resolution determined easily- Can be very complex
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Decision-Making (Uncertainty)
Several outcomes for each decision Probability of occurrence of each
outcome unknown Insufficient information Assess risk and willingness to take it Pessimistic/optimistic approaches
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Decision-Making (Probabilistic)
Decision under risk Probability of each of several possible
outcomes occurring Risk analysis
Calculate value of each alternative Select best expected value
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Influence Diagram (Presenting the model) Graphical representation of model Provides relationship framework Examines dependencies of
variables Any level of detail Shows impact of change Shows what-if analysis
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Modeling with Spreadsheets Flexible and easy to use End-user modeling tool Allows linear programming and
regression analysis Features what-if analysis, data
management, macros Seamless and transparent Incorporates both static and dynamic
models
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Simulations- Imitation of reality- Allows for experimentation and time compression- Descriptive, not normative- Can include complexities, but requires special skills- Handles unstructured problems- Optimal solution not guaranteed- Methodology - Problem definition - Construction of model - Testing and validation - Design of experiment - Experimentation & Evaluation - Implementation
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Simulations- Probabilistic independent variables - Discrete or continuous distributions - Time-dependent or time-independent- Visual interactive modeling - Graphical - Decision-makers interact with model - may be used with artificial intelligence- Can be objected oriented
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Decision Making- Process of choosing amongst alternative
courses of action for the purpose of attaining a goal or goals.
- The four phases of the decision process are: (Simon’s)
- Intelligence - Design - Choice - Implementation - Monitoring (added recently)
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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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Intelligence Phase( Contd…)- Intelligence Phase - Automatic - Data Mining - Expert systems, CRM, neural networks - Manual - OLAP - KMS - Reporting - Routine and ad hoc
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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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Design Phase (Contd…)- 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 and
ERP etc…
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Decision-Making Choice Phase
- Principle of choice - Describes acceptability of a solution approach- Normative Models - Optimization
Effect of each alternative Rationalization
More of good things, less of bad things Courses of action are known quantity Options ranked from best to worse
Suboptimization Decisions made in separate parts of organization without
consideration of whole
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Choice Phase (Contd...) Decision making with commitment
to act Determine courses of action
Analytical techniques Algorithms Heuristics Blind searches
Analyze for robustness
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Choice Phase (Contd…) Choice Phase
Identification of best alternative Identification of good enough
alternative What-if analysis Goal-seeking analysis May use KMS, GSS, CRM, and ERP
systems
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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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Implementation Phase (Contd…) Implementation Phase
Improved communications Collaboration Training Supported by KMS, expert systems,
GSS
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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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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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Problems Satisfying is the willingness to
settle for less than ideal. Form of sub optimization
Bounded rationality Limited human capacity Limited by individual differences and
biases Too many choices
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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-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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DSS as a methodology A DSS is a methodology that supports
decision-making. It is:
Flexible; Adaptive; Interactive; GUI-based; Iterative; and Employs modeling.
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Business Intelligence Proactive Accelerates decision-making Increases information flows Components of proactive BI:
Real-time warehousing Exception and anomaly detection Proactive alerting with automatic recipient
determination Seamless follow-through workflow Automatic learning and refinement
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Components of DSS Subsystems:
Data management Managed by DBMS
Model management Managed by MBMS
User interface Knowledge Management and
organizational knowledge base
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Data Management Subsystem Components:
Database Database management system Data directory Query facility
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Levels of decision making Strategic
Supports top management decisions Tactical
Used primarily by middle management to allocate resources
Operational Supports daily activities
Analytical Used to perform analysis of data
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(ad hoc analysis)
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DSS Classifications GSS v. Individual DSS
Decisions made by entire group or by lone decision maker
Custom made v. vendor ready made Generic DSS may be modified for use
Database, models, interface, support are built in
Addresses repeatable industry problems Reduces costs
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