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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