Decision Support & Executive Information Systems: LECTURE 5 Amare Michael Desta

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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 2

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 Output boundary Environment 3

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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 5

Decision-Making

(Certainty)

- Assume complete knowledge - All potential outcomes known - Easy to develop - Resolution determined easily - Can be very complex 6

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 7

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 8

( 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 9

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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 11

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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 13

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 14

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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) 16

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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 18

Intelligence Phase

( Contd…) - Intelligence Phase - Automatic - Data Mining - Expert systems, CRM, neural networks - Manual - OLAP - KMS - Reporting - Routine and ad hoc 19

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 20

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… 21

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 22

Choice Phase (Contd...)    Decision making with commitment to act Determine courses of action     Analytical techniques Algorithms Heuristics Blind searches Analyze for robustness 23

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 24

Decision-Making Implementation Phase   Putting solution to work Vague boundaries which include:  Dealing with resistance to change   User training Upper management support 25

Implementation Phase (Contd…)  Implementation Phase     Improved communications Collaboration Training Supported by KMS, expert systems, GSS 26

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 27

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 28

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 29

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 31

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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 35

Components of DSS  Subsystems:  Data management  Managed by DBMS    Model management  Managed by MBMS User interface Knowledge Management and organizational knowledge base 36

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Data Management Subsystem  Components:     Database Database management system Data directory Query facility 38

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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 40

( ad hoc analysis) 41

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 42

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