LECTURE 5 Amare Michael Desta

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

2. 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

3. 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

5. System Types - Closed system - Independent - Takes no inputs - Delivers no outputs to the environment - Black Box • Open system - Accepts inputs - Delivers outputs to environment

6. Decision-Making (Certainty) - Assume complete knowledge - All potentialoutcomes known - Easy to develop - Resolution determined easily - Can be very complex

7. 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

8. 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

9. 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

11. 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

13. 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

14. 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

16. 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)

18. 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

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

20. 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

21. 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…

22. 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

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

24. 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 ERPsystems

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

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

27. 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

28. 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

29. 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

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

31. 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

33. 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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35. 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

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

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

40. 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

41. (ad hoc analysis)

42. 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