Decision Support Executive Information Systems LECTURE 5 Amare

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

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 boundary Environment Output 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) n n n 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) n n n Decision under risk Probability of each of several possible outcomes occurring Risk analysis n n Calculate value of each alternative Select best expected value 8

Influence Diagram (Presenting the model) n n n 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 n n n 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 1. 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 n n n Develop alternative courses of action Analyze potential solutions Create model Test for feasibility Validate results Select a principle of choice n n 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 n n Rationalization n n Effect of each alternative More of good things, less of bad things Courses of action are known quantity Options ranked from best to worse Suboptimization n Decisions made in separate parts of organization without 22

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

Choice Phase (Contd…) n Choice Phase n n n 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 n n Putting solution to work Vague boundaries which include: n n n Dealing with resistance to change User training Upper management support 25

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

Developing Alternatives n Generation of alternatives n n n May be automatic or manual May be legion, leading to information overload Scenarios Evaluate with heuristics Outcome measured by goal attainment 27

Descriptive Models n n Describe how things are believed to be Typically, mathematically based Applies single set of alternatives Examples: n n Simulations What-if scenarios Cognitive map Narratives 28

Problems n Satisfying is the willingness to settle for less than ideal. n n Bounded rationality n n n Form of sub optimization 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. 30

Decision-Making in humans n Cognitive styles n n What is perceived? How is it organized? Subjective Decision styles n n n How do people think? How do they react? Heuristic, analytical, autocratic, democratic, consultative 31

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DSS as a methodology n n A DSS is a methodology that supports decision-making. It is: n n n Flexible; Adaptive; Interactive; GUI-based; Iterative; and Employs modeling. 33

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Business Intelligence n n Proactive Accelerates decision-making Increases information flows Components of proactive BI: n n n 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 n Subsystems: n Data management n n Model management n n n Managed by DBMS Managed by MBMS User interface Knowledge Management and organizational knowledge base 36

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

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Levels of decision making n Strategic n n Tactical n n Used primarily by middle management to allocate resources Operational n n Supports top management decisions Supports daily activities Analytical n Used to perform analysis of data 40

(ad hoc analysis) 41

DSS Classifications n GSS v. Individual DSS n n Decisions made by entire group or by lone decision maker Custom made v. vendor ready made n Generic DSS may be modified for use n n n Database, models, interface, support are built in Addresses repeatable industry problems Reduces costs 42

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