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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 Output boundary Environment 3 4 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 10 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 12 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 15 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 17 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 Rationalization 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 Decisions made in separate parts of organization without 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. Bounded rationality 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 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 32 DSS as a methodology A DSS is a methodology that supports decision-making. It is: Flexible; Adaptive; Interactive; GUI-based; Iterative; and Employs modeling. 33 ` 34 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 Model management Managed by DBMS Managed by MBMS User interface Knowledge Management and organizational knowledge base 36 37 Data Management Subsystem Components: Database Database management system Data directory Query facility 38 39 Levels of decision making Strategic Tactical Used primarily by middle management to allocate resources Operational Supports top management decisions 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 43