Characteristics and Components of Decision Making Process Important

Characteristics and Components of Decision Making Process

Important Features of Decision Support Systems An effective Decision Support System needs to incorporate a number of features. o Support of semi structured Decisions – Semi structured problems involve a decision-making process that can’t be defined before actually going through the process of making the decision. o Support for Database Access and Modeling – DSS attempt to combine the use of models or analytic techniques with traditional data access and retrieval functions. A manager can overcome some of the problems associated with traditional information systems by determining what databases can be used, by defining what data analysis techniques are required, and by identifying what outputs are meaningful. o Support for All Phases of the Decision-Making Process – An effective decision support system should support the three phases of the decision making process: intelligence, design and choice. During the intelligence phase a situation requiring a decision is visualised. During the Design Phase the problem is defined and alternative solutions are considered. During the Choice Phase a course of action is selected.

Support for Communications among Decision makers Decision Support Systems must support decision making at all levels of the organisation, because some decisions require communications among decision marking at all levels, decision support systems need to support group decision making. Availability of Memory Aids In making decisions managers constantly have to recall information or the results of operations conducted at previous times. Decision makers need memory aid, so a decision support system should provide them. Availability of Control aids for Decision Making Many managers feel some anxiety about using computer-based systems. Without effective training in the early phases of computer operation, managers may give up and return to paper-and-pencil methods.

Components of a Decision Support System q Data Components of a Decision Support System q Building a Data Warehouse q Data Mining and Intelligent Agents § § Associations – The Process of linking together events. Sequences – The process of identifying patterns. Classifications – The process of organising data into patterns Clusters – The process of inferring rules about certain subgroups that distinguish them from subgroups. q Model Component § Statistical Models – regression analysis, variance analysis, exponential smoothing. § Accounting Models – depreciation, tax planning, cost analysis § Personal Models – In-basket simulations, role playing exercises § Marketing Models – Advertising Strategy Analysis, Consumer Choice, Consumer switching behaviour.

Development of Decision Support Systems USER COGNITIVE LOOP IMPLEMENTATION LOOP User Learning New Uses New Versions Feedback about needed changes Pressure of Evolution DESIGNER SYSTEM Evolution of system capabilities EVOLUTION LOOP

Decision Support Systems Development Life Cycle Planning Application Research Analysis Design Construction Testing Evaluation Training Operation Maintenance Adaptation

Benefits of Decision Support Systems Ø Ø Ø The ability to examine more alternatives The ability to achieve a better understanding of the business The ability to respond quickly to unexpected situations The ability to carry out ad hoc types of reporting and analysis The ability to provide timely information for control of ongoing operations Ø The ability to save time and costs Ø The ability to make better decisions

The Risks of Decision Support Systems Ø Lack of Quality Assurance Ø Lack of Data Security Ø Failure to Specify Correct Requirements Ø Failure to understand Design Alternatives

Expert Systems and Decision Support Systems DECISION SUPPORT SYSTEMS EXPERT SYSTEMS Objective Assists the human Provides ‘expert’ consulting Decision Maker The Human The system Query Type Human queries the machine Machine queries the human Problem Area Complex, wide Narrow domain Database Includes factual knowledge Includes procedures and data Evolution Adapts to the changing environment Supports a fixed problem domain

A Comparison between Expert Systems and Decision Support Systems PROCESSES FEATURES Specify objectives Modeling syntax DSS Retrieve Data entry DSS Generate Alternatives “What-if” analysis DSS Infer consequences of alternatives Modeling syntax with IF/THEN/ELSE logic ES Assimilate numerical and graphical information Statistical functions ES Evaluate sets of sequences Financial evaluation functions; optimisation ES

The Components of Expert System Explanation Subsystem Knowledge Base Inference Engine Knowledge acquisition subsystem User interface Decision Maker Expert: Knowledge Engineer

Characteristics of an Expert System CHARACTERISTICS DEFINITION Goal Reason for building the system Domain General area of application Task Specific task the system is to accomplish Input Data needed to accomplish the task Output Results of the system Paradigm Conceptual Problem-solving model used to structure the system Tools Software environment in which the system is implemented Methodology Method used to build the system