As markets become more competitive and businesses more complex the information that managers need to make decisions has grown to be more demanding. Managers require more decisions support systems for them to cope with the numerous decisions that they have to make.
This workshop therefore focuses on the tools of decision support systems and how they are used. The more knowledgeable an organization is about the use of decision support systems, the better positioned it will be to improve its bottom line and to be more competitive. Evidences will be presented.
- Characteristics of the Decision Making Process
- Phases of Decision Making
- Types of Decision Problems
- Attributes of the Decision Makers
- Strategies for Decision Making
- Implications for Decision Support Systems
- Important features of Decision Support Systems
- Support of Semi-Structured Decisions
- Support for Communications in the Decision Making Process
- Support for Communications among Decision Makers
- Components of a Decision Support System
- Data Mining and Intelligent Agents Model Component.
- The Tools of Decision Support
- Use of SQL to query a Data Base to produce a list of Products or Insurance Services by various categories
- Cash Flow Analysis using a Spreadsheet, Software tools for decision support.
- Data Base Software
- Model Base software
- Statistical Software
- Display Base Software
- The Development of Decision Support System
- Adaptive Design Framework Decision Support System (DSS)
- Development Life Cycle
- Expert Systems in the UK Life Insurance Industry. Current status and Future trends: A Review and Analysis.
- The Risks of DSS
- Models for Group Decision Support
- Basic Features
- Advanced Features
- Artificial Intelligence (AI)
- AI and Natural Language Processing
- Machine Vision
- Expert System
- Differences between DSS and Expert Systems
- A Comparison between Expert systems and DSS
- Characteristics of Expert Systems
- Components of an Expert System
- Knowledge Acquisitions subsystems.
- Knowledge Management System.
- The strategic use of Expert Systems for Risk Management in the Insurance Industry.
- A review of Expert System and a Conventional Information system.
- How an Expert System Works
- Rule-Based Systems
- Frame- Based
- Difference between an Expert System and a Conventional Information System
- Kinds of Problems an Expert System Solves and Opportunities they address, and other Expert Systems Applications in Businesses.
- How Expert Systems are developed.
- The stages of Building an Expert System.
- The Role of the Knowledge Engineers.
- Prototyping Approach in Expert Systems Development
- How Knowledge is acquired from Experts
- The Advantages of Expert System
- Limits of Expert Systems
- Expert System Tools
- The Elements of an Expert System Shell
- Expert systems with applications
- “Application of Data Mining techniques in Customer Relationship Management. An in-depth review and classification.
- Case Problems
On Completion of the workshop, participants should:
- Be Capable of comparing and contrasting expert systems, artificial intelligence and other forms of DSS
- Understand the risks of DSS
- Illustrate the use of Expert Systems and Artificial Intelligence
- Identify Situations where DSS and Expert system will be applicable in business.
- How Expert Systems are Developed
- Identify characteristics of Expert Systems
- Illustrate How Expert Systems Work.
- Illustrate the weakness of Expert Systems
- Discuss Industry Experience in the use of Expert Systems and more.
All Managers, Assistant Managers and Supervisors