1. Decision Support System (DSS): It’s a computer-based system that aids the process of decision-making. It is an interactive, flexible and adaptable computer system. It is specially developed for supporting the solution of a non-structured management problem for improved decision-making. DSS is a specific class of computerized information systems that supports business and organizational decision-making activities.
Components of DSS:
- Model Management
- Data Management
- User Interface Management
- It saves time.
- Enhances efficiency
- Reduces the cost
- It improves personal efficiency
- It increases the decision-maker satisfaction.
Disadvantages:
- Information Overload
- Status reduction
- Over-emphasize decision-making.
Types of Decision Support systems are Document-driven, Data-driven, Knowledge-driven, Model-driven, and Communication-driven.
Applications include medical diagnosis, business management, agriculture, rail projects, and many more.
Examples: GPS route planning, Crop-planning, ERP dashboards, and others.
2. Expert System: It is a computer program that is designed to mimic the decision-making ability of a decision-maker. It organizes a set of knowledge about a particular subject. It contains facts and judgmental knowledge which gives it the ability to guess like a human. There are set of rules on which it makes decisions using an if-else structure. The inference engine does reasoning by manipulating the knowledge base. The user interface represents questions and information to the operator and also receives answers from the operator.
Components of Expert System:
- Knowledge Base
- Knowledge acquisition and learning module
- Inference Engine
- User Interface
- Explanation module
Advantages:
- Cost is reduced
- Danger is reduced
- Increases the availability
- Performance
Disadvantages:
- It can make absurd errors if data is entered incorrectly.
- Doesn’t provide a creative response in certain situations.
- It relies on rules and knowledge being correct.
Types of Expert Systems are rule-based expert systems, frame-based expert systems, fuzzy expert systems, neural expert systems, and neuro-fuzzy expert systems.
Applications include Help desks and Information management. Hospitals. Employee performance evaluation. Loan analysis. and many more.
Examples: MYCIN, DENDRAL, and others.
Difference between DSS and Expert System:
| S. No. | DSS | Expert System |
|---|---|---|
| 1. | DSS is an interactive system that enables decision-makers to solve unstructured or semi-structured problems by taking help from models and data. | An Expert System is a problem-solving computer program that excels at a particular issue domain that is difficult to solve and takes specialized knowledge and ability. |
| 2. | It facilitates decision-making. | It automates decision-making. |
| 3. | The decision environment is unstructured. | The decision environment has structure. |
| 4. | It extracts or gains knowledge from a computer system. | Inject expert knowledge into a computer system. |
| 5. | Alternatives still may not be completely understood. | Alternatives and goals are frequently predetermined. |
| 6. | Characteristics of the problem domain are complex and broad. | In this, it is limited and specialized. |
| 7. | The type of data manipulation is numeric. | The type of data manipulation is symbolic. |
| 8. | It has limited capacity. | It has a full capacity. |
| 9. | It uses goals and system data to establish alternatives and outcomes, so a good decision can be made. | The expert system can eventually replace the human decision-maker. |