Course Content
DSS – Introduction, Attributes, Characteristics, Benefits
Decision Support System (DSS): Concept and Philosophy, DSS: Deterministic Systems, Artificial intelligence (AI) System, Knowledge based expert system (KBES), MIS & the role of DSS, Transaction Processing System (TPS), Enterprise Management System (EMS), Enterprise Resource Planning (ERP) System, Benefits of ERP, EMS & ERP
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Decision Support System
Attributes of a DSS
Characteristics of a DSS
Benefits of DSS
Classification of DSS
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Classification of DSS
Types – Decision Support System
Decision support systems can be broken down into categories, each based on their primary sources of information.
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Data-driven DSS
Model-driven DSS
Communication-driven and group DSS
Knowledge-driven DSS
Document-driven DSS
Example – Decision support system
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GPS Routing
ERP dashboard
(CDSS)Clinical Decision Support System
DSS-Deterministic System
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DSS-Deterministic System
Difference between MIS and DSS
Artificial Intelligence (AI) System
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Introduction Artificial Intelligence (AI) System
Narrow or Weak AI
General or Strong AI
More Category based on functionality
Knowledge Based Expert System (KBES)
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Introduction: Knowledge Based Expert System (KBES)
Features of Knowledge Based Expert System
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Features of Knowledge Based Expert System
Components of Knowledge Based Expert System
Transaction Processing System (TPS)
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Introduction – Transaction Processing System
Key characteristics of Transaction Processing Systems
Examples of Transaction Processing Systems
Features of TPS
Enterprise Management System (EMS)
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Enterprise Management System (EMS)
Features of (EMS)
Enterprise Resource Planning (ERP) System
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What is ERP?
Key features and components of an ERP system
Management Information System
Data-driven DSS

Data-driven DSS

A data-driven DSS is a computer program that makes decisions based on data from internal databases or external databases. Typically, a data-driven DSS uses data mining techniques to discern trends and patterns, enabling it to predict future events.

Businesses often use data-driven DSS to help make decisions about inventory, sales and other business processes. Some are used to help make decisions in the public sector, such as predicting the likelihood of future criminal behavior.

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