1. 1.1 © 2010 Dr. Tarek Abd El-Hafeez Decision Support Systems Lecture 2 Decision Support Systems

2. 1.2 © 2010 Dr. Tarek Abd El-Hafeez Decision Support Systems DSS, may either be Model Driven DSSModel Driven DSS Data Driven DSSData Driven DSS Types of DSS

3. 1.3 © 2010 Dr. Tarek Abd El-Hafeez Decision Support Systems Model driven DSS uses following techniques  What-If analysis Attempt to check the impact of a change in the assumptions (input data) onthe proposed solution.  Goal Seek Analysis Attempt to find the value of the inputs necessary to achieve a desired level of output. It uses “backward” solution approach.

4. 1.4 © 2010 Dr. Tarek Abd El-Hafeez Decision Support Systems Data Driven DSSData Driven DSS As opposed to model driven DSS, these systems use large pools of data found in major organizational systems. They help to extract information from the large quantities of data stored. These systems rely on Data Warehouses created from Transaction Processing systems. They use following techniques for data analysis Online analytical processing (OLAP), andOnline analytical processing (OLAP), and Data miningData mining

5. 1.5 © 2010 Dr. Tarek Abd El-Hafeez Decision Support Systems Decision support software that allows the user to quickly analyze information that has been summarized into multidimensional views and hierarchies. The term online refers to the interactive querying facility provided to the user to minimize response time. It enables users to drill down into large volume of data in order to provide desired information, such as isolating the products that are more volatile from sales data. OLAP summarizes transactions into multidimensional user defined views.Decision support software that allows the user to quickly analyze information that has been summarized into multidimensional views and hierarchies. The term online refers to the interactive querying facility provided to the user to minimize response time. It enables users to drill down into large volume of data in order to provide desired information, such as isolating the products that are more volatile from sales data. OLAP summarizes transactions into multidimensional user defined views. Online Analytical Processing (OLAP)

6. 1.6 © 2010 Dr. Tarek Abd El-Hafeez Decision Support Systems Data mining is also known as Knowledge-Discovery in Databases (KDD). Put simply it is the processing of the data warehouse. It is a process of automatically searching large volumes of data for patterns. The purpose is to uncover patterns and relationships contained within the business activity and history and predict future behavior. Data mining has become an important part of customer relationship management (CRM).Data mining is also known as Knowledge-Discovery in Databases (KDD). Put simply it is the processing of the data warehouse. It is a process of automatically searching large volumes of data for patterns. The purpose is to uncover patterns and relationships contained within the business activity and history and predict future behavior. Data mining has become an important part of customer relationship management (CRM). Data Mining

7. 1.7 © 2010 Dr. Tarek Abd El-Hafeez Decision Support Systems Components of DSS There are two major components: DSS data base – is a collection of current and historical data from internal, external sources. It can be a massive data warehouse. Decision Support Software system – is the set of software tools used for data analysis. For instance Online analytical processing (OLAP) tools Data mining tools Models

8. 1.8 © 2010 Dr. Tarek Abd El-Hafeez Decision Support Systems  A data warehouse is a logical collection of information.  It is gathered from many different operational databases used to create business intelligence that supports business analysis activities and decision-making tasks.  It is primarily, a record of an enterprise's past transactional and operational information, stored in a database designed to favor efficient data analysis and reporting.  The term data warehouse generally refers to the combination of many different databases across an entire enterprise.  Data warehouses contain a wide variety of data that present a coherent picture of business conditions at a single point in time.  Data warehouses are generally batch updated at the end of the day, week or some period. Its contents are typically historical and static and may also contain numerous summaries. Data Warehouse

9. 1.9 © 2010 Dr. Tarek Abd El-Hafeez Decision Support Systems Data warehouses can become enormous with hundreds of gigabytes of transactions. As a result, subsets, known as "data marts," are often created for just one department or product line.Data warehouses can become enormous with hundreds of gigabytes of transactions. As a result, subsets, known as "data marts," are often created for just one department or product line. Data Warehouse combines databases across an entire enterprise. However, Data Marts are usually smaller and focus on a particular subject or department or product line. Following are the common techniques through which a data warehouse can be used.Data Warehouse combines databases across an entire enterprise. However, Data Marts are usually smaller and focus on a particular subject or department or product line. Following are the common techniques through which a data warehouse can be used. Data Mart

10. 1.10 © 2010 Dr. Tarek Abd El-Hafeez Decision Support Systems The data mining procedure involves following stepsThe data mining procedure involves following steps Exploration – includes data preparation which may involve filtering data and data transformations, selecting subsets of records. Exploration – includes data preparation which may involve filtering data and data transformations, selecting subsets of records. Model building and validation – involves the use of various models for predictive performance (i.e., explaining the variability in question and producing stable results across samples). Each model contains various patterns of queries used to discover new patterns and relations in the data. Model building and validation – involves the use of various models for predictive performance (i.e., explaining the variability in question and producing stable results across samples). Each model contains various patterns of queries used to discover new patterns and relations in the data. Deployment – That final stage involves using the model selected as best in the previous stage and applying it to new data in order to generate predictions or estimates of the expected outcome. Deployment – That final stage involves using the model selected as best in the previous stage and applying it to new data in order to generate predictions or estimates of the expected outcome. Architectures

11. 1.11 © 2010 Dr. Tarek Abd El-Hafeez Decision Support Systems Concept of Models Used in Decision Support System (DSS)Concept of Models Used in Decision Support System (DSS) “A model is an abstract representation that illustrates the components or relationships of a phenomenon.” Models are prepared so as to formulate ideas about the problem solutions that is allowing the managers to evaluate alternative solutions available for a problem in hand.

12. 1.12 © 2010 Dr. Tarek Abd El-Hafeez Decision Support Systems Physical ModelsPhysical Models Narrative ModelsNarrative Models Graphic ModelsGraphic Models Mathematical ModelsMathematical Models Types of Models Used in DSS

13. 1.13 © 2010 Dr. Tarek Abd El-Hafeez Decision Support Systems Physical ModelsPhysical Models Physical models are three dimensional representation of an entity (Object / Process). Physical models used in the business world include scale models of shopping centers and prototypes of new automobiles. Physical models are three dimensional representation of an entity (Object / Process). Physical models used in the business world include scale models of shopping centers and prototypes of new automobiles. The physical model serves a purpose that cannot be fulfilled by the real thing, e.g. it is much less expensive for shopping center investors and automakers to make changes in the designs of their physical models than to the final product themselves.The physical model serves a purpose that cannot be fulfilled by the real thing, e.g. it is much less expensive for shopping center investors and automakers to make changes in the designs of their physical models than to the final product themselves.

14. 1.14 © 2010 Dr. Tarek Abd El-Hafeez Decision Support Systems Narrative ModelsNarrative Models The spoken and written description of an entity as Narrative model is used daily by managers and surprisingly, these are seldom recognized as models. For instance, All business communications are narrative models

15. 1.15 © 2010 Dr. Tarek Abd El-Hafeez Decision Support Systems Graphic ModelsGraphic Models These models represent the entity in the form of graphs or pictorial presentations. It represents its entity with an abstraction of lines, symbols or shapes. Graphic models are used in business to communicate information. Many company’s annual reports to their stockholders contain colorful graphs to convey the financial condition of the firm. For Instance Bar graphs of frequently asked questions with number of times they are asked.Bar graphs of frequently asked questions with number of times they are asked.

16. 1.16 © 2010 Dr. Tarek Abd El-Hafeez Decision Support Systems Mathematical ModelsMathematical Models They represent Equations / Formulae representing relationship between two or more factors related to each other in a defined manner.They represent Equations / Formulae representing relationship between two or more factors related to each other in a defined manner. Types of Mathematical Models Mathematical models can further be classified as follows, based on : Influence of time – whether the event is time dependant or related Influence of time – whether the event is time dependant or related Degree of certainty – the probabilities of occurrence of an eventDegree of certainty – the probabilities of occurrence of an event Level of optimization – the perfection in solution the model will achieve. Level of optimization – the perfection in solution the model will achieve. Hence use of right model in decision support software is critical to the proper functionality of the system.

17. 1.17 © 2010 Dr. Tarek Abd El-Hafeez Decision Support Systems Group DSS When people responsible for decision making are geographically dispersed or are not available at a place at the same time, GDSS is used for quick and efficient decision making. GDSS is characterized by being used by a group of people at the same time to support decision making. People use a common computer or network, and collaborate simultaneously.When people responsible for decision making are geographically dispersed or are not available at a place at the same time, GDSS is used for quick and efficient decision making. GDSS is characterized by being used by a group of people at the same time to support decision making. People use a common computer or network, and collaborate simultaneously. An electronic meeting system (EMS) is a type of computer software that facilitates group decision-making within an organization. The concept of EMS is quite similar to chat rooms, where both restricted or unrestricted access can be provided to a user/member.An electronic meeting system (EMS) is a type of computer software that facilitates group decision-making within an organization. The concept of EMS is quite similar to chat rooms, where both restricted or unrestricted access can be provided to a user/member.

18. 1.18 © 2010 Dr. Tarek Abd El-Hafeez Decision Support Systems DSS vs. GDSS DSS can be extended to become a GDSS through: The addition of communication capabilities The ability to vote, rank, rate etc Greater system reliability