Decision support system-MIS

Management Information System - DSS

1. DECISION SUPPORT SYSTEM C A Yogaraja, Assistant Profesor, CSE K L University.

2. Introduction Decision support systems (DSS) are interactive software-based systems intended to help managers in decision-making by accessing large volumes of information generated from various related information systems involved in organizational business processes DSS uses the summary information, exceptions, patterns, and trends using the analytical models A decision support system helps in decision-making but does not necessarily give a decision itself The decision makers compile useful information from raw data, documents, personal knowledge, and/or business models to identify and solve problems and make decisions

3. DSS Architecture

4. DSS Architecture Architecture of DSS has four components Data Management User Interface Model Management Knowledge Management

5. DSS Architecture Data Management Data management subsystem includes a database contains relevant data for the situation and is managed by the software called DBMS. The data management subsystem may be interconnected with corporate data warehouse, a repository for corporate relevant decision making data. The data management subsystem is composed of DSS Database DBMS Data Directory Query facility

6. DSS Architecture Model Management Includes software with financial, statistical, management science, or other quantitative models that provide the system’s analytical capabilities and appropriate software management Modelling languages for building custom models are also included Often called as Model Base Management System (MBMS) The model management subsystem includes Model base MBMS Modelling language Model Directory Model execution, integration and command Processor

7. DSS Architecture User Interface Enables the users to communicate with and command the DSS Knowledge Management Provides knowledge for solution of the problem; supports any of the other subsystems or act as an independent component

8. Scope of DSS Enterprise wide DSS Linked to large data warehouse and serves many managers in the company. Desktop DSS Single use DSS is a small system that runs on a individual manager’s PC.

9. Characteristics Rapid Access to Information Some DSS provide fast and Continuous access to information Gauges on the dashboard of a vehicle Handle large amount of Data from multiple sources Advanced DBMS and Data warehouses have allowed decision makers to search for information with a DSS even when some data resides in different databases on different computer systems or networks. Provide Report and Presentation flexibility Managers can get the information as they want, presented in a format that suits their needs, even output can be displayed on computer screens or printed depending on their needs.

10. Characteristics Offer both textual and graphical orientation DSS’s can produce Text, tables, charts and more to get a better understanding of results Support drill down analysis A manager can get more level of detail when they needed by drilling down the data Perform complex, Sophisticated Analysis and Comparisons using advanced software Packages Marketing research surveys

11. Capabilities of DSS Using Decision Support System four basic types of analytical modeling activities are involved What-if analysis: An end user makes the change to variables or relationship among variables, and observe the resulting changes in the values. Example: what if we cut advertising by 10%? What would happen to sales? Goal Oriented: Process of determining the input values required to achieve a certain goal Example: House buyers determine the monthly payment they can afford and calculate the number of such payments required to pay the desired house.

12. Capabilities of DSS Risk Analysis: It is the important factor that affects the business enterprise. DSS allows the managers to assess the risks associated with various alternatives. Decisions can be then classified as low risk, medium risk, high risk. Model Building: DSS allows decision makers to identify the most appropriate model for solving the problems. It takes into account input variables, inter relationship among the variables and constraints. Example: A marketing manager of a TV manufacturing company is charged with the responsibility of developing a sales forecasting model for color Televisions Graphical Analysis: This helps the managers to quickly digest the large volumes of data and visualize the impacts of various course of action .

13. Components of a Decision Support System Like any other software system, DSS also has components and phases of development. No matter what kind of decision support system you’re looking to develop, you must plan around these four components: Input: What kind of input does it require to carry out the analysis? As mentioned earlier, it can be rule, problem, spreadsheet, text or database oriented. User Knowledge/Expertise: Whether inputs will require manual analysis by the user or not Output: Should the outcomes be comparative or generic? Decisions: Whether it should be a suggestion support system? Or you just want it to analyze the data and outcome of different actions?

14. Classification of DSS Data driven DSS These DSS has file drawer systems, data analysis systems, analysis information systems, data warehousing and emphasizes access to and manipulation of large databases of structured data Model driven emphasizes access to and manipulation of a statistical, financial, optimization, or simulation model Model-driven DSS use data and parameters provided by users to assist decision makers in analyzing a situation These systems usually are not data intensive and consequently are not linked to very large databases.

15. Classification of DSS Knowledge driven These systems provide recommendation and/or suggestion schemes which aids the user in selecting an appropriate alternative to a problem at hand. provides specialized problem-solving expertise stored as facts, rules, procedures, or in similar structures Knowledge driven DSS are often referred to as management expert systems or intelligent decision support systems.

16. Classification of DSS Document Driven These systems help managers retrieve and manage unstructured documents and web pages by integrating a variety of storage and processing technologies to provide complete document retrieval and analysis. It also access documents such as company policies and procedures, product specification, catalogs, corporate historical documents, minutes of meetings, important correspondence, corporate records, etc. and are usually driven by a task-specific search engine.

17. Classification of DSS Communication driven This breed of DSS is often called group decision support systems (GDSS). They are a special type of hybrid DSS that emphasizes the use of communications and decision models intended to facilitate the solution of problems by decision makers working together as a group. GDSS supports electronic communication, scheduling, document sharing and other group productivity and decision enhancing activities and involves technologies such as two-way interactive video, bulletin boards, e-mail, etc.

18. Classification of DSS Inter and Intra Organization DSS These systems are driven by the rapid growth of Internet and other networking technologies such as broadband WAN’s, LAN’s, WIP, etc. Inter-organization DSS are used to serve companies stakeholders (customers, suppliers, etc.), whereas intra-organization DSS are more directed towards individuals inside the company and specific user groups .

19. Classification of DSS New breeds of DSS Hybrid Systems , which are combinations units using aspects of more than one different type of DSS. A very popular example is Web based DSS , which can be driven by a combination of different models such as document-driven, communication driven and knowledge drive. Web-based DSS are computerized systems that delivers decision support information or decision support tools to a manager or business analyst using a "thin-client" Web browser like Netscape Navigator or Internet Explorer.

20. Classification of DSS New breeds of DSS On-line Analytical Processing (OLAP) - it performs multidimensional analysis of business data and provides the capability for complex calculations, trend analysis, and sophisticated data modeling. It is the foundation for many kinds of business applications for Business Performance Management, Planning, Budgeting, Forecasting, Financial Reporting, Analysis, Simulation Models, Knowledge Discovery, and Data Warehouse Reporting.

21. Steps in Designing DSS

22. Planning - Why Build the System?

23. Analysis- Who, What, When, Where?

24. Design- How will the system work?

25. Implementation- System Delivery?