1. DECISION SUPPORT SYSTEMS CONCEPTS, METHODOLOGIES, AND TECHNOLOGIES: AN OVERVIEW

2. Learning Objectives  Understand possible decision support system (DSS) configurations  Understand the key differences and similarities between DSS and business intelligence (BI) systems  Describe DSS characteristics and capabilities  Understand the essential definition of DSS  Understand DSS components and how they integrate

3. Learning Objectives  Describe the components and structure of each DSS component: the data management subsystem, the model management subsystem, the user interface (dialog) subsystem, the knowledge-based management subsystem, and the user  Explain Internet impacts on DSS and vice versa

4. Learning Objectives  Explain the unique role of the user in DSS versus management information systems (MIS)  Describe DSS hardware and software platforms  Understand important DSS classifications  Become familiar with some DSS application areas and applications  Understand important, current DSS issues

5. DSS Configurations  Decision support can be provided in many different configurations 。  These configurations depend on the nature of the management-decision situation and the specific technologies used for support

6. DSS Configurations  These technologies are assembled from four basic components (each with several variations and are typically deployed over the Web)  Data  Models  User interface  Knowledge (optional)

7. DSS Description  DSS application A DSS program built for a specific purpose (e.g., a scheduling system for a specific company)  Business intelligence (BI) A conceptual framework for decision support. It combines architecture, databases (or data warehouses), analytical tools, and applications

8. DSS Description  A DSS is an approach(or methodology)for support decision making, it also supports all phases of decision making and may include a knowledge component  A DSS can be used by a single user on a PC or can be Web-based for use by many people at several locations

9. DSS Characteristics and Capabilities Decision Support And Business Intelligence System /turban 著 /2005 年

10. DSS Description Decision Support And Business Intelligence System /turban 著 /2005 年

11. DSS Characteristics and Capabilities  Business analytics The application of models directly to business data. Business analytics involves using DSS tools, especially models, in assisting decision makers. It is essentially OLAP/DSS. See also business intelligence (BI).

12. DSS Characteristics and Capabilities  Predictive analytics A business analytical approach toward forecasting (e.g., demand, problems, opportunities) that is used instead of simply reporting data as they occur

13. DSS Characteristics and Capabilities  The key characteristics and capabilities of DSS  Support for decision makers, mainly in semistructured and unstructured situations, by bringing together human judgment and computerized information  Support for all managerial levels, ranging from top executives to line managers  Support for individuals as well as groups ， DSS support virtual teams through collaborative web tools

14. DSS Characteristics and Capabilities  Support for interdependent and/or sequential decisions  Support in all phases of the decision-making process  Support for a variety of decision-making processes and styles  DSS are flexible, so users can add, delete, combine, change, or rearrange basic elements; DSS can be readily modified to solve other, similar problems

15. DSS Characteristics and Capabilities  User-friendliness, strong graphical capabilities, and a natural language interactive human–machine interface can greatly increase the effectiveness of DSS  Improved effectiveness of decision making  The decision maker has complete control over all steps of the decision-making process in solving a problem  End users are able to develop and modify simple systems by themselves

16. DSS Characteristics and Capabilities  Models are generally utilized to analyze decision-making situations  Access is provided to a variety of data sources, formats, and types  Can be employed as a standalone tool used by an individual decision maker in one location or distributed throughout an organization and in several organizations along the supply chain  Can be integrated with other DSS and/or applications, and it can be distributed internally and externally, using networking and Web technologies

17. Components of DSS Decision Support And Business Intelligence System /turban 著 /2005 年

18. Components of DSS  Database management system (DBMS) Software for establishing, updating, and querying (e.g., managing) a database  Data warehouse A physical repository where relational data are organized to provide clean, enterprise-wide data in a standardized format  Database The organizing of files into related units that are then viewed as a single storage concept. The data in the database are generally made available to a wide range of users

19. Components of DSS  Model management subsystem  Model base management system (MBMS) Software for establishing, updating, combining, and so on (e.g., managing) a DSS model base  User interface The component of a computer system that allows bidirectional communication between the system and its user

20. Components of DSS  Knowledge-based management subsystem  The knowledge-based management subsystem can support any of the other subsystems or act as an independent component  Organizational knowledge base An organization’s knowledge repository,knowledge may be provided via web server, many artificial intelligence methods have been implemented in web develop system such as JAVA 。

21. Data Management Subsystem  The data management subsystem is composed of”  DSS database  DBMS  Data directory  Query facility

22. Data Management Subsystem Decision Support And Business Intelligence System /turban 著 /2005 年

23.  The Database  Internal data come mainly from the organization’s transaction processing system  External data include industry data, market research data, census data, regional employment data, government regulations, tax rate schedules, and national economic data  Private data can include guidelines used by specific decision makers and assessments of specific data and/or situations Data Management Subsystem

24.  Data organization In large organizations that use extensive amounts of data, such as WAL-MART, AT&T and American Airlines data warehouse and used when needed 。  Data extraction The process of capturing data from several sources, synthesizing them, summarizing them, determining which of them are relevant, and organizing them, resulting in their 。 effective integration

25. Data Management Subsystem  Database management system (DBMS)  Software for establishing, updating, and querying (e.g., managing) a database  Query Facility The (database) mechanism that accepts requests for data, accesses them, manipulates them, and queries them  Directory A catalog of all the data in a database or all the models in a model base

26. Data Management Subsystem  Key database and database management system issues  Data quality A key issue in data management ， and if your data management is poor the data cannot be trusted, and therefore neither can any analysis based on them 。

27. Data Management Subsystem  Data integration Data and information are all over the place in most organization 。 When it comes time to develop any enterprise system ， or even a single DSS ， data must be gathered from disparate sources and integrated into that single version of the truth 。  Scalability Key issues and important new directions discussed include the internet as the main driving force for applications especially across the enterprise

28. Data Management Subsystem - Data security one key issue that DBMS is supposed to handle by its very nature is data security

29. The Model Management Subsystem Decision Support And Business Intelligence System /turban 著 /2005 年

30.  Model base A collection of preprogrammed quantitative models (e.g., special statistical, financial, optimization, forecasting, management science and other quantitative models that provide the analysis capabilities) organized as a single unit 。 The Model Management Subsystem

31.  Four categories of models with the model base  Strategic models  Tactical models  Operational models  Analytical models The Model Management Subsystem

32.  Strategic models It is used to support top manager’s strategic planning responsibilities 。 Models that represent problems for the strategic level (i.e., executive level) of management  Tactical models It is used mainly by middle managers to assist in allocating and controlling the organization’s resources 。 Models that represent problems for the tactical level (i.e., midlevel) of management 。 The Model Management Subsystem

33.  Operational models It is used to support the day-to-day working activities of the organization 。 Models that represent problems for the operational level of management  Analytical models It is used to perform analysis on data 。 Mathematical models into which data are loaded for analysis The Model Management Subsystem

34.  Model building blocks and routines  Model building blocks Preprogrammed software elements that can be used to build computerized models. For example, a random-number generator can be employed in the construction of a simulation model  Model components for building DSS At higher level than building blocks, it is important to consider the different types of models and solution methods needed in the DSS 。  Modeling tools It is often necessary to customize models, using programming tools and languages 。 The Model Management Subsystem

35.  Model base management system: MBMS software has four main functions  Model creation, using programming languages, DSS tools and/or subroutines, and other building blocks  Generation of new routines and reports  Model updating and changing  Model data manipulation The Model Management Subsystem

36.  Model directory  Model execution is the process of controlling the actual running of the model  Model integration involves combining the operations of several models when needed  A model command processor is used to accept and interpret modeling instructions from the user interface component and route them to the MBMS, model execution, or integration functions The Model Management Subsystem

37. User Interface (Dialog) Subsystem  User interface The component of a computer system that allows bidirectional communication between the system and its user.  User interface management system (UIMS) The DSS component that handles all interaction between users and the system

38. User Interface (Dialog) Subsystem  The user interface process  Object A person, place, or thing about which information is collected, processed, or stored  Graphical user interface (GUI) An interactive, user-friendly interface in which, by using icons and similar objects, the user can control communication with a computer

39. User Interface (Dialog) Subsystem Decision Support And Business Intelligence System /turban 著 /2005 年

40. User Interface (Dialog) Subsystem  DSS user interfaces access is provided through Web browsers including:  Voice input and output  Portable devices  Direct sensing devices

41. User Interface (Dialog) Subsystem  DSS developments  Parallel processing hardware and software technologies have made major inroads in solving the scalability issue  Web-based DSS have made it easier and less costly to make decision-relevant information and model-driven DSS available to users in geographically distributed locations, especially through mobile devices

42. User Interface (Dialog) Subsystem  DSS developments  Parallel processing hardware and software technologies have made major inroads in solving the scalability issue  Web-based DSS have made it easier and less costly to make decision-relevant information and model-driven DSS available to users in geographically distributed locations, especially through mobile devices

43. User Interface (Dialog) Subsystem  DSS developments  Artificial intelligence continues to make inroads in improving DSS  Faster, intelligent search engines  Intelligent agents promise to improve the interface in areas such as direct natural language processing and creating facial gestures  The development of ready-made (or near-ready-made) DSS solutions for specific market segments has been increasing

44. User Interface (Dialog) Subsystem  DSS developments  DSS is becoming more embedded in or linked to most EIS  GSS improvements support collaboration at the enterprise level  Different types of DSS components are being integrated more frequently

45. Knowledge-Based Management Subsystem  Advanced DSS are equipped with a component called a knowledge-based management subsystem that can supply the required expertise for solving some aspects of the problem and provide knowledge that can enhance the operation of other DSS components

46. The User  The person faced with a decision that an MSS is designed to support is called the user, the manager, or the decision maker  MSS has two broad classes of users: managers and staff specialists  Staff specialists use the system much more frequently than manager and tend to be more detail-oriented  Staff analysts are often intermediaries between managers and the MSS

47. The User  Intermediary A person who uses a computer to fulfill requests made by other people (e.g., a financial analyst who uses a computer to answer questions for top management)  Staff assistant An individual who acts as an assistant to a manager

48. The User  Expert tool user A person who is skilled in the application of one or more types of specialized problem-solving tools  Business (system) analysts An individual whose job is to analyze business processes and the support they receive (or need) from information technology  Facilitators (in a GSS) A person who plans, organizes, and electronically controls a group in a collaborative computing environment

49. DSS Hardware  Hardware affects the functionality and usability of the MSS  The choice of hardware can be made before, during, or after the design of the MSS software  Major hardware options:  Organization’s servers  Mainframe computers with legacy DBMS,  Workstations  Personal computers  Client/server systems

50. DSS Hardware  Portability has become critical for deploying decision-making capability in the field, especially for salespersons and technicians  The power and capabilities of the World Wide Web have a dramatic impact on DSS  Communication and collaboration  Download DSS software  Use DSS applications provided by the company  Buy online from application service providers (ASPs)

51. DSS Classifications  AIS SIGDSS classification for DSS  Communications-driven and group DSS (GSS)  Data-driven DSS  Document-driven DSS  Knowledge-driven DSS, data mining, and management ES applications  Model-driven DSS  Compound DSS

52. DSS Classifications  Holsapple and Whinston’s classification  Text-oriented DSS  Database-oriented DSS  Spreadsheet-oriented DSS  Solver-oriented DSS  Rule-oriented DSS

53. DSS Classifications  Alter’s output classification  Data  File drawer systems  Data analysis systems  Data or models  Analysis information systems  Models  Accounting models  Representational models  Optimization models  Suggestion models

54. DSS Classifications  Other DSS categories  Institutional DSS A DSS that is a permanent fixture in an organization and has continuing financial support. It deals with decisions of a recurring nature  Ad hoc DSS A DSS that deals with specific problems that are usually neither anticipated nor recurring

55. DSS Classifications  Other DSS categories  Personal support  Group support  Organizational support  Group support system (GSS) Information systems, specifically DSS, that support the collaborative work of groups  Custom-made systems versus ready-made systems