Presentation on theme: "Decision Support Systems"— Presentation transcript:
1 Decision Support Systems Chapter 3: Decision Support Systems Concepts, Methodologies and Technologies: An Overview
2 Learning ObjectivesUnderstand possible decision support systems(DSS) configurations.Understand the key differences and similarities between DSS and BI systems.Describe DSS characteristics & capabilities.Understand the essential definitions of DSS.Understand DSS components and how they integrate.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 system and the user.
3 Learning Objectives Explain internet impact on DSS and vice versa. 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.
4 DSS ConfigurationsDepends on the management-decision situation and the specific technologies used for support.Technologies are typically deployed over the web and are assembled from:DataModelsUser InterfaceKnowledge (optional)Components are emphasized by the support provided (i.e. Model-Oriented DSS -> Model (spreadsheets), Data-Oriented DSS -> Database).
5 DSS Description BI DSS Monitor Situations. Identify problems and/or opportunities using analytical methods.User must identify wither a particular situation warrants attention and then analytical methods can be applied.Support the solution of a certain problem. Evaluate an opportunity.Utilized models and data access.Arguably considers DSS part of its internal building blocks.Utilizes models and data access, but they have their own databases that are used to solve a specific problem or set of problems(DSS Applications)Focuses on reporting and identification of problems by scanning data extracted from a data warehouses.Built to solve a specific problem and include their own databases
6 Some DSS DefinitionsSystems designed to support managerial decision-making in unstructured problems.Little (1970): Model based set of procedures for processing data and judgements to assist manager in his decision making.. Must be simple, robust, easy adaptive, completeMoore and Chang (1980): Structured problems are structured, because we treat them in that way.. DSS is an expandable system capable of supporting ad hoc data analysis and decision modeling for planning the futureBonczek (1980): A computerbased system with 3 interacting components, a language system, a knowledge system, problem processing systemKeen (1980): Final system can be developed by the adaptive process of learning and evolution by the user, the DSS builder, and the DSS itself
7 Generic DSS Description DSS is an approach (or methodology) for supporting decision making.Uses Interactive, Flexible, Adaptive CBIS developed for supporting the solution to a specific nonstructured management problem, it uses data, provides an easy user interface, and can incorporate the decision maker own insight.Includes models and is developed (possibly by end users) through an interactive and iterative process.Supports all phases of decision making and may include knowledge component.Can be used by a single user on a PC or can be Web based for use by many people in several locations.
8 DSS characteristics and capabilities There is no consensus on exactly what a DSS is, and there is obviously no agreement on the standard characteristics and capabilities of DSS.TermsBusiness Analytics (BA): implies the use of models and data to improve the organization’s performance or competitive posture. The focus is the use of models, even if they are deeply buried inside the system.Data mining and OLAP systems have models embedded in them but are still not well understood in practice.Web analytics: is an approach to using analytics tools on real-time Web information to assist in decision making .Predictive analytics: describes the business analytics methods of forecasting problems and opportunities rather that simply reporting them as they occur. It utilized advanced forecasting and simulation models.
12 Component of DSS DATA MANAGEMENT SUBSYSTEM: Includes database that contains relevant data for the situation and is managed by DBMS.Can be interconnected with the corporate data warehouse [A repository for corporate relevant decision-making data], usually, the data are stored or accessed via database Web server.MODEL MANAGEMENT SUBSYSTEM [MBMS]:Software package that includes financial, statistical, management science, or other quantitative models that provide the system’s analytical capabilities and appropriate software management.Modeling languages for building custom models are included.Often called Model Base Management System [MBMS].Can be connected to corporate or external storage of models.Model solution methods and management systems are implemented in Web development systems (such as Java) to run on application servers.
13 Component of DSS THE USER INTERFACE SUBSYSTEM User communicated with and commands the DSS through the user interface subsystem.User is considered part of the system.Researchers assert that some of the unique contributions of DSS are derived from the intensive interaction between the computer and the decision maker.Web browser provides a familiar, consistent graphical user interface (GUI) for most DSS.THE KNOWLEDGE-BASE MANAGEMENT SUBSYSTEMCan support any of the other subsystems or act as an independent component.It provides intelligence to augment the decision maker’s own.It can be interconnected with the organization's knowledge repository (part of a Knowledge management system [KMS] (The Organizational Knowledge Base)Knowledge may be provided via Web servers.By definition DSS must include the three major components DBMS, MBMS and user interface, the KBMS is optional but it can provide many benefits by providing intelligence in and to the three major components. The user may be considered a component of a DSS.
14 How DSS Component integrate Can be connected to a corporate intranet, extranet or the internet.Component communicate through web technologies.Web browsers are excellent choice for UI.
15 A Web Based DSS Architecture Web BrowserWebServerApplicationOptimization/Simulation, etc.DataWarehouseor DBMSRead about DSS & the Web mutual impact
16 DATA MANAGEMENT SUBSYSTEM: The Data management subsystem is composed of the following elements: 1- DSS database 2- DBMS 3- Data Directory 4- Query Facility
18 DATA MANAGEMENT SUBSYSTEM: DATABASE Interrelated data extracted from various sources, stored for use by the organization, and queried.Internal data, usually from TPS.External data from government agencies, trade associations, market research firms, forecasting firms.Private data or guidelines used by decision-makers.
19 DATA MANAGEMENT SUBSYSTEM: Database Management System Data OrganizationShould DSS have their own Databases.Data Extraction ETLThe process of capturing data from several sources & the integration process.
20 Data Management Subsystem Query Facility Access, manipulate and query dataAccepts requests for dataConsults the data directoryFormulates the direct requestsReports the results (on a web structured page)Ex: Search for all sales in the Southeast region during June 2006 and summarize sales by salesperson.
21 Data Management Subsystem Data Directory Catalog of all dataContains data definitionsAnswers questions about the availability of data itemsSourceMeaningAllows for additions, removals, and alterations
22 Key Database Management System Issues Data Quality: [GIGO].Data Integration: Single version of the truth.Scalability.Data Security.
23 Model Management Subsystem Components:Model baseModel base management systemModeling languageModel directoryModel execution, integration, and command processor
24 Model Management Subsystem Models (Model Base)Strategic, tactical operationalStatistical, financial, marketing,Management science,Accounting engineeringModel building blocksModel Base ManagementModeling commands: creationMaintenance: updateDatabase interfaceModeling languageDataManagementInterfaceKnowledge basedsubsystemModel DirectoryModel execution,integration andcommand processor
25 Model Management Subsystem Models in the Model Base Clasification with respect to time spanStrategic models: Supports top management decisionsTactical models: Used primarily by middle management to allocate resourcesOperational models: Supports daily activitiesAnalytical modelsUsed to perform analysis of data for strategic, tactical and operational decisionsAlso there are model building blocks and routines, likeRandom number generation, curve fitting, present value computation
26 Model Management Subsystem Model Management Activities Model executionControls running of modelModel integrationCombines several models’ operationsModel command processorReceives model instructions from user interfaceRoutes instructions to MBMS or model execution or integration functions
27 Model Management Subsystem Model Base Management System Functions:Model creationModel updatesModel data manipulationGeneration of new routines
28 Model Management Subsystem Model Directory Catalog of models and softwareDefinitionsFunctions to answer questions about availability and capability of the models
29 User Interface Management System Interacts with model, data and knowledge management subsystemsIncludes a natural language processor or standard objects (pull down menus, internet browsers)Includes GUI, frequently by web browsersAccomodates the user with a variety of input devicesProvides output with a various formats and output devices.Provides help capabilities
30 User Interface Management System Stores dataProcess multiple functions concurrentlySupport cummunication b/w users and tech. StaffProvides trainingProvides flexibility and adaptivenessCaptures, stores and analyzes the dialog usage
31 User Interface System Data management and DBMS Knowledge-based system Model management and MBMSUser Interface Management System (UIMS)Natural Language ProcessorInputActionLanguagesOutputDisplayLanguagePC DisplayPrinters, PlottersUsersBased on Figure 3.6, Schematic View of the User Interface
32 New User Interface Developments Voice/speech recognition (Ex: Clarissa developed at NASA Ames Research Team)Handwriting recognitionTranslation of text into voiceAutomatic real time natural language speech translator (on process)Displays are getting better by crisp images, holographic displays (Ex: LCD panels developed at Philips Research)
33 New User Interface Developments Tactile interfaces (Ex: Immersion Corp.’s Cyberforce Sys. includes a spandex glove that sense the doctors get when performing surgery)Videoconferencing (Mİcrososft developed RingCam, an omnidirectional videocamera to view the entire roomGesture interface that utilizes holographic displaysGesture
34 New Developments in DSS Access data from a data warehouse, use models from OLAP or data mining tools.Web technologiesLink components for accessing data and knowledge via web browsers or web like user interfacesEnable virtual teams to collaborateReduced technological barriers; made transactions easier and less costly by mobile communicationHardware shrinks in size, increases in speed etc.Faster, intelligent search engines by AI techniquesIn the future some DSS may include emotions, mood, tacit values and other soft factorstacit values
35 Knowledge-Based Management System Expert or intelligent agent system component to enhance the operation of other DSS componentsComplex problem solving in unstructured/semistructured systemsGives aid in models selection and constructionEnhances operations of other componentsA DSS that includes this optional component is called an intelligent DSS, DSS/ES, expert support system or knowledge-based DSSCaution: A KMS is a text oriented DSS; not a Knowledge-based Management System
36 DSS HardwareHardware affects the functionality and usability of DSS, De facto standardMajor hardware options: mainframe, server, workstation, PC, client/server systemDistributed DSS runs on different networks including internet, intranet, extranetAccess by client pc’s or by mobile devices notebook pc’s, PDA’s, cell phones
37 DSS HardwareModels run either on the server, mainframe, any exernal system or client pcWeb server with DBMS:Operates using browserData stored in variety of databasesCan be mainframe, server, workstation, or PCAny network typeAccess for mobile devices
38 DSS ClassificationsAssociation for Information Systems Special Interest Group In DSS [AIS SIGDSS]Communications-driven and group DSSData-driven DSSDocument-driven DSS, data mining, and management ES applicationsModel-driven DSSHolsapple and WhinstonText oriented, database oriented, spreadsheet oriented, solver oriented, rule oriented, or compound
39 DSS Classifications Alter Extent to which outputs can directly support or determine the decisionData oriented or model oriented
41 DSS Classifications Donovan and Madnick Ad hoc Hackathorn and Keen InstitutionalProblems of recurring natureAd hocProblems that are not anticipated or are not repetitiveHackathorn and KeenPersonal support, group support, or organizational support
42 DSS Classifications GSS v. Individual DSS Decisions made by entire group or by one decision makerCustom made v. vendor ready madeGeneric DSS may be modified for useDatabase, models, interface, support are built inAddresses repeatable industry problemsReduces costs
43 Web and DSS Data collection Communications Collaborations Download capabilitiesRun on Web serversSimplifies integration problemsIncreased usability features