Decision Support Systems Concepts, Methodologies, and Technologies: An Overview

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 BI systems • Describe DSS characteristics and capabilities • Understand the essential definition of DSS • Understand important DSS classifications • Understand DSS components and how they integrate

3. Learning Objectives • Describe the components and structure of each DSS component • Explain Internet impacts on DSS (and vice versa) • Explain the unique role of the user in DSS versus management information systems • Describe DSS hardware and software platforms • Become familiar with a DSS development language • Understand current DSS issues

4. Opening Vignette: “Decision Support System Cures for Health Care” • Company background • Problem • Proposed solution • Results • Answer and discuss the case questions

5. Opening Vignette: “Decision Support System Cures for Health Care” - Projected Vacancy Rate versus Desired Vacancy Rate

6. Opening Vignette: - Projected Vacancy Rate vs. Desired Vacancy Rate "What-if" scenario with 6 additional RN recruiters

7. Opening Vignette: - Demanded Hours versus Total Actual Hours versus Total Actual Hours with New Hires

8. DSS Configurations • Many configurations exist; based on • management-decision situation • specific technologies used for support • DSS have three basic components • Data • Model • User interface • (+ optional) Knowledge

9. DSS Configurations • Each component • has several variations; are typically deployed online • Managed by a commercial of custom software • Typical types: • Model-oriented DSS • Data-oriented DSS

10. DSS Description • A DSS is typically built to support the solution of a certain problem (or to evaluate a specific opportunity). This is a key difference between DSS and BI applications • BI systems monitor situations and identify problems and/or opportunities, using variety of analytic methods • The user generally must identify whether a particular situation warrants attention • Reporting/data warehouse plays a major role in BI • DSS often has its own database and models

11. DSS Description • DSS is an approach (or methodology) for supporting decision making • uses an interactive, flexible, adaptable computer-based information system (CBIS) • developed (by end user) for supporting the solution to a specific nonstructured management problem • uses data, model and knowledge along with a friendly (often graphical; Web-based) user interface • incorporate the decision maker's own insights • supports all phases of decision making • can be used by a single user or by many people

12. A Web-Based DSS Architecture Multi-tiered architecture uses a web browser to run the programs on an application server. The server access data to construct one or more models. Data may also be provided by a data server that optionally extract data from a data warehouse. When the user requires that the model be optimized, the model , populated with the data, is transferred to an optimization server. The optimization server accesses additional data from data server, if needed, solves problem, and provides the solution directly to the user’s web browser.

13. DSS Characteristics and Capabilities Both DSS and BI include analytical tools to run the systems • Business analytics implies the use of models and data to improve an organization's performance and/or competitive posture. OLAP and Data Mining system have embadded in them. • Web analytics implies using business analytics on real-time Web information to assist in decision making; often related to e-Commerce. Eg. CRM, SCM ect. • Predictive analytics describes the business analytics method of forecasting problems and opportunities rather than simply reporting them as they occur. They include advance forecasting and simulation model

14. Components of DSS Application

15. Components of DSS • Data Management Subsystem • Includes the database that contains the relevant data and its managed by Database management system (DBMS) • Can be connected to a data warehouse • Model Management Subsystem • Model base management system (MBMS) : is software package that includes financial, statistical, management science, or other quantitative models that provide the system’s analytical capabilities and appropriate software management.

16. Components of DSS • User Interface Subsystem • The user communicate with and commands the DSS through the user interface subsystem. The web browser provides a familiar, consistent graphical user interface(GUI) structure for DSS • Knowledgebase Management Subsystem • KMS can support any of the other subsystems or act as an independent component.

17. How the DSS Components Integrate • The four components can be connected to a corporate internet, to an extranet, or to the internet.

18. DSS ComponentsData Management Subsystem • The data management subsystem is composed of the following elements • DSS database • DBMS • Data directory • Query facility

19. Database Management SubsystemKey Data Issues • DSS Database : A DB is a collection of interrelated data, organized to meet the needs and structure of an organization that can be used by more than one person for more application. There are several possible configuration for a database.

20. DSS ComponentsData Management Subsystem Figure showing Data are extracted from internal and External data sources, as well as from personal data belonging to one or more users. The extraction results go to the specific application’s DB or to the corporate data warehouse.

21. Database Management Subsystem • DBMS : A DB is created , accessed, and update by a DBMS. • The Data Directory : The Data Directory is a catalog of all the data in a DB. It contains data definition, and its main function is to answer questions about the availability of data items, their sources, and their exact meaning. It also supports the addition of new entries, deletion of entries, and retrieval of information about specific objects. • Query Facility : It accepts requests , and determines how the request can be filled, formulates the detailed request, and returns the results to the issuer of the request. E.g. SQL

22. Database Management SubsystemKey Data Issues • Data quality • “Garbage in/garbage out" (GIGO) • Data integration • “Creating a single version of the truth” • Scalability • Large DB present major scalability problems, part of the problems can be solved by carefully spiliting data up and having them span multiple disk drives, perhaps each access by several processor. • Data Security • Data can protected by unauthorized user. i.e SQL guard

23. DSS ComponentsModel Management Subsystem • Model base • MBMS • Modeling language • Model directory • Model execution, integration, and command processor

24. DSS ComponentsModel Management Subsystem • Model base : A Model base contains routine and special statistical, financial, forecasting, management science, and other quantitative models that provides the analysis capabilities in a DSS. It can invoke, run, change, combine, and inspect. The model in the model base divided into four categories : • Strategic models : Support top managers’ strategic plan. • Tactical models : Used by the mid managers to assist in allocating and controlling the organization’s recourses. • Operational models: Support day-to-day working activities. • Analytic models : Used to perform analysis on data through statistical, management science, data mining etc.

25. DSS Components Model Management Subsystem • MBMS software has four (4) 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 • Model directory : It is catalog of all the models and other software in the model base. • Model execution, integration and command are usually controlled by model management.

26. DSS ComponentsUser Interface (Dialog) Subsystem User Interface covers all aspects of communication between a user and the DSS or any MSS. It includes not only the hardware and software but also factors that deal with ease of use, accessibility, and human-machine interactions. It’s the user’s standpoint because it is the only point of the system that the user sees. A difficult user interface is one of the major reasons managers do not use computers and quantitative analyses as they could. The web browser has been recognize as an effective DSS GUI because it is flexible, user friendly, and gateway to almost all sources of necessary information and data.

27. DSS ComponentsUser Interface (Dialog) Subsystem • Interface • Application interface • User Interface • Graphical User Interface (GUI) • DSS User Interface • Portal • Graphical icons • Dashboard • Color coding • Interfacing with PDAs, cell phones, etc.

28. DSS Components Knowledgebase Management System • Incorporation of intelligence and expertise • Knowledge components: • Expert systems, • Knowledge management systems, • Neural networks, • Intelligent agents, • Fuzzy logic, • Case-based reasoning systems, and so on • Often used to better manage the other DSS components

29. DSS ComponentsFuture/current DSS Developments • Hardware enhancements • Smaller, faster, cheaper, … • Software/hardware advancements • data warehousing, data mining, OLAP, Web technologies, integration and dissemination technologies (XML, Web services, SOA, grid computing, cloud computing, …) • Integration of AI -> smart systems

30. DSS User • One faced with a decision that an MSS is designed to support • Manager, decision maker, problem solver, … • The users differ greatly from each other • Different organizational positions they occupy; cognitive preferences/abilities; the ways of arriving at a decision (i.e., decision styles) • User = Individual versus Group • Managers versus Staff Specialists [staff assistants, expert tool users, business (system) analysts, facilitators (in a GSS)]

31. DSS Hardware • Typically, MSS run on standard hardware • Can be composed of mainframe computers with legacy DBMS, workstations, personal computers, or client/server systems • Nowadays, usually implemented as a distributed/integrated, loosely-coupled Web based systems • Can be acquired from • A single vendor • Many vendors (best-of-breed)

32. A DSS Modeling LanguagePlanners Lab (plannerslab.com) • Generating Assumptions

33. A DSS Modeling LanguagePlanners Lab (plannerslab.com) • Creating a new model

34. A DSS Modeling LanguagePlanners Lab (plannerslab.com)

35. A DSS Modeling LanguagePlanners Lab (plannerslab.com)

36. A DSS Modeling LanguagePlanners Lab (plannerslab.com)

37. A DSS Modeling LanguagePlanners Lab (plannerslab.com)

38. A DSS Modeling LanguagePlanners Lab (plannerslab.com)

39. A DSS Modeling LanguagePlanners Lab (plannerslab.com)

40. A DSS Modeling LanguagePlanners Lab (plannerslab.com)

41. A DSS Modeling LanguagePlanners Lab (plannerslab.com)

42. A DSS Modeling LanguagePlanners Lab (plannerslab.com)

43. A DSS Modeling LanguagePlanners Lab (plannerslab.com)

44. A DSS Modeling LanguagePlanners Lab (plannerslab.com)

45. A DSS Modeling LanguagePlanners Lab (plannerslab.com)

46. End of the Chapter • Questions / Comments…