Heuristic and Model Based Decisions, Simulations, and Searches - Coursework Example

Heuristic and Model Based Decisions, Simulations, and Searches al Affiliation The current dis aims to present an exploration of the subject of Decision Support Systems (DSS) Modeling. At the onset, popularized definitions of DSS were provided; prior to enumerating the types of DSS, as well as its fundamental components. In addition, major issues in modeling were identified and brief descriptive overviews of each were specifically noted, to wit: problem identification, environmental analysis, variable identification, forecasting, multiple modal use, model categories, model management, and finally, knowledge-based modeling. Also, major types of models used in DSS were presented, with more details provided for the simulation models. Table of Contents Decision Support System (DSS) 4 1.Types of DSS and Fundamental Components 5 2.Major Modeling Issues 7 2.1Problem Identification 7 2.2Environmental Analysis 7 2.3Variable Identification 7 2.4Forecasting 7 2. 5 Multiple Modal Use 8 2.5Model Categories (or Selection) 8 2.6Model Management 8 2.7Knowledge-Based Modeling 8 3.Major Types of Models Used in DSS 8 References 10 Decision Support System (DSS) Decision support system (DSS) was noted to have been originally coined by Scott-Morton in the 1970s (Sharda, Delen, & Turban, 2014). As cited, the definition popularized by Gorry and Scott-Morton (1971) for DSS is as follows: an “interactive computer-based systems, which help decision makers utilize data and models to solve structured problems” (cited in Sharda, Delen, & Turban, 2014, p. 13). Likewise, another definition of DSS which was a collaboration between Keen and Scott-Mortion (1978) was disclosed herewith: “Decision support systems couple the intellectual resources of individuals with the capabilities of the computer to improve the quality of decisions. It is a computer-based support system for management decision makers who deal with semistructured problems” (cited in Sharda, Delen, & Turban, 2014, p. 13). Both definitions emphasize the crucial components in DSS to include the system as computer-based and designed to assist decision makers through effective utilization of data, as well as models which are especially designed to solve structured or semi-structured problems. There are common attributes identified for DSS, such as: “adaptability and flexibility, high level of interactivity, ease of use, efficiency and effectiveness, complete control by decision-makers, ease of development, extendibility, support for modeling and analysis, support for data access, and standalone, integrated and Web-based” (Decision Support System (DDS), 2014, p. 1). In addition, several benefits have been attributed to the use and application of DSS. Among these benefits are: (1) facilitates the decision-making process; (2) assists in increasing core competencies and competitive advantage of the organization through enhanced ability to assume a proactive stance; (3) promotes and encourages interpersonal communication; (4) enhances advanced and continuous learning; (5) provides opportunities for new discoveries in problem solving and decision-making; and (6) assists in automating management process (Decision Support System (DDS), 2014). 1. Types of DSS and Fundamental Components In the discourse written by Power (n.d.), the author identified five (5) different types of DSS, to wit: (1) communication-driven, (2) data-driven, (3) document-driven, (4) knowledge-driven, and (5) model-driven. The relevant distinguishing features of each of the types of DSS are summarized in Table 1, on the next page. On the other hand, Druzdzel & Flynn (2002) identified three (3) fundamental components of DSS, as follows: (1) database management system (DMBS), (2) model-based management system, and (3) dialog generation and management system (DGMS). From these two different sources, it could be deduced that the disparities in categorization resulted from the evolution of DSS. As emphasized by Druzdzel & Flynn (2002), three (3) relevant endeavors were supported by DSS: framing, modeling, and problem solving. Table 1: Types of DSS DSS Types Targetted Stakeholder Purpose Mode of Deployment Examples Communication-driven Internal teams, including partners Assist in conducting meetings and for enhanced collaboration Web or Client server, groupware, video conferencing and computer-based bulletin boards Chats, instant messaging, online collaboration, net-meeting systems Data-driven Managers, staff, suppliers For queries, to solicit answers for varied purposes main frame system, client/server link, or the Web Databases with query systems Document-driven Broad base of stakeholders search web pages, locate documents with specifically identified keywords or search terms The Web, client/server link Databases with query systems, policies and procedures, product specifications, catalogs, and corporate historical documents, including minutes of meetings and correspondences Knowledge-driven Various stakeholders Provide appropriate advice to management, select products or services client/server systems, the Web, or software runnung on stand-alone PCs Model-driven Managers, decision-makers Assist in decision-making Stand-alone PCs. Client/server systems, or the Web Scheduling, decision analyses Source: Power, n.d. 2. Major Modeling Issues There have been eight (8) major modeling issues identified: problem identification, environmental analysis, variable identification, forecasting, multiple modal use, model categories (or selection), model management, and knowledge-based modeling (Chapter 5: Modeling and Analysis, n.d.). These issues are explained briefly below: 2.1 Problem Identification As the term implies, problem identification entails determining problems through the use of a specifically designed model. 2.2 Environmental Analysis Environmental analysis is one of the modeling issues that aims to explore forces in the macro external environment with the aim of finding a trend or pattern after comprehensive analysis. 2.3 Variable Identification Variable identification means “identifying the critical factors in a model and their relationships” (Chapter 5: Modeling and Analysis, n.d., p. 7). 2.4 Forecasting Forecasting is an analytical tool that aims to predict the future using variables in the model with greatest probabilities and potential levels of accuracy. Several forecasting tools assist in simulating future scenarios that assist decision makers in making responsible and viable courses of action. Some of the common forecasting methods include: extrapolation, judgment methods, moving average, exponential smoothing, time-series extrapolation, and regression and econometric models (Chapter 9: Building Model-Driven Decision Support Systems, 2001). 2. 5 Multiple Modal Use Multiple modal use is resorted to when complex problems require integration of various models. 2.5 Model Categories (or Selection) Model categories or selection approaches are considered when different types are required to solve specialized problems requiring diverse application models. 2.6 Model Management Model management entails planning, organizing, directing, and controlling various facets of an organization’s use of models. 2.7 Knowledge-Based Modeling Knowledge-based modeling delves into manners or approaches that take advantage of human knowledge in modeling (Chapter 5: Modeling and Analysis, n.d., p. 7). 3. Major Types of Models Used in DSS The major types of models used in DSS are enumerated as follows: optimization with few alternatives, optimization via an algorithm, optimization via an analytical formula, simulation, heuristics (“rules of thumb”), predictive models, and other models (Chapter 5: Modeling and Analysis, n.d.). For instance, simulation models were described as “a model that generates test conditions approximating actual or operational conditions. In a DSS context, simulation generally refers to a technique for conducting experiments with a computer-based model” (Chapter 9: Building Model-Driven Decision Support Systems, 2001, p. 179). An example of a simulation model is shown in Figure 1 below: Figure 1: Example of a Visual Simulation Model Source: Chapter 9: Building Model-Driven Decision Support Systems, 2001, p. 181 References Chapter 9: Building Model-Driven Decision Support Systems. (2001). Retrieved January 20, 2015, from dsc.ufcg.edu.br: http://www.dsc.ufcg.edu.br/~garcia/cursos/SAD/Notas/Capitulo9.pdf Decision Support System (DDS). (2014). Retrieved January 20, 2015, from Management Information System: http://www.tutorialspoint.com/management_information_system/decision_support_system.htm Druzdzel, M., & Flynn, R. (2002). Decision Support Systems. Retrieved January 20, 2015, from University of Pittsburg: http://www.pitt.edu/~druzdzel/psfiles/dss.pdf Gorry, A., & Scott-Morton, M. (1971). A Framework for Information Systems. Sloan Management Review, Vol. 13, No. 1,56-79. Keen, P., & Scott Morton, M. (1978). Decision Support Systems: An Organizational Perspective. Reading, MA: Addison-Wesley. Chapter 5: Modeling and Analysis. (n.d.). Retrieved January 20, 2015, from uic.edu: https://www.uic.edu/classes/idsc/.../model_ch5.ppt Power, D. (n.d.). Types of Decision Support Systems (DSS). Retrieved January 20, 2015, from gdrc.org: http://www.gdrc.org/decision/dss-types.html Sharda, R., Delen, D., & Turban, E. (2014). Business Intelligence and Analytics: Systems for Decision Support. Prentice Hall. Read More