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Explain the decision support system concept and how it differs from traditional management information systems. 8 Learning Objectives (continued) Slide 4: 4 Explain how the following information systems can support the information needs of executives, managers, and business professionals: Executive information systems Enterprise information portals Enterprise knowledge portals 8 Learning Objectives (continued) Slide 5: 5 Identify how neural networks, fuzzy logic, genetic algorithms, virtual reality, and intelligent agents can be used in business. How can expert systems be used in business decision-making situations? 8 Learning Objectives (continued) Slide 6: 6 Decision Support in Business 8 Section I Slide 7: 7 To succeed, companies need information systems that can support the diverse information and decision-making needs of their managers and business professionals. 8 Business and Decision Support Slide 8: 8 Information, Decisions, & Management The type of information required by decision makers is directly related to the level of management and the amount of structure in the decision situations. 8 Business and Decision Support (continued) Slide 9: 9 8 Business and Decision Support (continued) Slide 10: 10 Information Quality Timeliness Provided WHEN it is needed Up-to-date when it is provided Provided as often as needed Provided about past, present, and future time periods as necessary 8 Business and Decision Support (continued) Slide 11: 11 Information Quality (continued) Content Free from errors Should be related to the information needs of a specific recipient for a specific situation Provide all the information that is needed Only the information that is needed should be provided Can have a broad or narrow scope, or an internal or external focus Can reveal performance 8 Business and Decision Support (continued) Slide 12: 12 Information Quality (continued) Form Provided in a form that is easy to understand Can be provided in detail or summary form Can be arranged in a predetermined sequence Can be presented in narrative, numeric, graphic, or other forms Can be provided in hard copy, video, or other media. 8 Business and Decision Support (continued) Slide 13: 13 8 Business and Decision Support (continued) Slide 14: 14 Decision Structure Structured decisions Involve situations where the procedures to be followed can be specified in advance Unstructured decisions Involve situations where it is not possible to specify most of the decision procedures in advance 8 Business and Decision Support (continued) Slide 15: 15 Decision structure (continued) Semistructured decisions Some decision procedures can be specified in advance, but not enough to lead to a definite recommended decision 8 Business and Decision Support (continued) Slide 16: 16 Amount of structure is typically tied to management level Operational – more structured Tactical – more semistructured Strategic – more unstructured 8 Business and Decision Support (continued) Slide 17: 17 The growth of corporate intranets, extranets and the Web has accelerated the development and use of “executive class” information delivery & decision support software tools to virtually every level of the organization. 8 Decision Support Trends Slide 18: 18 The original type of information system Produces many of the products that support day-to-day decision-making These information products typically take the following forms: Periodic scheduled reports Exception reports Demand reports and responses Push reports 8 Management Information Systems Slide 19: 19 Management reporting alternatives Periodic scheduled reports Prespecified format Provided on a scheduled basis Exception reports Produced only when exceptional conditions occur Reduces information overload 8 Management Information Systems (continued) Slide 20: 20 Management reporting alternatives (continued) Demand reports and responses Available when demanded. Ad hoc Push reports Information is sent to a networked PC over the corporate intranet. Not specifically requested by the recipient 8 Management Information Systems (continued) Slide 21: 21 Enables managers and analysts to interactively examine & manipulate large amounts of detailed and consolidated data from many perspectives Analyze complex relationships to discover patterns, trends, and exception conditions Real-time 8 Online Analytical Processing Slide 22: 22 Involves.. Consolidation The aggregation of data. From simple roll-ups to complex groupings of interrelated data Drill-Down Display detail data that comprise consolidated data 8 Online Analytical Processing (continued) Slide 23: 23 Slicing and Dicing The ability to look at the database from different viewpoints. When performed along a time axis, helps analyze trends and find patterns 8 Online Analytical Processing (continued) Slide 24: 24 Computer-based information systems that provide interactive information support during the decision-making process DSS’s use Analytical models Specialized databases The decision maker’s insights & judgments An interactive, computer-based modeling process to support making semistructured and unstructured business decisions 8 Decision Support Systems Slide 25: 25 Designed to be ad hoc, quick-response systems that are initiated and controlled by the decision maker DSS Models and Software Rely on model bases as well as databases Might include models and analytical techniques used to express complex relationships 8 Decision Support Systems (continued) Slide 26: 26 DSS models and software (continued) Can combine model components to create integrated models in support of specific types of business decisions 8 Decision Support Systems (continued) Slide 27: 27 Geographic Information & Data Visualization Systems Special categories of DSS that integrate computer graphics with other DSS features GIS A DSS that uses geographic databases to construct and display maps and other graphics displays Decision Support Systems (continued) Slide 28: 28 Geographic information and data visualization systems (continued) Data visualization systems Represent complex data using interactive three-dimensional graphic forms Helps discover patterns, links, and anomalies 8 Decision Support Systems (continued) Slide 29: 29 An interactive modeling process Four types of analytical modeling What-if analysis Sensitivity analysis Goal-seeking analysis Optimization analysis 8 Using Decision Support Systems Slide 30: 30 What-If Analysis End user makes changes to variables, or relationships among variables, and observes the resulting changes in the values of other variables 8 Using Decision Support Systems (continued) Slide 31: 31 Sensitivity Analysis A special case of what-if analysis The value of only one variable is changed repeatedly, and the resulting changes on other variables are observed Typically used when there is uncertainty about the assumptions made in estimating the value of certain key variables 8 Using Decision Support Systems (continued) Slide 32: 32 Goal-Seeking Analysis Instead of observing how changes in a variable affect other variables, goal-seeking sets a target value (a goal) for a variable, then repeatedly changes other variables until the target value is achieved 8 Using Decision Support Systems (continued) Slide 33: 33 Optimization Analysis A more complex extension of goal-seeking The goal is to find the optimum value for one or more target variables, given certain constraints 8 Using Decision Support Systems (continued) Slide 34: 34 Data Mining for Decision Support Software analyzes vast amounts of data Attempts to discover patterns, trends, & correlations May perform regression, decision tree, neural network, cluster detection, or market basket analysis 8 Using Decision Support Systems (continued) Slide 35: 35 EIS’s combine many of the features of MIS and DSS Originally intended to provide top executives with immediate, easy access to information about the firm’s “critical success factors” Alternative names Enterprise information systems Executive support systems 8 Executive Information Systems Slide 36: 36 Features of an EIS Information presented in forms tailored to the preferences of the users Most stress use of graphical user interface and graphics displays May also include exception reporting and trend analysis 8 Executive Information Systems (continued) Slide 37: 37 A Web-based interface and integration of intranet and other technologies that gives all intranet users and selected extranet users access to a variety of internal & external business applications and services 8 Enterprise Portals and Decision Support Slide 38: 38 Business benefits More specific and selective information Easy access to key corporate intranet website resources Industry and business news Access to company data for stakeholders Less time spent on unproductive surfing 8 Enterprise Portals and Decision Support (continued) Slide 39: 39 IT that helps gather, organize, and share business knowledge within an organization Hypermedia databases that store and disseminate business knowledge. May also be called knowledge bases Best practices, policies, business solutions Entered through the enterprise knowledge portal 8 Knowledge Management Systems Slide 40: 40 Artificial Intelligence Technologies in Business 8 Section II Slide 41: 41 “Designed to leverage the capabilities of humans rather than replace them,…AI technology enables an extraordinary array of applications that forge new connections among people, computers, knowledge, and the physical world.” 8 Business and AI Slide 42: 42 A field of science and technology based on disciplines such as computer science, biology, psychology, linguistics, mathematics, & engineering Goal is to develop computers that can think, see, hear, walk, talk, and feel Major thrust – development of computer functions normally associated with human intelligence – reasoning, learning, problem solving 8 Artificial Intelligence Slide 43: 43 Domains of AI Three major areas Cognitive science Robotics Natural interfaces 8 Artificial Intelligence (continued) Slide 44: 44 Cognitive science Focuses on researching how the human brain works & how humans think and learn Applications Expert systems Adaptive learning systems Fuzzy logic systems Neural networks Intelligent agents 8 Artificial Intelligence (continued) Slide 45: 45 Robotics Produces robot machines with computer intelligence and computer controlled, humanlike physical capabilities Natural interfaces Natural language and speech recognition Talking to a computer and having it understand Virtual reality 8 Artificial Intelligence (continued) Slide 46: 46 Computing systems modeled after the brain’s mesh like network of interconnected processing elements, called neurons Goal – the neural network learns from data it processes 8 Neural Networks Slide 47: 47 A method of reasoning that resembles human reasoning Allows for approximate values and inferences Allows for incomplete or ambiguous data Allows “fuzzy” systems to process incomplete data and provide approximate, but acceptable, solutions to problems 8 Fuzzy Logic Systems Slide 48: 48 Uses Darwinian, randomizing, & other mathematical functions to simulate an evolutionary process that can yield increasingly better solutions Especially useful for situations in which thousands of solutions are possible & must be evaluated 8 Genetic Algorithms Slide 49: 49 Computer-simulated reality Relies on multi-sensory input/output devices Allows interaction with computer-simulated objects, entities, and environments in three dimensions 8 Virtual Reality Slide 50: 50 A “software surrogate” for an end user or a process that fulfills a stated need or activity Uses built-in and learned knowledge base about a person or process to make decisions and accomplish tasks 8 Intelligent Agents Slide 51: 51 A knowledge-based information system that uses its knowledge about a specific, complex application area to act as an expert consultant Provides answers to questions in a very specific problem area Must be able to explain reasoning process and conclusions to the user 8 Expert Systems Slide 52: 52 Components Knowledge base Software resources Knowledge base Contains Facts about a specific subject area Heuristics that express the reasoning procedures of an expert on the subject 8 Expert Systems (continued) Slide 53: 53 Software Resources Contains an inference engine and other programs for refining knowledge and communicating Inference engine processes the knowledge, and makes associations and inferences User interface programs, including an explanation program, allows communication with user 8 Expert Systems (continued) Slide 54: 54 Begin with an expert system shell Add the knowledge base Built by a “knowledge engineer” Works with experts to capture their knowledge Works with domain experts to build the expert system 8 Developing Expert Systems Slide 55: 55 8 The Value of Expert Systems Slide 56: 56 Benefits Can outperform a single human expert in many problem situations Helps preserve and reproduce knowledge of experts Limitations Limited focus, inability to learn, maintenance problems, developmental costs 8 The Value of Expert Systems (continued) Slide 57: 57 Is the form and use of information and decision support in e-business changing and expanding? Has the growth of self-directed teams to manage work in organizations changed the need for strategic, tactical, and operational decision making in business? 8 Discussion Questions Slide 58: 58 What is the difference between the ability of a manager to retrieve information instantly on demand using an MIS and the capabilities provided by a DSS? In what ways does using an electronic spreadsheet package provide you with the capabilities of a decision support system? 8 Discussion Questions (continued) Slide 59: 59 Are enterprise information portals making executive information systems unnecessary? Can computers think? Will they EVER be able to? 8 Discussion Questions (continued) Slide 60: 60 What are some of the most important applications of AI in business? What are some of the limitations or dangers you see in the use of AI technologies such as expert systems, virtual reality, and intelligent agents? What could be done to minimize such effects? 8 Discussion Questions (continued) References : 61 References James A. O'Brien; George M. Marakas. Management Information Systems: Managing Information Technology in the Business Enterprise 6th Ed., Boston: McGraw-Hill/ Irwin,2004 8 You do not have the permission to view this presentation. In order to view it, please contact the author of the presentation.