Supporting Decision Making Chapter 10 McGraw-Hill/IrwinCopyright © 2011 by The McGraw-Hill Companies, Inc. All rights reserved.
10-2 Identify the changes taking place in the form and use of decision support in business Identify the role and reporting alternatives of management information systems Describe how online analytical processing can meet key information needs of managers Explain the decision support system concept and how it differs from traditional management information systems Learning Objectives
10-3 Explain how the following information systems can support the information needs of executives, managers, and business professionals –Executive information systems –Enterprise information portals –Knowledge management systems Identify how neural networks, fuzzy logic, genetic algorithms, virtual reality, and intelligent agents can be used in business Learning Objectives
10-4 Give examples of several ways expert systems can be used in business decision- making situations Learning Objectives
10-5 Decision Structure Structured (operational) –Procedures can be specified in advance Unstructured (strategic) –Not possible to specify procedures in advance Semi-structured (tactical) –Decision procedures can be pre-specified, but not enough to lead to the correct decision
10-6 Data Mining Provides decision support through knowledge discovery –Analyzes vast stores of historical business data –Looks for patterns, trends, and correlations –Goal is to improve business performance Types of analysis –Regression –Decision tree –Neural network –Cluster detection –Market basket analysis
10-7 Market Basket Analysis One of the most common uses for data mining –Determines what products customers purchase together with other products Other uses –Cross Selling –Product Placement –Affinity Promotion –Survey Analysis –Fraud Detection –Analyze Customer Behavior
10-8 Executive Information Systems (EIS) Combines many features of MIS and DSS Provides immediate and easy information Identifies critical success factors Features –Customizable graphical user interfaces –Exception reports –Trend analysis –Drill down capability
10-9 Benefits of Expert Systems Captures human experience in a computer-based information system Limitations of Expert Systems Limited focus Inability to learn Maintenance problems Development cost Can only solve specific types of problems in a limited domain of knowledge
10-10 Knowledge Engineering A knowledge engineer –Works with experts to capture the knowledge they possess Facts and rules of thumb –Builds the knowledge base if necessary, the rest of the expert system –Similar role to systems analysts 10-10
10-11 Neural Networks Modeled after the brain’s mesh-like network of interconnected processing elements (neurons) –Interconnected processors operate in parallel and interact with each other –Allows the network to learn from the data it processes
10-12 Genetic Algorithms Genetic algorithm software –Uses Darwinian, randomizing, and other mathematical functions –Simulates an evolutionary process, yielding increasingly better solutions to a problem –Used to model a variety of scientific, technical, and business processes –Useful when thousands of solutions are possible
10-13 Virtual Reality (VR) Virtual reality is a computer-simulated reality –Fast-growing area of artificial intelligence –Originated from efforts to build natural, realistic, multi-sensory human-computer interfaces –Relies on multi-sensory input/output devices –Creates a three-dimensional world through sight, sound, and touch Telepresence –Using VR to perform a task in a different location
10-14 Intelligent Agents Software surrogate for an end user or a process that fulfills a stated need or activity –Uses built-in and learned knowledge base to accomplish tasks Software robots or bots
10-15 Types of Intelligent Agents User Interface Agents –Interface Tutors –Presentation Agents –Network Navigation Agents –Role-Playing Agents Information Management Agents –Search Agents –Information Brokers –Information Filters