McGraw-Hill/Irwin Copyright © 2008, The McGraw-Hill Companies, Inc. All rights reserved.McGraw-Hill/Irwin Copyright © 2008 The McGraw-Hill Companies, Inc. All rights reserved.

McGraw-Hill/Irwin Copyright © 2008, The McGraw-Hill Companies, Inc. All rights reserved.McGraw-Hill/Irwin Copyright © 2008 The McGraw-Hill Companies, Inc. All rights reserved. Decision Support Systems Chapter 10

10-3 Decision Support in Business Companies are investing in data-driven decision support application frameworks to help them respond to Changing market conditions Customer needs This is accomplished by several types of Management information Decision support Other information systems

10-4 Levels of Managerial Decision Making

10-5 Information Quality Information products made more valuable by their attributes, characteristics, or qualities Information that is outdated, inaccurate, or hard to understand has much less value Information has three dimensions Time Content Form

10-6 Attributes of Information Quality

10-7 Decision Structure Structured (operational) The procedures to follow when decision is needed can be specified in advance Unstructured (strategic) It is not possible to specify in advance most of the decision procedures to follow Semi-structured (tactical) Decision procedures can be pre-specified, but not enough to lead to the correct decision

10-8 Decision Support Trends The emerging class of applications focuses on Personalized decision support Modeling Information retrieval Data warehousing What-if scenarios Reporting

10-9 Business Intelligence Applications

10-10 Decision Support Systems Decision support systems use the following to support the making of semi-structured business decisions Analytical models Specialized databases A decision-maker’s own insights and judgments An interactive, computer-based modeling process DSS systems are designed to be ad hoc, quick-response systems that are initiated and controlled by decision makers

10-11 DSS Model Base Model Base A software component that consists of models used in computational and analytical routines that mathematically express relations among variables Spreadsheet Examples Linear programming Multiple regression forecasting Capital budgeting present value

10-12 Applications of Statistics and Modeling Supply Chain: simulate and optimize supply chain flows, reduce inventory, reduce stock-outs Pricing: identify the price that maximizes yield or profit Product and Service Quality: detect quality problems early in order to minimize them Research and Development: improve quality, efficacy, and safety of products and services

10-13 Management Information Systems The original type of information system that supported managerial decision making Produces information products that support many day-to-day decision-making needs Produces reports, display, and responses Satisfies needs of operational and tactical decision makers who face structured decisions

10-14 Management Reporting Alternatives Periodic Scheduled Reports Prespecified format on a regular basis Exception Reports Reports about exceptional conditions May be produced regularly or when an exception occurs Demand Reports and Responses Information is available on demand Push Reporting Information is pushed to a networked computer

10-15 Online Analytical Processing OLAP Enables managers and analysts to examine and manipulate large amounts of detailed and consolidated data from many perspectives Done interactively, in real time, with rapid response to queries

10-16 Online Analytical Operations Consolidation Aggregation of data Example: data about sales offices rolled up to the district level Drill-Down Display underlying detail data Example: sales figures by individual product Slicing and Dicing Viewing database from different viewpoints Often performed along a time axis

10-17 Geographic Information Systems GIS DSS uses geographic databases to construct and display maps and other graphic displays Supports decisions affecting the geographic distribution of people and other resources Often used with Global Positioning Systems (GPS) devices

10-18 Using Decision Support Systems Using a decision support system involves an interactive analytical modeling process Decision makers are not demanding pre-specified information They are exploring possible alternatives What-If Analysis Observing how changes to selected variables affect other variables

10-19 Using Decision Support Systems Sensitivity Analysis Observing how repeated changes to a single variable affect other variables Goal-seeking Analysis Making repeated changes to selected variables until a chosen variable reaches a target value Optimization Analysis Finding an optimum value for selected variables, given certain constraints

10-20 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

10-21 Analysis of Customer Demographics

10-22 Market Basket Analysis One of the most common uses for data mining Determines what products customers purchase together with other products Results affect how companies Market products Place merchandise in the store Lay out catalogs and order forms Determine what new products to offer Customize solicitation phone calls

10-23 Executive Information Systems EIS Combines many features of MIS and DSS Provide top executives with immediate and easy access to information Identify factors that are critical to accomplishing strategic objectives (critical success factors) So popular that it has been expanded to managers, analysis, and other knowledge workers

10-24 Features of an EIS Information presented in forms tailored to the preferences of the executives using the system Customizable graphical user interfaces Exception reports Trend analysis Drill down capability

10-25 Dashboard Example

10-26 Artificial Intelligence (AI) AI is a field of science and technology based on Computer science Biology Psychology Linguistics Mathematics Engineering The goal is to develop computers than can simulate the ability to think And see, hear, walk, talk, and feel as well

10-27 Attributes of Intelligent Behavior Some of the attributes of intelligent behavior Think and reason Use reason to solve problems Learn or understand from experience Acquire and apply knowledge Exhibit creativity and imagination Deal with complex or perplexing situations

10-28 Attributes of Intelligent Behavior Attributes of intelligent behavior (continued) Respond quickly and successfully to new situations Recognize the relative importance of elements in a situation Handle ambiguous, incomplete, or erroneous information

10-29 Domains of Artificial Intelligence

10-30 Cognitive Science Applications in the cognitive science of AI Expert systems Knowledge-based systems Adaptive learning systems Fuzzy logic systems Neural networks Genetic algorithm software Intelligent agents Focuses on how the human brain works and how humans think and learn

10-31 Robotics AI, engineering, and physiology are the basic disciplines of robotics Produces robot machines with computer intelligence and humanlike physical capabilities This area include applications designed to give robots the powers of Sight or visual perception Touch Dexterity Locomotion Navigation

10-32 Natural Interfaces Major thrusts in the area of AI and the development of natural interfaces Natural languages Speech recognition Virtual reality Involves research and development in Linguistics Psychology Computer science Other disciplines

10-33 Latest Commercial Applications of AI Decision Support Helps capture the why as well as the what of engineered design and decision making Information Retrieval Distills tidal waves of information into simple presentations Natural language technology Database mining

10-34 Latest Commercial Applications of AI Virtual Reality X-ray-like vision enabled by enhanced-reality visualization helps surgeons Automated animation and haptic interfaces allow users to interact with virtual objects Robotics Machine-vision inspections systems Cutting-edge robotics systems From micro robots and hands and legs, to cognitive and trainable modular vision systems

10-35 Expert Systems An Expert System (ES) A knowledge-based information system Contain knowledge about a specific, complex application area Acts as an expert consultant to end users

10-36 Components of an Expert System Knowledge Base Facts about a specific subject area Heuristics that express the reasoning procedures of an expert (rules of thumb) Software Resources An inference engine processes the knowledge and recommends a course of action User interface programs communicate with the end user Explanation programs explain the reasoning process to the end user

10-37 Expert System Application Categories Decision Management Loan portfolio analysis Employee performance evaluation Insurance underwriting Diagnostic/Troubleshooting Equipment calibration Help desk operations Medical diagnosis Software debugging

10-38 Benefits of Expert Systems Captures the expertise of an expert or group of experts in a computer-based information system Faster and more consistent than an expert Can contain knowledge of multiple experts Does not get tired or distracted Cannot be overworked or stressed Helps preserve and reproduce the knowledge of human experts

10-39 Limitations of Expert Systems The major 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

10-40 Neural Networks Computing systems 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

10-41 Fuzzy Logic Fuzzy logic Resembles human reasoning Allows for approximate values and inferences and incomplete or ambiguous data Uses terms such as “very high” instead of precise measures Used more often in Japan than in the U.S. Used in fuzzy process controllers used in subway trains, elevators, and cars

10-42 Example of Fuzzy Logic Rules and Query

10-43 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 Also called telepresence

10-44 Typical VR Applications Current applications of virtual reality Computer-aided design Medical diagnostics and treatment Scientific experimentation Flight simulation Product demonstrations Employee training Entertainment