McGraw-Hill/Irwin

Chapter 10

Supporting Decision Making

Copyright © 2011 by The McGraw-Hill Companies, Inc. All rights reserved.

Learning Objectives • 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

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Learning Objectives • 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

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Learning Objectives • Give examples of several ways expert systems can be used in business decision making situations

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Decision Support in Business • Provide responses to: – Changing market conditions – Customer needs • Several types of systems – Management information – Decision support – Other information systems

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RWC 1: Fact-Based Decision Making • Decisions based on facts beat decisions based on gut • Dashboard – Makes detailed statistics available in real-time • Scorecard – Software compares details to defined metrics • How prepared are organizations to synthesize and share key performance indicators?

• How prepared are executives to draw insight from information?

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Levels of Managerial Decision Making

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Attributes of Information Quality

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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

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Decision Support Systems

Decision support provided Management Information Systems

Provide information about the performance of the organization

Information form and frequency

Periodic, exception, demand, and push reports and responses

Information format Information processing methodology

Prespecified, fixed format Information produced by extraction and manipulation of business data

Decision Support Systems

Provide information and techniques to analyze specific problems Interactive inquiries and responses Ad hoc, flexible, and adaptable format Information produced by analytical modeling of business data

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Decision Support Trends Add info from new paragraphs

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Business Intelligence Applications

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DSS Components

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Management Information Systems

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Online Analytical Processing

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GIS and DVS Systems

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Using Decision Support Systems

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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

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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

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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

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Enterprise Information Portal Components

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Enterprise Knowledge Portal

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RWC 2: Shopping in Virtual Stores • Benefits of virtual stores – Help understand customer behavior – Test products faster, more convenient and precise – Win shelf space – Focus on ways to get customers’ attention – Avoids tipping off competitors – Cuts testing time – Avoid displays that clash with store decor • Environment of virtual shopping – Change variables with each test

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Attributes of Intelligent Behavior

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Domains of Artificial Intelligence

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Components of an Expert System

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Methods of Knowledge Representation • Case-Based – Examples from the past • Frame-Based – Collection of knowledge about an entity • Object-Based – Data elements include both data and the methods or processes that act on those data • Rule-Based – Factual statements in the form of a premise and a conclusion (If, Then)

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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

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Expert System Application Categories • Design/Configuration • Selection/Classification • Process Monitoring/Control

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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

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Development Tool • Expert System Shell – The easiest way to develop an expert system – A software package consisting of an expert system without its knowledge base – Has an inference engine and user interface programs

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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

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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

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Example of Fuzzy Logic Rules and Query

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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

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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

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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

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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

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RWC 3: Driving Competitive Advantage • Advanced technologies impact businesses – Goodyear reduced time to market – Public Utility Company JEA determines optimal combinations of oil and natural gas – The Ohio State University Medical Center (OSUMC) uses robots to move supplies

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RWC 4: Business Intelligence Deployments • Analyze raw data (e.g., sales transactions) • Extract useful insights • Can transform business processes • Can impact the bottom line • Major impediment - most companies don’t understand their business processes well enough • Uncovering flawed business processes beats merely to monitoring

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