Decision Support Systems Chapter 9 Decision Support Systems McGraw-Hill/Irwin Copyright © 2010 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

Learning Objectives Explain how these information systems can support the information needs of executives, managers, and business professionals Executive information systems Enterprise information portals Knowledge management systems

Learning Objectives Identify how neural networks, fuzzy logic, genetic algorithms, virtual reality, and intelligent agents can be used in business Give examples of ways expert systems can be used in business decision-making situations

Decision Support in Business Companies invest in data-driven decision support application frameworks to help them respond to Changing marketing conditions Customer needs Management information Accomplished by several types of Decision support Other information systems

Case 1: Hillman Group, Avnet, Quaker Chemical BI refers to a variety of software applications used to analyze an organization’s raw data and extract useful insights from them BI tools, coupled with business process changes, can have a significant impact on the bottom line Most companies don’t understand their business processes well enough to determine how to improve them Companies using BI to uncover flawed business processes can more successfully compete against companies using BI merely to monitor what’s happening

Case Questions What are the business benefits of BI deployments such as those implemented by Avnet and Quaker Chemical? What roles do data and business processes play in achieving those benefits? What are the main challenges to the change of mindset required to extend BI tools beyond mere reporting? What can companies do to overcome them?

Case Questions Avnet and Quaker Chemical implemented systems and processes that affect the practices of their salespeople In which ways did the latter benefit from these new implementations? How important was their buy-in to the success of these projects? Discuss alternative strategies for companies to foster adoption of new systems like these

Levels of Managerial Decision Making

Information has three dimensions Information Quality Information products are made more valuable by their attributes, characteristics, or qualities Outdated, inaccurate, or hard to understand information has much less value Information has three dimensions Time Content Form

Attributes of Information Quality

Decision Structure Structured (operational) The procedures to follow when a 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

Decision Support Systems Management Information Systems Decision Support Systems Decision support provided Provide information about the performance of the organization Provide information and techniques to analyze specific problems Information form and frequency Periodic, exception, demand, and push reports and responses Interactive inquiries and responses Information format Pre-specified, fixed format Ad hoc, flexible, and adaptable format Information processing methodology Information produced by extraction and manipulation of business data Information produced by analytical modeling of business data

Decision Support Trends Personalized decision support Modeling Information retrieval Data warehousing What-if scenarios Reporting

Decision Support Trends

Business Intelligence Applications

Decision Support Systems To support the making of semi-structured business decisions, DSS uses Analytical models Specialized databases Decision-maker’s own insights and judgments Interactive, computer-based modeling process DS systems Ad hoc, quick-response systems Initiated and controlled by decision makers

DSS Components

DSS Model Base Model Base Spreadsheet Examples A software component Consists of models used in computational and analytical routines Mathematically expresses relationships among variables Spreadsheet Examples Linear programming Multiple regression forecasting Capital budgeting present value

Applications of Statistics and Modeling Supply Chain Simulate & optimize supply chain flows, reduce inventory & stock-outs Pricing Identify the price that maximizes yield or profit Product & Service Quality Detect quality problems early in order to minimize them Research & Development Improve quality, efficacy, and safety of products and services

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, displays, and responses Satisfies needs of operational and tactical decision makers who face structured decisions

Management Reporting Alternatives Periodic Scheduled Reports Pre-specified format, issued on a regular basis Exception Reports Reports about exceptional conditions, scheduled or on event Demand Reports & Responses Information is available on demand Push Reporting Information is pushed to a networked computer

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

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

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 System (GPS) devices

Data Visualization Systems (DVS) Represents complex data using interactive, three-dimensional graphical forms (charts, graphs, maps) Helps users interactively sort, subdivide, combine, and organize data while it is in its graphical form

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

Using Decision Support Systems What-If Analysis Sensitivity Analysis Basic analytical modeling activities Goal-seeking Analysis Optimization Analysis What-If Analysis Observing how changes to selected variables affect other variables 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

Data Mining 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

Analysis of Customer Demographics

Market Basket Analysis One of the most common uses for data mining Determines what products customers purchase together with other products Typical applications of MBA Cross-selling Product placement Affinity promotion Survey analysis Fraud detection Customer behavior identification

Executive Information Systems (EIS) Combines many features of MIS and DSS Provides top executives with immediate, easy access to information Identifies factors critical to accomplishing strategic objectives So popular it was expanded to managers, analysis, and other knowledge workers

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

Web-Based Executive Information System

Enterprise Information Portals A Web-based interface and integration of MIS, DSS, EIS, and other technologies Available to all intranet users and select extranet users Provides access to a variety of internal and external business applications and services Typically tailored or personalized to the user or groups of users Often has a digital dashboard Also called enterprise knowledge portals

Enterprise Information Portal Components

Enterprise Knowledge Portal

Case 2: Goodyear, JEA, OSUMC, Monsanto Advanced technologies (AI, mathematical simulations, robotics) can have dramatic impacts on business processes and financial results Goodyear designers can perform tests 10 times faster using simulation, reducing a new tire’s time to market from two years to nine months Public Utility Company JEA uses neural network technology to automatically determine the optimal combinations of oil and natural gas the utility’s boilers need to produce electricity cost effectively, given fuel prices and the amount of electricity required The Ohio State University Medical Center replaced its overhead rail transport system with 46 self-guided robotic vehicles to move linens, meals, trash, and medical supplies throughout the 1,000-bed hospital

Case Study Questions In all of the project outcomes in the case, the payoffs are both larger and achieved more rapidly than in more traditional system implementations Why do you think this is the case? How are these projects different from others you have come across in the past? What are those differences? How do these technologies create business value for the implementing organizations? In which ways are these implementations similar in how they accomplish this, and how are they different?

Case Study Questions In all of the case examples, companies had an urgent need that prompted them to investigate radical, new technologies Do you think the story would have been different had the companies been performing well already? Why or why not? To what extent are these innovations dependent on the presence of a problem or crisis?

Artificial Intelligence (AI) Engineering Computer science AI is a field of science and technology based on… Mathematics Biology Linguistics Psychology

Artificial Intelligence (AI) Think Feel See Ultimate goal for computers Hear Talk Walk

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 Respond quickly and successfully to new situations Recognize relative importance of situation elements Handle ambiguous, incomplete, erroneous info

Domains of Artificial Intelligence 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 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 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

Expert Systems An Expert System (ES) Knowledge-based information system Contains knowledge about a specific, complex application area Acts as an export consultant to end users

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, recommends a course of action User interface programs communicate with the end user Explanation programs explain the reasoning process to the end user

Methods of Knowledge Representation Case-based Frame-based Object-based Case-Based Knowledge organized in the form of cases Cases are examples of past performance, occurrences, and experiences Frame-Based Knowledge organized in a hierarchy or network of frames A frame is a collection of knowledge about an entity, consisting of a complex package of data values describing its attributes Object-Based Knowledge represented as a network of objects An object is a data element that includes both data and the methods or processes that act on those data Rule-Based Knowledge represented in the form of rules and statements of fact Rules are statements that typically take the form of a premise and a conclusion (If, Then) Rule-based

Expert System Application Categories Decision Management Diagnostic/Troubleshooting Design/Configuration Selection/Classification Decision Management Loan portfolio analysis Employee performance evaluation Insurance underwriting Diagnostic/Troubleshooting Equipment calibration Help desk operations Medical diagnosis Software debugging Design/Configuration Computer option installation Manufacturability studies Communications networks Selection/Classification Material selection Delinquent account identification Information classification Suspect identification Process Monitoring/Control Machine control (including robotics) Inventory control Production monitoring Chemical testing Process Monitoring/Control

Benefits of Expert Systems Captures expertise of expert(s) 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

Limitations of Expert Systems Major limitations of expert systems Limited focus Inability to learn Maintenance problems Development and maintenance costs Can only solve specific types of problems in a limited domain of knowledge

Developing Expert Systems Suitability Criteria for Expert Systems Domain The domain or subject area of the problem is small and well-defined Expertise Solutions to the problem require the efforts of an expert Complexity Problem solving is complex, and requires logical inference processing

Developing Expert Systems Suitability Criteria for Expert Systems Structure… solution process must be able to cope with ill-structured, uncertain, missing, and conflicting data and a changing problem situation Availability… an expert exists who is articulate, cooperative, and supported by the management and end users involved in the development process

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

Knowledge Engineering A knowledge engineer Works with experts to capture the knowledge (facts and rules of thumb) they possess Builds the knowledge base, and if necessary, the rest of the expert system Performs a role similar to that of systems analysts in conventional information systems development

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 Recognizes patterns and relationships in data

Fuzzy Logic Resembles human reasoning Allows approximate values and inferences, and incomplete or ambiguous data Uses terms like “very high” instead of precise measures Allows processing of incomplete data Results in quick, approximate solutions Used in fuzzy process controllers (subway trains, elevators, cars)

Example of Fuzzy Logic Rules and Query

Genetic Algorithms Uses Darwinian, randomizing, and other mathematical functions Stimulates an evolutionary process, yielding increasingly better solutions Genetic algorithm software Especially useful for situations in which thousands of solutions are possible Being used to model a variety of scientific, technical, and business processes

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

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

Also called software robots or bots 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 make decisions and accomplish tasks in a way that fulfills the intentions of a user Also called software robots or bots

Network Navigation Agents User Interface Agents Interface Tutors Observe user computer operations, correct user mistakes, provide hints/advice on efficient software use Presentation Agents Show information in a variety of forms/media based on user preferences Network Navigation Agents Discover paths to information, provide ways to view it based on user preferences Role Playing Play what-if games and other roles to help users understand information and make better decisions

Information Management Agents Search Agents Help users find files and databases, search for information, and suggest and find new types of information products, media, resources Information Brokers Provide commercial services to discover and develop information resources that fit business or personal needs Information Filters Receive, find, filter, discard, save, forward, and notify users about products received or desired, including e-mail, voice mail, and other information media

Case 3: Harrah’s, LendingTree, DeepGreen, Cisco The promise of AI of automating decision making has been very slow to materialize The new generation AI applications Easier to create and manage Don’t require anyone to identify problems or to initiate analysis Decision-making capabilities are embedded into the normal flow of work, and are triggered without human intervention

Case 4: Harrah’s, LendingTree, DeepGreen, Cisco The new generation AI applications Sense online data or conditions, apply codified knowledge or logic and make decisions with minimal human intervention Rely on experts and managers to create and maintain rules and monitor the results Managers in charge of automated decision systems must develop processes for managing exceptions

Case Study Questions Why did some previous attempts to use artificial intelligence technologies fail? What differences between the new AI-based applications versus the old caused the authors to declare that automated decision making is finally coming of age? What types of decisions are best suited for automated decision making? Provide examples of successful applications from the companies in this case

Case Study Questions What role do humans play in automated decision making applications? What challenges face managers where automated decision-making systems are being used? What solutions are needed to meet such challenges?