Chapter 10 Decision Support Systems Mc GrawHillIrwin Copyright

Chapter 10 Decision Support Systems Mc. Graw-Hill/Irwin Copyright © 2009 by The Mc. Graw-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 10 -2

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 10 -3

Learning Objectives • Give examples of several ways expert systems can be used in business decision-making situations 10 -4

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 -5

Case 1: Hillman Group, Avnet, and Quaker Chemical • BI refers to a variety of software applications used to analyze an organization’s raw data (e. g. , sales transactions) and extract useful insights from them. • BI projects can transform business processes. BI tools, coupled with changes to business processes, can have a significant impact on the bottom line. • Major impediment to using BI that transforms business processes is that most companies don’t understand their business processes well enough to determine how to improve them. • Companies that use BI to uncover flawed business processes are in a much better position to successfully compete than those companies that use BI merely to monitor what’s happening. 10 -6

Case Questions 1. 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? 2. 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? Use examples from the case to illustrate your answer. 3. Both 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. 10 -7

Levels of Managerial Decision Making 10 -8

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 -9

Attributes of Information Quality 10 -10

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 -11

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 Prespecified, fixed format Ad hoc, flexible, and adaptable format Information produced by extraction and manipulation of business data Information produced by analytical modeling of business data Information processing methodology 10 -12

Decision Support Trends • The emerging class of applications focuses on – Personalized decision support – Modeling – Information retrieval – Data warehousing – What-if scenarios – Reporting 10 -13

Business Intelligence Applications 10 -14

Decision Support Systems • Decision support systems use the following to support the making of semistructured 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 -15

DSS Components 10 -16

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 -17

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 -18

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 -19

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 -20

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 -21

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 -22

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 -23

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 10 -24

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 -25

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 -26

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 -27

Analysis of Customer Demographics 10 -28

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 -29

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 -30

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 -31

Enterprise Information Portals • An EIP is 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 10 -32

Enterprise Information Portal Components 10 -33

Enterprise Knowledge Portal 10 -34

Case 2: Goodyear, JEA, OSUMC, and Monsanto • Advanced technologies such as AI, mathematical simulations, and robotics can have dramatic impacts on both business processes and financial results. • At Goodyear, designers can perform tests 10 times faster using simulation, reducing a new tire’s time to market from two years to as little as 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 (OSUMC) 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. 10 -35

Case Study Questions 1. Consider the outcomes of the projects discussed in the case. In all of them, 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? Provide several examples. 2. 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? Use examples from the case to support your answer. 3. In all of these examples, companies had an urgent need that prompted them to investigate these 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? 10 -36

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 -37

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 -38

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 -39

Domains of Artificial Intelligence 10 -40

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 -41

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 -42

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 -43

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 -44

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 -45

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 -46

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 -47

Components of an Expert System 10 -48

Methods of Knowledge Representation • 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 10 -49

Methods of Knowledge Representation • 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) 10 -50

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 -51

Expert System Application Categories • Design/Configuration – Computer option installation – Manufacturability studies – Communications networks • Selection/Classification – – Material selection Delinquent account identification Information classification Suspect identification • Process Monitoring/Control 10 -52

Expert System Application Categories • Process Monitoring/Control – Machine control (including robotics) – Inventory control – Production monitoring – Chemical testing 10 -53

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 -54

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 -55

Developing Expert Systems • Suitability Criteria for Expert Systems – Domain: the domain or subject area of the problem is small and well-defined – Expertise: a body of knowledge, techniques, and intuition is needed that only a few people possess – Complexity: solving the problem is a complex task that requires logical inference processing 10 -56

Developing Expert Systems • Suitability Criteria for Expert Systems – Structure: the 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 10 -57

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 10 -58

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 10 -59

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 -60

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 -61

Example of Fuzzy Logic Rules and Query 10 -62

Genetic Algorithms • Genetic algorithm software – Uses Darwinian, randomizing, and other mathematical functions – Simulates an evolutionary process, yielding increasingly better solutions to a problem – Being uses to model a variety of scientific, technical, and business processes – Especially useful for situations in which thousands of solutions are possible 10 -63

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 -64

Typical VR Applications • Current applications of virtual reality – Computer-aided design – Medical diagnostics and treatment – Scientific experimentation – Flight simulation – Product demonstrations – Employee training – Entertainment 10 -65

Intelligent Agents • A 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 call software robots or bots 10 -66

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 10 -67

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 10 -68

Case 3: Oracle Corporation and Others: Dashboards for Executives • Web-based “dashboards” – Displays critical information in graphic form – Assembled from data pulled in real time from corporate software and databases – Managers see changes almost instantaneously – Now available to smaller companies • Potential problems – Pressure on employees – Divisions in the office – Tendency to hoard information 10 -69

Case Study Questions 1. What is the attraction of dashboards to CEOs and other executives? What real business value do they provide to executives? 2. The case emphasizes that managers of small businesses and many business professionals now rely on dashboards. What business benefits do dashboards provide to this business audience? 10 -70

Case Study Questions 3. What are several reasons for criticism of the use of dashboards by executives? Do you agree with any of this criticism? 10 -71

Case 4: Harrah’s Entertainment, Lending. Tree, Deep. Green Financial, and Cisco Systems: • The promise of AI of automating decision making has been very slow to materialize. • The new generation AI applications are easier to create and manage, do not require anyone to identify the problems or to initiate the analysis, decisionmaking capabilities are embedded into the normal flow of work, and are triggered without human intervention. 10 -72

Case 4: Harrah’s Entertainment, Lending. Tree, Deep. Green Financial, and Cisco Systems: • They sense online data or conditions, apply codified knowledge or logic and make decisions with minimal human intervention. • But they rely on experts and managers to create and maintain rules and monitor the results. • Also, managers in charge of automated decision systems must develop processes for managing exceptions. 10 -73

Case Study Questions 1. Why did some previous attempts to use artificial intelligence technologies fail? What key differences of the new AIbased applications versus the old cause the authors to declare that automated decision making is finally coming of age? 2. What types of decisions are best suited for automated decision making? Provide several examples of successful applications from the companies in this case to illustrate your answer. 10 -74

Case Study Questions 3. What role do humans play in automated decision making applications? What are some of the challenges faced by managers where automated decision-making systems are being used? What solutions are needed to meet such challenges? 10 -75