Unit Three                    Streaming Business                        OperationsMcGraw-Hill/Irwin      © 2008 The McGraw...
Unit Three• Chapter Nine – Enabling the Organization –  Decision Making• Chapter Ten – Extending the Organization –  Suppl...
Unit Three• Decision-enabling, problem-solving, and  opportunity-seizing systems                                          ...
Chapter 9                    Enabling the Organization                       – Decision MakingMcGraw-Hill/Irwin         © ...
Learning Outcomes9.1 Define the systems organizations use to    make decisions and gain competitive    advantages9.2 Descr...
Learning Outcomes9.4 List and describe four types of artificial    intelligence systems9.5 Describe three types of data-mi...
Decision Making• Reasons for the growth of decision-making  information systems  – People need to analyze large amounts of...
Decision Making• Model – a simplified representation or  abstraction of reality• IT systems in an enterprise              ...
Transaction Processing Systems• Moving up through the organizational pyramid users move  from requiring transactional info...
Transaction Processing Systems•   Transaction processing system - the basic business    system that serves the operational...
Decision Support Systems•   Decision support system (DSS) – models information to    support managers and business profess...
Decision Support Systems• What-if analysis                               9-12
Decision Support Systems• Goal-seeking analysis                               9-13
Decision Support Systems• Interaction between a TPS and a DSS                                        9-14
Executive Information Systems •   Executive information system (EIS) – a     specialized DSS that supports senior level   ...
Executive Information Systems• Interaction between a TPS and an EIS                                         9-16
Executive Information Systems• Digital dashboard – integrates information  from multiple components and presents it in  a ...
Artificial Intelligence (AI)• Intelligent system – various commercial  applications of artificial intelligence• Artificial...
Artificial Intelligence (AI)• The ultimate goal of AI is the ability to build a  system that can mimic human intelligence ...
Artificial Intelligence (AI)•   Four most common categories of AI include:    1. Expert system – computerized advisory    ...
Artificial Intelligence (AI)•   Four most common categories of AI include:    3. Genetic algorithm – an artificial intelli...
Data Mining•   Data-mining software includes many forms of    AI such as neural networks and expert    systems            ...
Data Mining•   Common forms of data-mining analysis    capabilities include:    – Cluster analysis    – Association detect...
Cluster Analysis•   Cluster analysis – a technique used to divide    an information set into mutually exclusive    groups ...
Association Detection•   Association detection – reveals the    degree to which variables are related    and the nature an...
Statistical Analysis•   Statistical analysis – performs such    functions as information correlations,    distributions, c...
OPENING CASE STUDY QUESTIONS             Second Life  1. How could companies use Second Life     to enhance decision makin...
OPENING CASE STUDY QUESTIONS             Second Life  3. How could a company such as Nike use     decision support systems...
CHAPTER NINE CASE    DARPA Grand Challenge•   The DARPA Grand Challenge was designed    to leverage American ingenuity to ...
Chapter Nine Case Questions1. Describe how the DoD is using AI to   improve its operations and save lives2. Explain why th...
Chapter Nine Case Questions3. Describe how autonomous vehicles could be used   by organizations around the world to improv...
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  • Decision making and problem solving encompass large-scale, opportunity-oriented, strategically focused solutions Organizations today can no longer use a “cook book” approach to decision making This Unit focuses on technology to help make decisions, solve problems, and find new innovative opportunities Decision support systems Executive information systems Artificial intelligence (AI) Data mining Customer relationship management Supply chain management Enterprise resource planning
  • CLASSROOM OPENER GREAT BUSINESS DECISIONS – Walt Disney Decides to Call His Mouse Cartoon Character Mickey, not Mortimer Sunday, November 18, 1928, is a historic moment in time since it is the day that the premier of Steamboat Willie debuted, a cinematic epic of seven minutes in length. This was the first cartoon that synchronized sound and action. Like all great inventions, Mickey Mouse began his life in a garage. After going bankrupt with the failure of his Laugh O Gram Company, Walt Disney decided to rent a camera, assemble an animation stand, and set up a studio in his uncle’s garage. At the age of 21, Walt and his older brother Roy launched the Disney Company in 1923. The company had a rocky start. Its first film, Alice , hardly made enough money to keep the company in business. His second film, Oswald the Rabbit , was released in 1927 with small fanfare. Then Disney’s luck changed and in 1928 he released his seven minute film about a small mouse named Mickey. Disney never looked back. The truth is Mickey Mouse began life as Mortimer Mouse. Walt Disney’s wife, Lilly, did not like the name and suggested Mickey instead. Walt Disney has often been heard to say “I hope we never lose sight of one fact – that this was all started by a mouse.” Would Mortimer have been as successful as Mickey? Would Mortimer have been more successful than Mickey? How could Walt Disney have used technology to help support his all-important decision to name his primary character? There are many new technologies helping to drive decision support systems, however it is important to note that some decisions, such as the name of a mouse, are made by the most complex decision support system available, the human brain.
  • 9.1 Define the systems organizations use to make decisions and gain competitive advantages Decision support system (DSS) – models information to support managers and business professionals during the decision-making process Executive information system (EIS) – a specialized DSS that supports senior level executives within the organization Artificial intelligence (AI) – simulates human intelligence such as the ability to reason and learn Data mining – typically includes many forms of AI such as neural networks and expert systems. Data mining tools apply algorithms to information sets to uncover inherent trends and patterns in the information 9.2 Describe the three quantitative models typically used by decision support systems Sensitivity analysis – the study of the impact that changes in one (or more) parts of the model have on other parts of the model What-if analysis – checks the impact of a change in an assumption on the proposed solution Goal-seeking analysis – finds the inputs necessary to achieve a goal 9.3 Describe the relationship between digital dashboards and executive information systems An executive information system (EIS) is a specialized DSS that supports senior level executives within the organization A digital dashboard integrates information from multiple components and present it in a unified display A digital dashboard is a form of EIS
  • 9.4 List and describe four types of artificial intelligence systems The three most common categories of AI include: Expert systems – computerized advisory programs that imitate the reasoning processes of experts in solving difficult problems Neural Networks – attempts to emulate the way the human brain works Intelligent agents – special-purposed knowledge-based information system that accomplishes specific tasks on behalf of its user 9.5 Describe three types of data-mining analysis capabilities Cluster analysis – a technique used to divide an information set into mutually exclusive groups such that the members of each group are as close together as possible to one another and the different groups are as far apart as possible Association detection – reveals the degree to which variables are related and the nature and frequency of these relationships in the information Statistical analysis – performs such functions as information correlations, distributions, calculations, and variance analysis
  • What is the value of information? The answer to this important question varies depending on how the information is used Ask your students why two people looking at the exact same pieces of information could extract completely different value from the information Ans: One way that people can extract different value from similar information is by the information technology tools they use to analyze the information Also, people’s personal experience and expertise will determine how they view and analyze information Reasons for growth of decision-making information systems People need to analyze large amounts of information —Improvements in technology itself, innovations in communication, and globalization have resulted in a dramatic increase in the alternatives and dimensions people need to consider when making a decision or appraising an opportunity. People must make decisions quickly —Time is of the essence and people simply do not have time to sift through all the information manually. People must apply sophisticated analysis techniques, such as modeling and forecasting, to make good decisions —Information systems substantially reduce the time required to perform these sophisticated analysis techniques. People must protect the corporate asset of organizational information — Information systems offer the security required to ensure organizational information remains safe.
  • Models can calculate risks, understand uncertainty, change variables, and manipulate time Ask your students if any of them have ever worked with a DSS, EIS, or AI system Ans: Many of your students have worked with a DSS and might not know it. Excel is a DSS. You can use many of the tools found in Excel, such as Scenario Manager, Goal Seek, Solver, and Pivot Tables to support DSS activities Decision support system (DSS) – models information to support managers and business professionals during the decision-making process Executive information system (EIS) – a specialized DSS that supports senior level executives within the organization Artificial intelligence (AI) – simulates human intelligence such as the ability to reason and learn Data mining – typically includes many forms of AI such as neural networks and expert systems. Data mining tools apply algorithms to information sets to uncover inherent trends and patterns in the information.
  • The structure of a typical organization is similar to a pyramid Organizational activities occur at different levels of the pyramid People in the organization have unique information needs and thus require various sets of IT tools (see Figure) At the lower levels of the pyramid, people perform daily tasks such as processing transactions Moving up through the organizational pyramid, people (typically managers) deal less with the details (“finer” information) and more with meaningful aggre­gations of information (“coarser” information) that help them make broader decisions for the organization Granularity refers to the extent of detail in the information (means fine and detailed or “coarse” and abstract information)
  • Analysts typically use TPS to perform their daily tasks What types of TPS are used at your college? Payroll system (Tracking hourly employees) Accounts Payable system Accounts Receivable system Course registration system Human resources systems (tracking vacation, sick days)
  • In a DSS, data is first queried and collected from the knowledge database Results from the query are then checked and analyzed against decision models Once checked against the decision models, the results are then generated for review to find a “best” solution for the situation One national insurance company uses DSSs to analyze the amount of risk the company is undertaking when it insures drivers who have a history of driving under the influence of alcohol. The DSS discovered that only 3 percent of married male homeowners in their forties received more than one DUI. The company decided to lower rates for customers falling into this category, which increased its revenue while mitigating its risk. CLASSROOM EXERCISE DSSs All Around Break your students into groups and ask them to compare sensitivity analysis, what-if analysis, and goal-seeking analysis and to provide a business example of when they would use each type? Sensitivity analysis – studies the impact on a single change in a current model. For example – if we continually change the amount of inventory we carry, how low can our inventories go before issues start occurring in other parts of the supply chain? This would require changing the inventory level and watching the model to see “how sensitive” it is to inventory levels. What-if analysis – determines the impact of change on an assumption or an input. For example – if the economic condition improves, how will it affect our sales? Goal-seeking analysis – solves for a desired goal. For example – we want to improve revenues by 30 percent, how much does sales have to increase and costs have to decrease to meet this goal?
  • This figure displays Excel being used as a DSS to determine “what-if” analysis by using Excel’s Scenario Manager to determine what will happen to total sales as the price and quantity of units sold changes If your students are interested in learning more about Excel DSS tools, such as Scenario Manager, have them review the Excel technology plug-ins
  • This figure displays Excel being used as a DSS to determine “goal-seeking” by using Excel’s Goal Seek tool to determine how much money a person can borrow with an interest rate of 5.5% and a monthly payment of $1,300 If your students are interested in learning more about Excel DSS tools, such as Goal Seek and Solver, have them review the Excel technology plug-ins Goal Seek and Solver offer similar functionality Solver is more advanced than Goal Seek as Solver allows the user to enter in many additional constraints on the end result
  • The TPS supplies transaction-based data to the DSS The DSS summarizes and aggregates the information from the many different TPS systems, which assists managers in making informed decisions. Burlington Northern and Santa Fe Railroad (BNSF) regularly tests its railroad tracks Each year hundreds of train derailments result from defective tracks Using a DSS to schedule train track replacements helped BNSF decrease its rail-caused derailments by 33 percent
  • Can you name a few different situations when you would use consolidation, drill-down, and slice-and-dice? Consolidation would occur when grouping multiple store sales together to get a total for the company Drill-down would occur when digging into the numbers on the balance sheet or income statement, such as revenues broken down into individual product revenues for each store during different dates and times Slice-and-dice would occur when users begin looking at information with different dimensions, similar to the cubes of information
  • Why would you need interaction between a TPS and EIS? The EIS needs information from the TPS to help executives make decisions Without knowing order information, inventory information, and shipping information from the TPSs, it would be very difficult for the CEO to make strategic decisions for the organization
  • As digital dashboards become easier to use, more executives can perform their own analysis without inundating IT personnel with queries and request for reports Why, according to Nucleus Research, is there a direct correlation between use of digital dashboards and a company’s return on investment (ROI)? Digital dashboards, whether basic or comprehensive, deliver results quickly The quicker employees have information, the quicker they can respond to problems, threats, and opportunities
  • RivalWatch offers a strategic business information service using AI that enables organizations to track the product offerings, pricing policies, and promotions of online competitors Clients can determine the competitors they want to watch and the specific information they wish to gather, ranging from products added, removed, or out of stock to price changes, coupons offered, and special shipping terms RivalWatch allows its clients to check each competitor, category, and product either daily, weekly, monthly, or quarterly
  • Photo one represents an AI Robot at Manchester Airport in England The Hefner AI Robot Cleaner alerts passengers to security and nonsmoking rules while it scrubs up to 65,600 square feet of floor per day Photo two displays a SmartPump that keeps drivers in their cars on cold, wet days The SmartPump can service any automobile built after 1987 that has been fitted with a special gas cap and a windshield-mounted transponder that tells the robot where to insert the pump Photo three displays the Miami Police Bomb squad’s AI robot that is used to locate and deactivate bombs Highlight the security and safety that can be gained through the use of AI robots for your class
  • Expert systems Human expertise is transferred to the expert system, and users can access the expert system for specific advice Most expert systems contain information from many human experts and can therefore perform a better analysis than any single human Ask your students how expert systems could be used in the medical field Neural networks Neural networks are most useful for decisions that involve patterns or image recognition Typically used in the finance industry to discover credit card fraud by analyzing individual spending behavior
  • Genetic algorithms Essentially an optimizing system, it finds the combination of inputs that give the best outputs Intelligent agents Used for environmental scanning and competitive intelligence An intelligent agent can learn the types of competitor information users want to track, continuously scan the Web for it, and alert users when a significant event occurs RivalWatch uses intelligent agents
  • Data-mining tools apply algorithms to information sets to uncover inherent trends and patterns in the information Analysts use this information to develop new business strategies and business solutions Ask your students to identify an organization that would “not” benefit from investing in data warehousing and data-mining tools Ans: None CLASSROOM EXERCISE Analyzing Multiple Dimensions of Information Jump! is a company that specializes in making sports equipment, primarily basketballs, footballs, and soccer balls. The company currently sells to four primary distributors and buys all of its raw materials and manufacturing materials from a single vendor. Break your students into groups and ask them to develop a single cube of information that would give the company the greatest insight into its business (or business intelligence). Product A, B, C, and D Distributor X, Y, and Z Promotion I, II, and III Sales Season Date/Time Salesperson Karen and John Vendor Smithson
  • Can you explain the difference between cluster analysis, association detection, and statistical analysis? Cluster analysis - a technique used to divide an information set into mutually exclusive groups such that the members of each group are as close together as possible to one another and the different groups are as far apart as possible Association detection – reveals the degree to which variables are related and the nature and frequency of these relationships in the information Statistical analysis – performs such functions as information correlations, distributions, calculations, and variance analysis Cluster analysis, association detection, and statistical analysis are covered in detail over the next few slides
  • Some examples of cluster analysis include: Consumer goods by content, brand loyalty or similarity Product market typology for tailoring sales strategies Retail store layouts and sales performances Corporate decision strategies using social preferences Control, communication, and distribution of organizations Industry processes, products, and materials Design of assembly line control functions Character recognition logic in OCR readers Data base relationships in management information systems
  • Maytag uses association detection to ensure that each generation of appliances is better than the previous generation Maytag’s warranty analysis tool automatically detects potential issues, provides quick and easy access to reports, and performs multidimensional analysis on all warranty information
  • Kraft uses statistical analysis to assure consistent flavor, color, aroma, texture, and appearance for all of its lines of foods Kraft evaluates every manufacturing procedure, from recipe instructions to cookie dough shapes and sizes to ensure that the billions of Kraft products that reach consumers each year taste great (and the same) with every bite Nestle Italiana uses data mining and statistical analysis to determine production forecasts for seasonal confectionery products The company’s data-mining solution gathers, organizes, and analyzes massive volumes of information to produce powerful models that identify trends and predict confectionery sales
  • 1. How could companies use Second Life to enhance decision making for a new product or service? By gaining feedback on the product or service from Second Life. Many companies are using Second Life to pilot virtual products. In the American Apparel store you can view clothes that are at the real store. Auto manufacturers are using Second Life to allow customers to tour virtual cars. Universities are even using Second Life to offer virtual campus tours and information. The possibilities are endless, and far less expensive then testing products in the real world, with far more diverse customers available on Second Life.   2. How could financial companies use neural networks in Second Life to help their businesses? A neural network, also called an artificial neural network, is a category of AI that attempts to emulate the way the human brain works. The types of decisions for which neural networks are most useful are those that involve pattern or image recognition because a neural network can learn from the information it processes. Neural networks analyze large quantities of information to establish patterns and characteristics in situations where the logic or rules are unknown. The finance industry is a veteran in neural network technology and has been relying on various forms of it for over two decades. The industry uses neural networks to review loan applications and create patterns or profiles of applications that fall into two categories: approved or denied. One neural network has become the standard for detecting credit card fraud. Since 1992, this technology has slashed fraud by 70 percent for U.S. Bancorp. Now, even small credit unions are required to use the software in order to qualify for debit-card insurance from Credit Union
  • 3. How could a company such as Nike use decision support systems on Second Life to help its business? A decision support system (DSS) models information to support managers and business professionals during the decision-making process. Three quantitative models are typically used by DSSs: (1) sensitivity analysis, (2) what-if analysis, and (3) goal-seeking analysis. Nike could use any of these three types of models to help its business. By asking questions to Second Life customers it could run these models to help it make business decisions. 4. How could an apparel company use Second Life to build a digital dashboard to monitor virtual operations? A common feature of an executive information system is a digital dashboard. Digital dashboards integrate information from multiple components and tailor the information to individual preferences. Digital dashboards commonly use indicators to help executives quickly identify the status of key information or critical success factors. A company could build a digital dashboard on Second Life to monitor a virtual store. It could track and monitor everything that it could track in a real store including: Number of customers Types of customers Time spent in store Number of items avatar looked at in the store Number of interactions with store avatars Number of items purchased Revenue per sale
  • A few video clips on the Darpa Grand Challenge http://reviews.cnet.com/4660-10620_7-6353439.html?tag=cnetfd.sd
  • 1. Describe how the DoD is using AI to improve its operations and save lives. The DARPA Grand Challenge was designed to leverage American ingenuity to develop autonomous vehicle technologies that can be used by the military. Using AI driven vehicles the DOD will be able to send vehicles into dangerous situations without endangering any soldiers. 2. Explain why the DoD would use an event, such as the DARPA Grand Challenge, to further technological innovation. By offering a generous prize, along with notoriety the DOD is able to get many of the greatest minds in the country working on creating autonomous vehicles. It is a win-win. The DOD receives the technology and the winning team receives a prize and notoriety.
  • 3. Describe how autonomous vehicles could be used by organizations around the world to improve business efficiency and effectiveness. There are numerous ways that autonomous vehicles could be used around by businesses from making deliveries, transporting goods and services to taking employees to and from the airport. The uses are limitless. 4. The Ansari X is another technological innovation competition focusing on spacecraft. To win the $10 million Ansari X Prize, a private spacecraft had to be the first to carry the weight equivalent of three people to an altitude of 62.14 miles twice within two weeks. SpaceShipOne, a privately built spacecraft, won the $10 million Ansari X Prize on October 4, 2004. Describe the potential business impacts of the Ansari X competition. Space travel is the next exciting frontier. Business impacts could range from vacation trips to the moon to picking up space materials for the production of goods and services. The competition could also inspire other types of competition such as underwater houses and personal flying machines.
  • Chapter 9

    1. 1. Unit Three Streaming Business OperationsMcGraw-Hill/Irwin © 2008 The McGraw-Hill Companies, All Rights Reserved
    2. 2. Unit Three• Chapter Nine – Enabling the Organization – Decision Making• Chapter Ten – Extending the Organization – Supply Chain Management• Chapter Eleven – Building a Customer-centric Organization – Customer Relationship Management• Chapter Twelve – Integrating the Organization from End to End – Enterprise Resource Planning 9-2
    3. 3. Unit Three• Decision-enabling, problem-solving, and opportunity-seizing systems 9-3
    4. 4. Chapter 9 Enabling the Organization – Decision MakingMcGraw-Hill/Irwin © 2008 The McGraw-Hill Companies, All Rights Reserved
    5. 5. Learning Outcomes9.1 Define the systems organizations use to make decisions and gain competitive advantages9.2 Describe the three quantitative models typically used by decision support systems9.3 Describe the relationship between digital dashboards and executive information systems 9-5
    6. 6. Learning Outcomes9.4 List and describe four types of artificial intelligence systems9.5 Describe three types of data-mining analysis capabilities 9-6
    7. 7. Decision Making• Reasons for the growth of decision-making information systems – People need to analyze large amounts of information – People must make decisions quickly – People must apply sophisticated analysis techniques, such as modeling and forecasting, to make good decisions – People must protect the corporate asset of organizational information 9-7
    8. 8. Decision Making• Model – a simplified representation or abstraction of reality• IT systems in an enterprise 9-8
    9. 9. Transaction Processing Systems• Moving up through the organizational pyramid users move from requiring transactional information to analytical information 9-9
    10. 10. Transaction Processing Systems• Transaction processing system - the basic business system that serves the operational level (analysts) in an organization• Online transaction processing (OLTP) – the capturing of transaction and event information using technology to (1) process the information according to defined business rules, (2) store the information, (3) update existing information to reflect the new information• Online analytical processing (OLAP) – the manipulation of information to create business intelligence in support of strategic decision making 9-10
    11. 11. Decision Support Systems• Decision support system (DSS) – models information to support managers and business professionals during the decision-making process• Three quantitative models used by DSSs include: 1. Sensitivity analysis – the study of the impact that changes in one (or more) parts of the model have on other parts of the model 2. What-if analysis – checks the impact of a change in an assumption on the proposed solution 3. Goal-seeking analysis – finds the inputs necessary to achieve a goal such as a desired level of output 9-11
    12. 12. Decision Support Systems• What-if analysis 9-12
    13. 13. Decision Support Systems• Goal-seeking analysis 9-13
    14. 14. Decision Support Systems• Interaction between a TPS and a DSS 9-14
    15. 15. Executive Information Systems • Executive information system (EIS) – a specialized DSS that supports senior level executives within the organization • Most EISs offering the following capabilities: – Consolidation – involves the aggregation of information and features simple roll-ups to complex groupings of interrelated information – Drill-down – enables users to get details, and details of details, of information – Slice-and-dice – looks at information from different perspectives 9-15
    16. 16. Executive Information Systems• Interaction between a TPS and an EIS 9-16
    17. 17. Executive Information Systems• Digital dashboard – integrates information from multiple components and presents it in a unified display 9-17
    18. 18. Artificial Intelligence (AI)• Intelligent system – various commercial applications of artificial intelligence• Artificial intelligence (AI) – simulates human intelligence such as the ability to reason and learn 9-18
    19. 19. Artificial Intelligence (AI)• The ultimate goal of AI is the ability to build a system that can mimic human intelligence 9-19
    20. 20. Artificial Intelligence (AI)• Four most common categories of AI include: 1. Expert system – computerized advisory programs that imitate the reasoning processes of experts in solving difficult problems 2. Neural Network – attempts to emulate the way the human brain works – Fuzzy logic – a mathematical method of handling imprecise or subjective information 9-20
    21. 21. Artificial Intelligence (AI)• Four most common categories of AI include: 3. Genetic algorithm – an artificial intelligent system that mimics the evolutionary, survival-of- the-fittest process to generate increasingly better solutions to a problem 4. Intelligent agent – special-purposed knowledge-based information system that accomplishes specific tasks on behalf of its users • Multi-agent systems • Agent-based modeling 9-21
    22. 22. Data Mining• Data-mining software includes many forms of AI such as neural networks and expert systems 9-22
    23. 23. Data Mining• Common forms of data-mining analysis capabilities include: – Cluster analysis – Association detection – Statistical analysis 9-23
    24. 24. Cluster Analysis• Cluster analysis – a technique used to divide an information set into mutually exclusive groups such that the members of each group are as close together as possible to one another and the different groups are as far apart as possible• CRM systems depend on cluster analysis to segment customer information and identify behavioral traits 9-24
    25. 25. Association Detection• Association detection – reveals the degree to which variables are related and the nature and frequency of these relationships in the information – Market basket analysis – analyzes such items as Web sites and checkout scanner information to detect customers’ buying behavior and predict future behavior by identifying affinities among customers’ choices of products and services 9-25
    26. 26. Statistical Analysis• Statistical analysis – performs such functions as information correlations, distributions, calculations, and variance analysis – Forecast – predictions made on the basis of time-series information – Time-series information – time-stamped information collected at a particular frequency 9-26
    27. 27. OPENING CASE STUDY QUESTIONS Second Life 1. How could companies use Second Life to enhance decision making for a new product or service? 2. How could financial companies use neural networks in Second Life to help their businesses? 9-27
    28. 28. OPENING CASE STUDY QUESTIONS Second Life 3. How could a company such as Nike use decision support systems on Second Life to help its business? 4. How could an apparel company use Second Life to build a digital dashboard to monitor virtual operations? 9-28
    29. 29. CHAPTER NINE CASE DARPA Grand Challenge• The DARPA Grand Challenge was designed to leverage American ingenuity to develop autonomous vehicle technologies that can be used by the military• With the goal of saving lives on the battlefield, the DARPA Grand Challenge brings together individuals and organizations from industry, the R&D community, government, the armed services, and academia, and includes students, backyard inventors, and automotive enthusiasts 9-29
    30. 30. Chapter Nine Case Questions1. Describe how the DoD is using AI to improve its operations and save lives2. Explain why the DoD would use an event, such as the DARPA Grand Challenge, to further technological innovation 9-30
    31. 31. Chapter Nine Case Questions3. Describe how autonomous vehicles could be used by organizations around the world to improve business efficiency and effectiveness4. The Ansari X is another technological innovation competition focusing on spacecraft. To win the $10 million Ansari X Prize, a private spacecraft had to be the first to carry the weight equivalent of three people to an altitude of 62.14 miles twice within two weeks. SpaceShipOne, a privately built spacecraft, won the $10 million Ansari X Prize on October 4, 2004. Describe the potential business impacts of the Ansari X competition 9-31

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