T​‌‍‍‍‌‍‍‍‌‍‍‍‌‌‌‌‌‌‍‍​he case studies below provide you with an opportunity to critically analyze events that are taking place in real-life businesses. This helps to develop your critical thinking and research skills as you research each of these scenarios. For this assignment, you will review four case studies, one from Chapter 9, one from Chapter 10, one from Chapter 11, and one from Chapter 12. Then, you will evaluate the studies and respond to each of the questions below, using both critical thinking and theory as well as supporting documentation. In Chapter 9, read the case study ”Soma Bay Prospers with ERP in the Cloud” on pages 345–346 of the textbook. Then, address the prompts below. ? Describe the problem that Soma Bay encountered in this case study. ? Why was an enterprise resource planning (ERP) system needed to solve the problem? ? What are some challenges that this ERP system poses? ? Will this ERP system help the company achieve operational excellence? Why, or why not?

 In Chapter 10, read the case study “A Nasty Ending for Nasty Gal” on pages 414–416 of the textbook. Then, answer the questions below. ? Was it a good idea to use social media as a marketing and social tool for Nasty Gal? Why, or why not? ? What were the management, organization, and technological issues that contributed to the failure of Nasty Gal? ? If you were hired as the new chief executive officer (CEO) of Nasty Gal, what would you have done differently to keep the company from filing for bankruptcy? In Chapter 11, read the case study “Can Cars Drive Themselves–And Should They?” on pages 453–455 of the textbook. Then, address the prompts below. ? Discuss some benefits and disadvantages of automated automobiles. Be sure to include a discussion of ethical and social issues presented by automated automobiles. ? If you were the CEO of a major auto manufacturer, would you invest resources in the development and marketing of automated automobiles? Why, or why not? In Chapter 12, read the case study “Anthem Benefits from More Business Intelligence” on pages 480-481 of the textbook. Then, address the prompts below. ? Discuss the problem with the old human resources (HR) system at Anthem. ? Describe the new People Data Central (PDC) portal adopted by Anthem. What does it do, and how does it improve data analytics capabilities at Anthem? ? How will the new PDC portal help the HR department make better decisions? 


 Soma Bay Prospers with ERP in the Cloud Soma Bay is a 10-million-square-mile resort community on the Egyptian shore of the Red Sea. It has many attractions that make it a first-class vacation paradise, including five hotels, a championship golf course, water sport facilities, a world-class spa, and luxury vacation homes. Soma Bay Development Company is headquartered in Hurghada, Egypt and has more than 2,000 employees. Unfortunately, political upheavals and economic conditions have taken a toll on occupancy rates and profitability. When President Hosni Mubarak was overthrown during the Egyptian revolution of 2011, there was a sharp devaluation of Egyptian currency. In the years that followed, political conditions stabilized and the Egyptian economy recovered, but the tourism industry lost U.S. $1.3 billion after the downing of a commercial airliner over the Sinai Desert in late 2015. Soma Bay Development Company’s hotel occupancy rates plummeted from more than 50 percent in 2015 to 25 percent in the first quarter of 2016. Foreign exchange fluctuations and political upheavals are forces beyond Soma Bay’s control, but what the company’s management can do during downturns is react intelligently by closely monitoring operations and costs.

 This is possible thanks to the company’s use of a JD Edwards Enterprise One ERP system from Oracle with applications and data residing in Oracle’s Cloud Infrastructure as a Service (Oracle Cloud IaaS). In the past, Soma Bay Development Company had tried to run much of the company using unwieldy Excel-based systems. Managers had to manually manipulate spreadsheets to understand the basic drivers of profitability, and it often took too long to obtain the information needed for sound decision making. These systems made it difficult for Soma Bay to manage its aggressive growth plans, which included construction of 1000 new homes over a five-year period. Mohammed Serry, Soma Bay Company’s CFO, and his team selected JD Edwards Enterprise One for a solution because it could create standardized business processes across functional areas and provide timely reports that explain the profitability of each business unit using a standard chart of accounts. The software can identify the profitability drivers and growth drivers of a business. Enterprise One seamlessly combines data from the general ledger and other financial systems with data from operational systems. 

Soma Bay’s Enterprise One cloud platform makes it easy to create cash flow reports, project management reports, accounts receivable aging reports, facility management reports, and key performance indicator reports throughout Soma Bay’s distributed organization. Company management also appreciates Oracle Cloud IaaS disaster recovery capabilities. Several years ago, water from an upper floor flooded Soma Bay’s Cairo data center. The company was able to restore data and resume operations quickly because it had backups stored in Oracle Cloud. JD Edwards Enterprise One contains more than 80 separate application modules designed to support a wide range of business processes. The software suite also features mobile applications that support both iOS and Android and can be used on smartphones and tablets. Soma Bay uses the JD Edwards Enterprise One modules for Financials, Procurement, Inventory Management, Job Cost, Real Estate Management, Homebuilder Management, Capital Asset Maintenance, Service Management, and Time and Labor. JD Edwards Enterprise One Homebuilder Management helps Soma Bay coordinate activities and analyze profitability throughout its home-building cycle down to the lot level. JD Edwards Enterprise One Real Estate Management streamlines financial, operational, and facilities management processes for finished properties, coordinating tasks among teams and providing a comprehensive management view of each unit. The Job Cost module shows ongoing costs for the real estate business, which helps management allocate expenses for materials, labor, and other needs and also track expenses against the budgets and forecasts established at the outset of each facilities management project. Managers can identify projects with codes and merge them with financial account numbers to determine budget expenses versus actual expenses. 

They can thereby verify if complex projects are on track and share expense data among divisions. The Enterprise One software creates currency-neutral financial reports. This helps reconcile revenue from Soma Bay’s tourism division (which caters to Germany and other parts of Europe) with its home sales division (which is aimed primarily at Egyptians) to neutralize the effect of different currencies on financial results. Home building accounts for about 25 percent of corporate revenue. Having a dual revenue stream mitigates risks. If the tourism business is slow, Soma Bay still has revenue from the real estate business, and vice versa. The ERP system provides the data required to closely track costs. For example, in 2017 Soma Bay spent 100 million Egyptian pounds (equivalent to approximately U.S. $5.7 million) on new construction. 

The Enterprise One system provided the information about cash management and cash flow for sustaining this level of expansion. Soma Bay can carefully monitor cash flow and payments to contractors. During the 2016 downturn, Soma Bay used the Enterprise One cost management and profitability capabilities to provide detailed financial data that helped managers carefully control fixed operating expenses, helping to minimize losses. Enterprise One provided a solid understanding of costs and profitability, even though revenue came from different currencies and markets. It was able to show the impact of falling occupancy rates on the business, excluding foreign exchange effect, to help management measure overall performance by legal entity. This knowledge helped Soma Bay weather the downturn and implement an aggressive turnaround plan. Today, 95 percent of Soma Bay staff members use the Enterprise One software in some capacity. The company has a more stable operating model. Occupancy rates at its five hotels are rising. Soma Bay Development Company is building 500 vacation homes in six seaside communities. According to Cherif Samir, Financial Controller for Soma Bay, being able to track every penny the company spends on a project has revolutionized the business. A Nasty Ending for Nasty Gal In 2006, Sophia Amoruso was a 22-year-old hitchhiking, dumpster-diving community college dropout with a lot of time on her hands. After reading a book called Starting an eBay Business for Dummies, she launched an eBay store called Nasty Gal Vintage, named after a song and 1975 album by the jazz singer Betty Davis, second wife of the legendary Miles Davis. Nasty Gal’s styling was edgy and fresh—a little bit rock and roll, a little bit disco, modern, but never hyper-trendy. Eight years after its founding, Nasty Gal had sold more than $100 million in new and vintage clothing and accessories, employed more than 350 people, had more than a million fans on Facebook and Instagram, and was a global brand. It looked like a genuine e-commerce success story. Or was it? When Amoruso began her business, she did everything herself out of her tiny San Francisco apartment—merchandising, photographing, copywriting, and shipping. She got up at the crack of dawn to make 6 a.m. estate sales, haggled with thrift stores, spent hours photoshopping the images she styled and shot photos herself using models she recruited herself, and ensured that packaging was high quality. She would inspect items to make sure they were in good enough shape to sell. She zipped zippers, buttoned buttons, connected hooks, folded each garment, and slid it into a clear plastic bag that was sealed with a sticker. Then she boxed the item and affixed a shipping label on it. She had to assume that her customers were as particular and as concerned with aesthetics as she was. Amoruso had taken photography classes at a community college, where she learned to understand the importance of silhouette and composition. She bought vintage pieces with dramatic silhouettes—a coat with a big funnel collar, a ’50s dress with a flared skirt, or a Victorian jacket with puffy sleeves. Exaggerating everything about the silhouette through the angle from which it was photographed helped Amoruso produce tiny thumbnails for eBay that attracted serious bidders. She was able to take an object, distill what was best about it, and then exaggerate those qualities so they were visible even in its tiniest representation. When the thumbnail was enlarged, it looked amazing. Amoruso has been a heavy user of social tools to promote her business. When she first started out, she used MySpace, where she attracted a cult following of more than 60,000 fans. The company gained traction on social media with Nasty Gal’s aesthetic that could be both high and low, edgy and glossy. Amoruso took customer feedback very seriously and believed customers were at the center of everything Nasty Gal did. When she sold on eBay, she learned to respond to every customer comment to help her understand precisely who was buying her goods and what they wanted. Amoruso said that the content Nasty Gal customers created has always been a huge part of the Nasty Gal brand. It was very important to see how customers wore Nasty Gal’s pieces and the types of photographs they took. They were inspiring. Social media is built on sharing, and Nasty Gal gave its followers compelling images, words, and content to share and talk about each day. They could be a crazy vintage piece, a quote, or a behind-the-scenes photo. At most companies the person manning the Twitter and Facebook accounts is far removed from senior management. Amoruso did not always author every Nasty Gal tweet, but she still read every comment. If the customers were unhappy about something, she wanted to hear about it right away. At other businesses, it might take months for customer feedback to filter up to the CEO. When Nasty Gal first joined Snapchat, Amoruso tested the water with a few Snaps, and Nasty Gal followers responded in force. In June 2008, Amoruso moved Nasty Gal Vintage off eBay and onto its own destination website, www.nastygal.com. In 2012, Nasty Gal began selling clothes under its own brand label and also invested $18 million in a 527,000-square-foot national distribution center in Shepherdsville, Kentucky, to handle its own shipping and logistics. Venture capitalists Index Ventures provided at least $40 million in funding. Nasty Gal opened a brick-and-mortar store in Los Angeles in 2014 and another in Santa Monica in 2015. With growing direct-to-consumer demand and higher inventory replenishment requirements driven by new store openings, Nasty Gal invested in a new warehouse management system. The warehouse management system investment was designed to increase warehouse productivity and shorten order cycle times so that Nasty Gal’s supply chain could better service its mushrooming sales. (Order cycle time refers to the time period between placing of one order and the next order.) The company selected HighJump’s Warehouse Management System (WMS) with the goal of increasing visibility and overall productivity while keeping fill rates above 99 percent. (The fill rate is the percentage of orders satisfied from stock at hand.) Key considerations were scalability and capabilities for handling retail replenishment in addition to direct-to-consumer orders. HighJump’s implementation team customized the WMS software to optimize the business processes that worked best for an e-commerce retailer that ships most of its items straight to the customer, with a small subset going to retail stores. The WMS software was also configured to support processes that would scale with future growth. Picking efficiency and fill rates shot up, with fill rates above 99 percent, even though order volume climbed. Nasty Gal experienced tremendous growth in its early years, being named INC Magazine’s fastest-growing retailer in 2012 and earning a number one ranking in Internet Retailer’s Top 500 Guide in 2016. By 2011, annual sales hit $24 million and then nearly $100 million in 2012. However, sales started dropping to $85 million in 2014 and then $77 million in 2015. Nasty Gal’s rapid expansion had been fueled by heavy spending in advertising and marketing. This is a strategy used by many start-ups, but it only pays off in the long run if one-time buyers become loyal shoppers. Otherwise, too much money is spent on online marketing like banner ads and paying for influencers. If a company pays $70 on marketing to acquire a customer and that customer only buys once from it, the company won’t make money. A company that spends $200 million to make $100 million in revenue is not a sustainable business. Nasty Gal had a “leaky bucket” situation: Once it burned through its fundraising capital and cut down on marketing, sales continued to drop. Nasty Gal couldn’t hold onto customers. Some were dissatisfied with product quality, but many were more attracted to fast-fashion retailers such as Zara and H&M, which both deliver a wider array of trendy clothes through online and bricks-and-mortar stores at lower prices and are constantly changing their merchandise. The actual market for the Nasty Gal brand was quickly saturated. There was a limit to the number of women Nasty Gal appealed to: Nasty Gal had a California cool, young girl look, and it was unclear how attractive it was in other parts of the United States and around the world. Nasty Gal also wasted money on things that didn’t warrant large expenditures. The company quintupled the size of its headquarters by moving into a 50,300-square-foot location in downtown Los Angeles in 2013—far more space than the company needed, according to industry experts. The company had also opened a 500,000-square-foot fulfillment center in Kentucky to handle its own distribution and logistics as well as two bricks-and-mortar stores in Los Angeles and Santa Monica. Even in the hyper-trendy fashion business, companies have to closely monitor production, distribution, and expenses for operations to move products at a scale big enough to make a profit. Nasty Gal’s mostly young staff focused too much on the creative side of the business. While it was growing, Nasty Gal built its management team, hiring sizzling junior talent from retail outlets such as Urban Outfitters. But their traditional retail backgrounds clashed with the start-up mentality. As Nasty Gal expanded, Amoruso’s own fame also grew, and she was sidetracked by other projects. She wrote two books. The first, titled #Girlboss, described the founding of Nasty Gal and Amoruso’s business philosophy and was adapted by Netflix into a show with Amoruso as executive producer. (The series was cancelled in June 2017 after just one season.) Employees complained about Amoruso’s management style and lack of focus. Amoruso resigned as chief executive in 2015 but remained on Nasty Gal’s board of directors until the company filed for Chapter 11 bankruptcy on November 9, 2016. Between 2015 and 2016, Nasty Gal had raised an additional $24 million in equity and debt financing from venture-focused Stamos Capital Partners LP and Hercules Technology Growth Capital Inc. Even though the funding helped Nasty Gal stay afloat, the company still had trouble paying for new inventory, rent, and other operating expenses. Within weeks of filing for Chapter 11 protection, Nasty Gal sold its brand name and other intellectual property on February 28, 2017, for $20 million to a rival online fashion site, the United Kingdom’s Boohoo.com. Boohoo is operating Nasty Gal as a standalone website, but Nasty Gal’s stores are closing. Boohoo believes Nasty Gal’s arresting style and loyal customer base will complement Boohoo and expand global opportunities for growth. Many customers have complained about the quality of fabric and customer service. Amoruso subsequently turned to developing Girlboss—a media company that hosts a website, a podcast, and two annual conferences, called the Girlboss Rally. She also launch​‌‍‍‍‌‍‍‍‌‍‍‍‌‌‌‌‌‌‍‍​ed the Girlboss Foundation, which has given out $130,000 to women-owned small businesses. Can Cars Drive Themselves—And Should They? Will cars really be able to drive themselves without human operators? Should they? And are they good business investments? Everyone is searching for answers. Autonomous vehicle technology has reached a point where no automaker can ignore it. Every major auto maker is racing to develop and perfect autonomous vehicles, believing that the market for them could one day reach trillions of dollars. Companies such as Ford, General Motors, Nissan, Mercedes, Tesla, and others have invested billions in autonomous technology research and development. Ford invested $1 billion in AI firm Argo AI, and GM bought a self-driving car startup called Cruise. Ford has set a goal of producing a self-driving car with no pedals by 2021. Ride-hailing companies like Uber and Lyft believe driverless cars that eliminate labor costs are key to their long-term profitability. Cars that drive themselves have been on the road in select locations in California, Arizona, Michigan, Paris, London, Singapore, and Beijing. Waymo, the company that emerged from Google’s self-driving car project, predicts that by 2020 its fleet of self-driving Jaguars will make as many as one million trips per day. A car that is supposed to take over driving from a human requires a very powerful computer system that must process and analyze large amounts of data generated by myriad sensors, cameras, and other devices to control and adjust steering, accelerating, and braking in response to real-time conditions. Key technologies include: SENSORS:?Self-driving cars are loaded with sensors of many different types. Sensors on car wheels measure car velocity as it drives and moves through traffic. Ultrasonic sensors measure and track positions of line curbs, sidewalks, and objects very close to the car. CAMERAS:?Cameras are needed for spotting things like lane lines on the highway, speed signs, and traffic lights. Windshield-mounted cameras create a 3-D image of the road ahead. Cameras behind the rear-view mirror focus on lane markings. Infrared cameras pick up infrared beams emitted from headlamps to extend vision for night driving. LIDARS:?Lidars are light detection and ranging devices which sit on top of most self-driving cars. A lidar fires out millions of laser beams every second, measuring how long they take to bounce back. The lidar takes in a 360-degree view of a car’s surroundings, identifying nearby objects with an accuracy up to 2 centimeters. Lidars are very expensive and not yet robust enough for a life of potholes, extreme temperatures, rain, or snow. GPS:?A global positioning system (GPS) pinpoints the car’s macro location, and is accurate to within 1.9 meters. Combined with reading from tachometers, gyroscopes, and altimeters, it provides initial positioning. RADAR:?Radar bounces radio waves off of objects to help see a car’s surroundings, including blind spots, and is especially helpful for spotting big metallic objects, such as other vehicles. COMPUTER:?All the data generated by these technologies needs to be combined, analyzed, and turned into a robot-friendly picture of the world, with instructions on how to move through it, requiring almost supercomputer-like processing power. Its software features obstacle avoidance algorithms, predictive modeling, and “smart” object discrimination (for example, knowing the difference between a bicycle and a motorcycle) to help the vehicle follow traffic rules and navigate obstacles. MACHINE LEARNING, DEEP LEARNING, AND COMPUTER VISION TECHNOLOGY:?The car’s computer system has to be “trained” using machine intelligence and deep learning to do things like detect lane lines and identify cyclists, by showing it millions of examples of the subject at hand. Because the world is too complex to write a rule for every possible scenario, cars must be able to “learn” from experience and figure out how to navigate on their own. MAPS:?Before an autonomous car takes to the streets, its developers use cameras and lidars to map its territory in extreme detail. That information helps the car verify its sensor readings, and it is key for any vehicle to know its own location. Self-driving car companies are notorious for overhyping their progress. Should we believe them? At this point, the outlook for them is clouded. In March 2018, a self-driving Uber Volvo XC90 operating in autonomous mode struck and killed a woman in Tempe, Arizona. Since the crash, Arizona has suspended autonomous vehicle testing in the state, and Uber is not renewing its permit to test self-driving cars in California. The company has also stopped testing autonomous cars in Pittsburgh and Toronto and it’s unclear when it will be revived. Even before the accident, Uber’s self-driving cars were having trouble driving through construction zones and next to tall vehicles like big truck rigs. Uber’s drivers had to intervene far more frequently than drivers in other autonomous car projects. The Uber accident raised questions about whether autonomous vehicles were even ready to be tested on public roads and how regulators should deal with this. Autonomous vehicle technology’s defenders pointed out that nearly 40,000 people die on U.S. roads every year, and human error causes more than 90 percent of crashes. But no matter how quickly self-driving proliferates, it will be a very long time before the robots can put a serious dent in those numbers and convince everyday folks that they’re better off letting the cars do the driving. While proponents of self-driving cars like Tesla’s Elon Musk envision a self-driving world where almost all traffic accidents would be eliminated, and the elderly and disabled could travel freely, most Americans think otherwise. A Pew Research Center survey found that most people did not want to ride in self-driving cars and were unsure if they would make roads more dangerous or safer. Eighty-seven percent wanted a person always behind the wheel, ready to take over if something went wrong. There’s still plenty that needs to be improved before self-driving vehicles could safely take to the road. Autonomous vehicles are not yet able to operate safely in all weather conditions. Heavy rain or snow can confuse current car radar and lidar systems—autonomous vehicles can’t operate on their own in such weather conditions. These vehicles also have trouble when tree branches hang too low or bridges and roads have faint lane markings. On some roads, self-driving vehicles will have to make guidance decisions without the benefit of white lines or clear demarcations at the edge of the road, including Botts’ Dots (small plastic markers that define lanes). Botts’ Dots are not believed to be effective lane-marking for autonomous vehicles. Computer vision systems are able to reliably recognize objects. What remains challenging is “scene understanding”—for example, the ability to determine whether a bag on the road is empty or is hiding bricks or heavy objects inside. Although autonomous vehicle vision systems are now capable of picking out traffic lights reliably, they are not always able to make correct decisions if traffic lights are not working. This requires experience, intuition, and knowing how to cooperate among multiple vehicles. Autonomous vehicles must also be able to recognize a person moving alongside a road, determine whether that person is riding a bicycle, and how that person is likely to respond and behave. All of that is still difficult for an autonomous vehicle to do right now. Chaotic environments such as congested streets teeming with cars, pedestrians, and cyclists are especially difficult for self-driving cars to navigate. Driving a car to merge into rapidly flowing lanes of traffic is an intricate task that often requires eye contact with oncoming drivers. How can autonomous vehicles communicate with humans and other machines to let them know what they want to do? Researchers are investigating whether electronic signs and car-to-car communication systems would solve this problem. There’s also what’s called the “trolley problem”: In a situation where a crash is unavoidable, how does a robot car decide whom or what to hit? Should it hit the car coming up on its left or a tree on the side of the road? A less advanced version of autonomous vehicle technology is already on the market. Cadillac Super Cruise, Nissan ProPilot Assist, and Tesla Autopilot are capable of keeping a car in its lane and a safe distance from other cars, allowing the “driver” behind the wheel to take hands off the wheel, provided that person keeps paying attention and is ready to take control if needed. These less-advanced systems can’t see things like stopped fire trucks or traffic lights. But humans haven’t made good driving backups because their attention tends to wander. At least two Tesla drivers in the U.S. have died using the system. (One hit a truck in 2016, another hit a highway barrier in 2018.) There is what is called a “handoff problem.” A semi-autonomous car needs to be able to determine what its human “driver” is doing and how to get that person to take the wheel when needed. And let’s not forget security. A self-driving car is essentially a collection of networked computers and sensors linked wirelessly to the outside world, and it is no more secure than other networked systems. Keeping systems safe from intruders who want to crash or weaponize cars may prove to be the greatest challenge confronting autonomous vehicles in the future. Self-driving cars require new ecosystems to support them, much as today’s cars are dependent on garages, gasoline stations, and highway systems. New roads, highways, and automotive supply chains will have to be rebuilt for self-driving cars. The big auto makers that build millions of cars a year rely on complex, precise interaction among hundreds of companies, including automotive component suppliers and the services to keep cars running. They need dealers to sell the cars, gas pumps or charging stations to fuel them, body shops to fix them, and parking lots to store them. Manufacturers of autonomous vehicles need to rethink interactions and processes built up over a century. The highway infrastructure will need to change over time to support autonomous vehicles. Waymo has partnered with Avis to take care of its fleet of driverless minivans in Arizona, and it’s working with a startup called Trov to insure their passengers. GM is retooling one of its plants to produce Chevrolet Bolts without steering wheels or pedals. A computer-driven car that can handle any situation as well as a human under all conditions is decades away at best. Many analysts expect the first deployment of self-driving technology will be robot taxi services operating in limited conditions and areas, so their operators can avoid particularly tricky intersections and make sure everything is mapped in fine detail. The Boston Consulting Group predicts that 25 percent of all miles driven in the U.S. by 2030 may be by shared self-driving vehicles. To take a ride, you’d probably have to use predetermined pickup and drop-off points, so your car can always pull over safely and legally. The makers of self-driving cars will be figuring out how much to charge so they can recoup their research and development costs, but not so much as to dissuade potential riders. They’ll struggle with regulators and insurance companies over what to do in the inevitable event of a crash. Some pundits predict that in the next few decades, driverless technology will add $7 trillion to the global economy and save hundreds of thousands of lives. At the same time, it could devastate the auto industry along with gas stations, taxi drivers, and truckers. People might stop buying cars because services like Uber using self-driving cars would be cheaper. This could cause mass unemployment of taxi drivers and large reductions in auto sales. It would also cut down the need for many parking garages and parking spaces, freeing up valuable real estate for other purposes. More people might decide to live further from their workplaces because autonomous vehicles linked to traffic systems would make traffic flow more smoothly and free riders to work, nap, or watch video while commuting. Some people will prosper. Most will probably benefit, but many will be left behind. Driverless technology is estimated to change one in every nine U.S. jobs, although it will also create new jobs. Another consideration is that the tremendous investment in autonomous vehicles, estimated to be around $32 billion annually, might be better spent on improving public transportation systems like trains and subways. Does America need more cars in sprawling urban areas where highways are already jammed? The accidents self-driving cars have experienced so far point to the need to create a dependable standard for measuring reliability and safety. In 2018, twenty-nine states have enacted legislation regulating autonomous vehicles, with a few states requiring a safety driver always be in the car ready to take control. U.S. federal regulators have delayed formulating an overarching set of self-driving car standards, leaving a gap for the states to fill. The federal government is only now poised to create its first law for autonomous vehicles. This law is like Arizona’s and would allow hundreds of thousands of driverless cars to be deployed within a few years and would restrict states from putting up hurdles for the industry. Anthem Benefits from More Business Intelligence Anthem Inc. is one of the largest health benefit companies in the United States. One in eight Americans receives coverage for their medical care through Anthem’s affiliated plans. Anthem also offers a broad range of medical and specialty products, such as life and disability insurance benefits; dental, vision, and behavioral health services; and long-term care insurance and flexible spending accounts. Anthem is headquartered in Indianapolis, Indiana and earned over $90 billion in revenue in 2017. Anthem has been an industry leader in analyzing data to reduce fraud and waste, cultivate customers, fine-tune its products, and keep healthcare costs low. For example, the company analyzes the data it collects on benefit claims, clinical data, electronic health records, lab results, and call centers to determine each person’s risk for an emergency room visit or a stroke. This information helps the company identify opportunities for improvement and individuals who might benefit from additional services or wellness coaching. Now Anthem is applying its analytical skills internally to sharpen decisions about how to deploy and develop its employees as a strategic resource. Anthem has 56,000 employees and would like better answers to these questions: What is our employee turnover rate for call center staff in Colorado Springs or across the United States? What is the cost of that turnover? Are we differentiating rewards for our top performers? How many of our nurses might retire within the next couple of years? What is the relationship between our time-to-hire metrics and national unemployment rates? Anthem created a cloud-based People Data Central (PDC) portal, whose interactive “workforce intelligence” dashboard lets HR and other Anthem users access and ask questions such as these using internal and third-party data. The portal serves 56,000 employees and presents data in dashboards, graphs, reports, and other highly visual formats that are easy to comprehend and manipulate. A high-level “executive scorecard” feature explores the relationship between Human Resources metrics and business outcomes to use in short- and long-term planning. One scorecard report showed the relationship between customer growth, Anthem’s net hire ratio, and total costs associated with internal and external labor. PDC provides a total of seven dashboards on “hire-to-retire” HR operations, 50 summary views from which users can drill down into detailed reports, and links to other relevant data and training sources. The company also formed a Talent Insights team, whose members include MBAs, PhDs, and CPAs, to develop more sophisticated data analysis and help users work with the data. For example, the Talent Insights team worked with Anthem’s Wellness team to examine data on employees who utilize company wellness credits to determine whether this could be correlated with reduced absenteeism and employee turnover. Every month, the team looks at a different slice of data using various indicators such as company performance, employee participation in wellness programs, or reductions in absenteeism. For example, one month the team broke down Anthem’s workforce by generational age bands and examined the potential ramifications for company performance, highlighting its analysis in the portal’s “insight spotlight” section. The PDC uses Oracle Human Capital Management (HCM) Cloud and Oracle Business Intelligence Cloud Service. Oracle HCM Cloud is the cloud-based version of Oracle tools for Human Capital Management (human resources management), including tools for talent management and workforce management. Oracle Business Intelligence Cloud Service provides powerful data analytics tools as a cloud service that is available to anyone in the enterprise. 

The service includes tools for ad hoc query and analysis, interactive dashboards, and reports. Anthem’s Oracle-based cloud platform took less than six months to deploy. Anthem’s human resources and talent data used to be scattered among many different systems and spreadsheets, so they were difficult to aggregate and compare. Basic questions such as “What is our turnover rate?” or “How many open positions do we have right now?” produced different answers, depending on who was asked, even within the same location or work group. To improve the HR department’s recruiting, hiring, promotion, and employee development programs, Anthem needed better analytic tools and a single standardized enterprise-wide view of its data. The PDC is able to perform sentiment analysis (see Chapter 6). The portal includes a channel where employees can voice their opinions about their work experiences confidentially, using emojis and limited text entries.

 The system analyzes these ongoing sentiments to create “team vitals” reports to help management identify and address potential productivity risks. The company often finds information on which it can immediately take action, such as ideas for improving processes or re-prioritizing resources. The Anthem Talent Insights team is also helping other business groups outside of Human Resources make better use of data. These employees from other functional business areas typically have access only to the data for their business units. Talent Insights helps them combine these data with HR data so they can answer questions such as how their high-potential employees whose careers are advancing compare with those in other parts of the company. The t​‌‍‍‍‌‍‍‍‌‍‍‍‌‌‌‌‌‌‍‍​eam developed a model that accurately predicts first-year attrition and identifies the causes of employee turnover. Anthem management is using this information to increase employee retention and create better profiles for future hires.


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