Reference no: EM131052586

CASE

If necessity is the mother of invention, then capitalism is surely the mother of innovation. Companies are being driven to develop unique applications of undeniably cool technologies by the drive to create a sustainable competitive advantage. "At the end of the day, as cool as this thing we've developed is, it's a tool," says Stephanie Wernet, Goodyear's CIO. "It is meant to serve a business end. In our case, this tool lets us put out new, more innovative products faster than the competition." Working with Sandia National Labs, Goodyear's IT department developed software to design and test tires virtually. In the past, the company built physical prototypes and tested them by driving thousands of miles on tracks. Using a mathematical model, the software simulates tire behavior in different driving conditions so that the designer can see how the tire gets pushed, pulled, and stretched as it rolls down a road, hits bumps, turns corners, screeches to a halt, and grips the road in wet, dry, and icy conditions.

Goodyear wanted to shorten that time to get its products to market more quickly. Three research and development employees advanced the idea of testing prototypes using computer simulations, which could do the job faster. The company had never done simulations but figured initial investments and subsequent maintenance costs were worth the payoff. Goodyear's cost of goods sold, as well as its sales, decreased by 2.6 percent from 2003 to 2004, the year its first fully simulated tires hit the market. Meanwhile, the research and development (R&D) budget for tire testing and design decreased by 25 percent. Custom-built software runs on hundreds of processors on hundreds of Linux computers in a massively parallel computing environment. Goodyear invested more than $6 million to build this high-powered computing environment. It plans to expand and upgrade its Linux clusters to meet business demands for new tires and to improve the fidelity of its virtual tests. The company believes it is the first tire maker to use computers to design and test its wheels. Although the auto industry has done computer-assisted design work since the 1980s, the technology had not been applied to tires because their malleable materials made simulation difficult. Designers can perform 10 times more tests, reducing a new tire's time to market from two years to as little as nine months. Goodyear attributes its sales growth from $15 billion in 2003 to $20 billion in 2005 to new products introduced as a result of this change. Public utility JEA uses neural network technology to create an artificial intelligence system it has recently implemented. The system automatically determines 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. It also ensures that the amount of nitrous oxide (N2O) emitted during the generation process does not exceed government regulations. JEA needed to decrease operating expenses, in particular fuel costs, as oil and gas prices began their precipitous ascent in 2002. Forty percent of JEA's $1.3 billion budget goes to the purchase of oil and gas to power its boilers, so a small change in the way electricity is produced could add millions of dollars to the bottom line. Neural network technology models the process of producing electricity. Optimization software from NeuCo determines the right combinations of oil and gas to produce electricity at low cost while minimizing emissions. JEA, which serves more than 360,000 customers in Jacksonville and three neighboring Florida counties, is the first utility in the world to apply neural network technology to the production of electricity in circulating fluidized-bed boilers. It built a system that makes decisions based on historical operating data and as many as 100 inputs associated with the combustion process, including air flows and megawatt outputs. The system learns which fuel combinations are optimal by making adjustments to the boiler in real time; it also forecasts what to do in the future based on specific fuel cost assumptions. "We had issues with oil prices. At the same time, gas prices went from $4 a BTU to over $14. We need to use gas because it decreases emissions. This solution helped us balance all of those items," says Wanyonyi Kendrick, JEA's CIO. The project, which IT drove, cost $800,000 and paid for itself in eight weeks. The system reduced the quantity of natural gas that is used to control N 2O emissions by 15 percent, an estimated annual savings of $4.8 million. With natural gas prices at $11 per BTU, JEA expects to save $13 million on fuel in 2006.

What's more, JEA has discovered it can use the new technology applications for its water business. 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. The robots do not interact with patients; they carry out routine tasks that hospital staff used to do. Faced with declining revenue and rising costs, OSUMC needed to save money while improving patient care. A steering committee comprising IT, other hospital departments, consultants, and vendors drove this project. They convinced medical staff of its value by demonstrating the technology and communicating how it improved working conditions and patient care. Materials transport was identified as a place to cut costs since the hospital needed to upgrade the existing system. The robots, made by FMC Technologies, are guided by a wireless infrared network from Cisco Systems. The network is embedded in corridor walls and elevators designed for the robots' use. Three Windows servers linked to the network maintain a database of robot jobs and traffic patterns. OSUMC is the first hospital in the United States to implement an infrared-guided automated system for transporting materials. Hospital staff use a touch-screen computer connected to a server to call a robot when, for example a linen cart needs to go to the laundry room. To get from point A to point B, the robots rely on a digital map of the medical center programmed into their memory; they also track their movements against the number of times their wheels rotate in a full circle.

So if it takes a robot 1,000 wheel revolutions to get from a building's kitchen to the sixth floor, and its wheels have moved in 500 revolutions, the robot knows it is halfway there. If a robot loses network contact, it shuts down. The $18 million system is expected to save the hospital approximately $1 million a year over the next 25 years. Since it went live in 2004, OSUMC has saved $27,375 annually on linen delivery alone. OSUMC's CIO Detlev Smaltz says the system improves patient care by freeing up personnel: "If we can take mundane jobs like taking out the trash off of our employees and give them more time to do the things they came into the health-care profession to do, then that's an added benefit of the system."

Monsanto's IT department created software to identify genes that indicate a plant's resistance to drought, herbicides, and pests; those genetic traits are used to predict which plants breeders should reproduce to yield the healthiest, most bountiful crops. The software crunches data from breeders worldwide and presents them in a colorful, easy-to-comprehend fashion. By pinpointing the best breeding stock, it increases breeders' odds of finding a commercially viable combination of genetic traits from one in a trillion to one in five. Monsanto's global breeding organization drove the project. When the patent expired for Roundup, Monsanto's signature weed killer, the St. Louis company invested in growing its business involving seeds and genetic traits, which comprises more than half of its $6.3 billion revenue and $255 million profits in 2005. Monsanto believes it can sell more corn, soybean, and cotton seeds if farmers know its seeds will produce heartier crops and require fewer sprays of insecticide and herbicide, thus reducing costs. Monsanto's scientists use the software to engineer seeds that effectively resist drought and pests and to produce plants that are healthier for humans and animals to eat. They do it by implanting those seeds with the genetic material that makes a plant resist insects or produce more protein. What would Gregor Mendel, the father of genetics, think of this? "This is really different from the way breeders bred their crops," says Monsanto CIO Mark Showers. "They didn't have this level of molecular detail to determine and select plants they wanted to move forward from year to year." Monsanto reaps the benefit of its software but wouldn't reveal development costs. Earnings per share (EPS) on an ongoing basis grew from $1.59 to $2.08, or 30 percent, from 2004 to 2005. Its EPS is expected to grow by 20 percent more in 2006. "In the last four or five years, we've had a marked improvement in taking market share from our competition. We've grown our share at a couple of points per year," says Showers.

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?