Please read the case and answer3 questions at the end.

Goodyear, JEA, OSUMC, and Monsanto: Cool Technologies Driving Competitive Advantage.

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 N2O 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 selfguided

 

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.

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?