DATA ANALYTICS AT FORD MOTOR COMPAMY COVERING ALL BETS

How analytics helped Ford turn its fortunes: Like all automakers, Ford wants to make the best choices when picking the next new vehicle and fuel technologies. Its future depends on it. Data analytics is playing a central role in that process. Ford has partnered with researchers at Chalmers University of Technology in Sweden to develop a global energy model to help decision-makers understand global energy supply and demand and how to meet its needs at a minimal cost and in a sustainable way. "The model looks 100 years into the future and can be used to address what-if questions, such as, 'If we had a carbon dioxide emission target of x, what would that mean for autos, trains and planes?'" says senior technical leader Tim Wallington, who heads the Ford analytics team focused on sustainability issues.


CYBERSPACE, DECEMBER 2, 2013 (COMPUTERWORLD) DATA WILL SET YOU FREE. Like all automakers, Ford wants to make the best choices when picking the next new vehicle and fuel technologies. Its future depends on it.

Data analytics is playing a central role in that process.


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Ford has partnered with researchers at Chalmers University of Technology in Sweden to develop a global energy model to help decision-makers understand global energy supply and demand and how to meet its needs at a minimal cost and in a sustainable way.

"The model looks 100 years into the future and can be used to address what-if questions, such as, 'If we had a carbon dioxide emission target of x, what would that mean for autos, trains and planes?'" says senior technical leader Tim Wallington, who heads the Ford analytics team focused on sustainability issues.

"There is a wide range of vehicle fuel technologies in the future, and what we did in the model is include our best estimates of the current and likely efficiency of those technologies and a range of costs associated with them," Wallington explains.

For example, "when looking at electric vehicles, we know what they cost now, but how much they'll cost in the future we don't know," he says. "We feel confident that the cost of batteries will decline, but we don't know by how much, so we include in the model a number of different takes."

By manipulating various what-if factors, including emission targets, fuel types and costs, Ford researchers ultimately came to the conclusion that, for now, "there is no silver bullet," Wallington says. In other words, no single technology is the correct choice for the vehicle of the future, which is why Ford has adopted a portfolio strategy to developing sustainable technologies and fuel options.

"We did thousands of scenarios, and the bottom line is that given the uncertainties in future costs and efficiencies, it's not possible to pick a winner," Wallington says. "Customers can vote with their money as to which they want and which one wins the future. This is the high-level strategy."

The upshot is that Ford is making a range of vehicles with alternative fuel options. These include cars with advanced diesel engines, hybrids, plug-in hybrids, all-electric vehicles and alternative fuel vehicles. The company uses the catchphrase "the power of choice" to market that strategy.

Some of Ford's competitors, meanwhile, are focusing on battery-powered and electric vehicles. "Others have put more of their eggs in a single basket," says John Ginder, manager of system analytics and environmental science at Ford. "In the first decades of this century, [other automakers] spent a lot on fuel cell vehicles.

They were very bullish on them. We are intrigued by them, but we also have a prudent risk management." That approach, says Ginder, is solidly based on analytics. Julia King

Data Analytics at Ford Translating Business Needs to Mathematical Language


As John Ginder sees it, the best data scientists possess a combination of mathematical expertise, familiarity with computer science and programming applications, and the ability to translate business needs into mathematical language.

Usually, Ginder can find people skilled in one or two of those areas. "But it's hard to find all three traits in one person," he says.

Ginder, an early proponent of analytics at Ford, is a physicist by training. As it turns out, so are many of the automaker's other 200 or so data scientists.

"I personally look for people who can adapt and reinvent themselves on a regular basis," Ginder says. "We have physicists, chemists, applied mathematicians and research specialists. Physicists are a good source because they have a certain mindset on how to approach problems. But we also want people with MBAs and engineers."

Ford's data scientists are assigned to so-called centers of excellence associated with different departments, such as marketing and research. They work on both strategic and tactical issues, ranging from which models of cars to produce to where to source materials and where to build certain vehicles.

The key to a successful project is the stability of the data, Ginder says.

"I'm always looking for a stable source of data and looking to what degree we can expect to continue to have that data," he says. "My contention is if there's not a guarantee we'll have access to that data in an ongoing fashion, we don't want to develop an application around it."

Over the past decade and a half, Ford's centers of excellence have developed successful relationships with the company's IT department. But it wasn't always that way.

"Fifteen years ago, IT was largely the organization we had to go to for access to data from transactions. They were the source of data and we were a source of headaches because we were developing what they considered shadow IT," Ginder recalls.

That began to change when Ginder's team was working on SIMS, the Smart Inventory Management System. "We started to understand some of the constraints of the IT organization and understand their practices in terms of how applications were developed," Ginder says. "They were following a traditional waterfall process, which we in research thought was ineffective."

Today, as a result of working with the analytics team on SIMS, "our IT application development organization has moved almost entirely to agile practices," Ginder says. Julia King, COMPUTERWORLD

Author Bio


Julia King, Executive Editor, Events/National Correspondent at Computerworld, is an award-winning business and technology journalist with more than 20 years experience covering a wide range of topics, from IT executive leadership and technology workforce management to cloud and grid computing, business analytics and IT return on investment.

Ms. King was the founding executive editor of the award-winning ComputerworldROI magazine and spearheaded Computerworlds coverage of information economics.


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