Merging Data Lanes
To do that, Ford integrates and analyzes several data streams, including data on what it has already built and sold, data on what has sold in the context of what was available in inventory at the time of the sale, plus data on what customers are searching for and configuring on company websites. This data is then combined with economic data to predict vehicle sales relative to housing starts, employment rates and other information. The system is known as the Smart Inventory Management System, or SIMS.
Data Analytics at Ford
Covering All Bets
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.
"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.
"We largely figured things out over various experiments and applications in this area over many years, but much of it was not enabled until the last few years because of the increase in computational power," Goodman says. "We have 20,000 compute cores in the building next door at our disposal. We have computers with 1.5TB of RAM. It's those sorts of resources that have enabled us to synthesize this kind of data."
All around, SIMS is acknowledged as a pivotal factor in Ford's turnaround and the overall success of Mulally's global "One Ford" strategy.
"As we globalize and leverage products from around the world, it means new complexity management challenges," says Goodman. Before, "if any feature was desired in any market, we'd engineer it and make it available. When you get into different roof heights, different interiors, different wheels and so on, we can offer an astronomically large number of combinations. Imagine 300 billion and then multiply it by itself again. Customers get overwhelmed by too many choices." Instead, Goodman says, "we want to match customers' wants with our global supply chain. It's a challenge throughout the industry, but there's huge value in getting it right." This requires knowing customers' key preferences, then tailoring the bulk of the company's production to those preferences, rather than building a very wide range of models with many combinations of features. It comes down to building cars that most customers want most of the time.
This is where SIMS has really paid off. Assembly plant schedules and parts forecasts have both significantly improved because "we can run those schedules with better algorithms," says Goodman. "In 2007, we could do it but it would take two to three weeks, and assemblers needed answers in 20 minutes or less. Today, we can do it in two minutes."
Ford dealerships, which are independent franchises, also have benefitted. Some 3,500 dealerships receive the weekly reports. Those that subscribe to SIMS recommendations say that they are selling cars at higher prices and more quickly, says Ginder.
Down the Road
Yet Ford's analytics experts say they have only just begun to scratch the surface with big data. The next big frontier is data that streams from vehicles themselves.
"The volume of data generated by vehicles is huge," says Ginder. Ford's Fusion Energi plug-in hybrid car gets the equivalent of 108 miles per gallon. It also generates 25GB of data per hour.
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.
Ford currently offers three kinds of hybrid cars: two plug-in models and one electric model. All are equipped with an embedded modem that customers can enable on an opt-in basis to stream data about vehicle performance back to the manufacturer.
"We gather data every time the customer plugs in. We know where they're plugging in, how many gas miles they drove, how many electric miles, how often they plug in and how often they take trips. It's helping to shape where we go next with products," says Mike Tinskey, director of vehicle electrification and infrastructure. Still, "our big data [from vehicles] is relative for now. It's small but growing," he says.
It's also extremely valuable, especially when combined with other data from social media sites.
"It's how we get to know our customers better," says Cavaretta. "If we know how people are using their vehicle and what they're saying about it, we can then look at how it relates to our internal business processes." When integrated, the data creates a veritable heat map of where improvements need to be made.
Ford also provides reports back to the drivers. "We're taking the big data and processing it for all stakeholders as a monthly report," Tinskey says, noting that customers receive a link to a customized monthly report detailing their usage and vehicle performance.
Intersecting the Internet of Things
Tinskey, Cavaretta and others on Ford's analytics team anticipate that, increasingly, vehicle data will be combined with other types of internal and external data via the Internet of Things. That could enable customers to do things like tune their cars' engines to individual specifications and pay discounted rates for the electricity they use to recharge, depending on the time of day or night that they drive and/or recharge their vehicles.
"The really cool thing is we know where these customers are charging and we know what electricity generation looks like in their ZIP codes. We have a grid generation database of all the ZIP codes in the United States, and we tie that data together with charging data," explains Tinskey.
"What we're proposing is that as we reach critical mass, vehicles could respond to calls from the utility to scale back [consumption]. One scenario is that customers would delay charging their cars when power demand is high and the utility would compensate them by charging them a lower rate," he says. The scenario is similar to programs already in place for heating and cooling homes. Tinskey notes, for example, that his home air conditioner is on a separate meter "and I enjoy lower rates whenever I use it in exchange for turning it off or down during certain hours when the utility calls for that."
The Internet of Things enables the combination of internal, external and vehicle data to provide new services, says Cavaretta.
"It could be that the vehicle is a mobile sensor platform connected to sensors in the road or in streetlights to monitor traffic conditions, weather conditions and energy usage. It could then communicate information to the vehicle and change the vehicle's behavior. The ability to take information embedded in sensors in the environment and then better understand how to modify the vehicle or optimize business processes is the real key," he says.
The bottom line: Big data is only going to get bigger at Ford, which has clearly tied its future to the power of analytics.