There’s no doubt that making cars is an art. Automotive design, which still involves much sketching, sculpting and esthetic appeal is an important factor in the success of the car industry. But today’s automakers also know that it’s a science. And the effective use of big data is becoming an increasingly important aspect of their business. In fact, consulting firm McKinsey says automotive data could have a global value of $450 to $750 billion by the year 2030. With billions of dollars at stake, in an incredibly competitive industry, these companies are looking for any edge they can find. And often, the best place to find one is in the billions of data points that the auto companies are collecting right now on a regular basis.
Whether it’s customer-focus groups or digital data streamed directly from a vehicle, automakers are managing an increasingly wide range of metrics that, at this stage, covers almost every aspect of the auto industry—from manufacturing to sales to on-the-road performance. It’s this expansive reach that makes so many of the big-data solutions for automakers also applicable to other fields.
Eliminating Weak Links from the Supply Chain
The foundation of a strong auto company is its manufacturing operations, and for that, an efficient, reliable supply chain is vital. An auto plant relies on parts and materials—and suppliers—drawn from all over the world. Getting the right stuff in the right place at the right time is what enables them to build the cars in the first place.
Automakers use big data from dealer management systems (DMS), customer relationship management (CRM) and customer-satisfaction surveys. They gather metrics on customer inquiries, sales, inventory levels and even customer order patterns on various models, trims and colors to understand what content should be included in each vehicle to make it most appealing. This information is integrated with data regarding international weather patterns, the availability of raw materials, and more, to ensure suppliers can properly keep the assembly line rolling. Risk-management analytics are helping to reduce possible supply chain disruptions, too, as the ability to gather real-time data across the supply chain and into dealerships is helping managers be more proactive in their decision making. The latest predictive models even claim they can “help identify part defects among suppliers in advance.”
Developing Better Cars Faster
Designing and engineering new cars and trucks remains a years-long process that’s rife with opportunities for wasting both time and money. Consider: A model in development may have a certain feature used in an automaker’s current products, say a kind of power-window button. But an auto company that’s monitoring data from the industry customer-satisfaction studies, its dealership service departments, and its own internal research can recognize concerns with that button in the numbers. In such a case, the manufacturer can stop the new model’s development earlier in the process and resolve it as inexpensively and as soon as possible. Such data analysis can inform the operational process, which allows for more accurate material procurement, manpower planning and real-time vendor coordination.
Of course, automakers also rely on a massive amount of engineering data when designing new cars and trucks. Vehicles are complex systems made up of other complex systems, and to achieve a goal such as increasing fuel economy can require data from wind-tunnel testing for aerodynamics, the study of the rolling resistance of the tires, and powertrain evaluations. Automakers then draw crucial information from each data bucket, while also searching for relationships between data sets that can be leveraged.
Informing the Future
Automotive analytics are also helping the auto industry prepare the cars of the future. For instance, the Center for Automotive Research (CAR) collected data to assess the cost and effectiveness of powertrain technologies developed to meet standards in fuel economy and greenhouse gas emissions. The research found that when it came to the cost of meeting those standards, there is a significant discrepancy between what automakers and regulators project it to be. It also found that manufacturers believed that regulators over-estimated the efficiency gains from the required powertrain technologies. This data is not only a tool in helping car manufacturers prepare for these regulations, it also may better inform regulators and policymakers as they make decisions going forward, which could benefit automakers in the long run.
Big data is also an obvious significant enabler of the latest advances in autonomous driving: The technology behind self-driving cars “learns” to keep occupants safe through rigorous modeling and analysis of real-world conditions, as well as by comparing that to driver information collected by the automakers over time. Needless to say, an incredible flow of data has to be parsed and acted upon. Some experts claim that autonomous cars can produce up to 1 gigabyte of data every second, capturing information about a vehicle’s speed, whether its lights or wipers are on, how often the brakes are being applied, and other clues to traffic conditions. Indeed, Tesla Motors has used data to determine how fast a car was traveling before a crash, and whether the semi-autonomous driving system was engaged at the time of impact, according to the AP. Further, the whole concept behind the connected car is that the vehicles will share real-time data on the road, which can increase the stream of information exponentially. And that’s part of the reason why autonomous driving remains a work in progress.
Making Marketing Work
If you’ve spent any time watching TV or surfing the web, you know automotive marketing is a huge business of its own. It’s also becoming more focused and effective than ever, thanks to the growth of marketing mix analytics. By comprehensive evaluation of customer responses and internal business operations, this branch of big data is helping automakers balance the use of incentives, rebates, financing deals and other attractors in their marketing efforts, all as they optimize backroom efficiencies. One concrete trend recently identified in a Deloitte study for Automotive News is a shift to treating “households” as the consumer unit instead of individual customers–since this better reflects the role of a car in many family settings. Bolstering this shift: Data indicating how a vehicle’s life cycle can be repeated by a second generation in the same family as new drivers come of age.
A final detail from the Deloitte study that is worth calling out for all data users, not just those from the auto industry: Savvy business owners have been using statistics to increase their sales for decades. What’s new about big data is how big it is. Computer power gives people access to so much information that it can be easy to lose the signal for all the noise. Thus, Deloitte indicates that today’s successful automakers are favoring a new management style that specifically considers the intricacies of data analysis, with leaders who have a broad view of the business and recognize the need to acquire relevant data from across an enterprise, and support an “ecosystem” that’s wholly committed to analytics.