After incorporating in 1903, Ford Motors became the leading car manufacturing company by 1913 with the affordable Model T. A business which was considered the largest automobile company worldwide at a time was, however, in trouble in the year 2007. A loss of more than $10 billion- the largest in the history of Ford in the end of the financial year 2006, demanded forward-thinking innovation strategy from the company. And this is when the company embraced big data analytics- with a view to making it’s mammoth comeback.
It was just a matter of four years that the company finally started posting profits and launching new vehicles. In the same year, they sold more than 2 million cars- by becoming the only company to cross 2 million sales since 2007. In 2013, they were awarded the ’INFORMS’ Prize for their substantial efforts in data science and analytics. So what exactly did they do to bring themselves back from the brink of disaster to the forefront of the automobile industry, yet again? How did they harness big data analytics to gain even larger profits? Here’s how!
There’s no denying that the most important component of any manufacturing business depends on understanding the needs of customers and later finding a way to supply the products and services to them. According to a leading researcher scientist at Ford, the options and colors available in a specific dealership can have a big effect on sales and that it matters what’s available for sale on a car lot at a particular moment. The situation demanded Ford to get even smarter with its inventory. In the year 2007, Ford started in-house production of its Smart Inventory Management System (SIMS). This system enabled dealers to predict which vehicles customers would be interested in before they entered the lot, further allowing assembly and production plants to ship them after the manufacturing process.
Ford opened a lab in Silicon Valley to improve the quality of its vehicles. To improve in terms of emissions, fuel consumption, and safety, the company gathered data in real-time from more than 4 million cars with a remote application software and in-cars sensors. The data thus collected was leveraged in real-time by the engineers to solve issues and know the performance of the car in different weather and road conditions.
Another cutting-edge model known as ‘The Prototype Optimization’ was also introduced by the company. As a part of this program, the company can compute an optimal number of vehicles that can be then used to perform the maximum number of tests for insights. By leveraging this model, Ford succeeded in solving the problem that revolved around the cost of producing numerous prototypes with numerous variants. This model was used on the European Transit Vehicle for the first time and it saved Ford approximately $10 million.
A plug-in hybrid, The Fusion Energi generates around 24GB data per hour by streaming performance data from a specific car to the company. For Ford, big data means myriad things. From electric miles to gas miles and fuel information, every time a customer plugs in, Ford gathers data. With this, Ford has also partnered with Chalmers University, Sweden to generate a global energy model to anticipate and try the energy supply and demand of future.
A producer of self-learning neural networks and something it calls the ‘CO2 Glide Path’, Ford relies on several open source tools like Hadoop to manage and process data. In order to perform statistical analysis, it uses the programming language R. Other than these, it uses numerous open source applications associated with data-mining and text-mining.
Big data analytics is here, and Ford is sure making it count.