In 1913, the Ford Motor Company was at the forefront of car manufacture. Designing the reasonably-priced Model T to appeal to the masses and employing division of labour & moblised assembly lines in the factory made Ford the largest automobile factory in the world at that time.
In 2007, the Ford Motor Company was in trouble. The end of 2006 financial year brought with it reports of a $12.6 billion loss, the largest in the company’s history. Yet, once again, forward-thinking innovation- this time, in the form of data analytics- led Ford back to the path of prosperity. By 2009, Ford was posting profits for the first time in 4 years, as well as launching 25 new vehicle lines. The same year they sold 2.3 million cars, the only company to exceed the 2 million mark since 2007. Last year, they won the the 2013 INFORMS Prize for Company-Wide Efforts in Analytics & Data Science. So how exactly did Ford harness data analytics to bring them back from the brink and thrust them once again to the forefront of their industry?
An essential component of any successful manufacturing business is having your finger on the pulse of what your consumers really want, and an efficient way of supplying it to them. As Ford research scientist Bryan Goodman explains, “Quite a few customers walk into a dealership and want to leave with a vehicle that day… We have to get the right vehicle with the right engine and right set of features and controls to the right dealerships.” Ford’s solution to this is its Smart Inventory Management System, or SIMS. SIMS integrates a range of data, including data on what is being built and sold; data on what’s being sold relative to what other models are in the inventory; data on what specs are being searched for on the company website; and data on the housing prices and employment rates in dealership locations. By manipulating all of this data, SIMS ensures that dealership stock and car specifications are optimised to consumer demand.
The models produced using SIMS data are fine-tuned to the tiniest detail. “When you get into different roof heights, different interiors, different wheels and so on, we can offer an astronomically large number of combinations,” Goodman states. “Imagine 300 billion and then multiply it by itself again.”
Ford also began mining social media posts to gain greater insight into public opinion of their models. ‘We now use text-mining algorithms to formulate a more complete picture of what consumers want that is not available using traditional market research,’ said Michael Cavaretta, Ford technical leader for predictive analytics and data mining. Analysis of this kind was used to make decisions on last year’s Ford Escape, such as whether it should feature the flip-glass system of the previous model, or a new power liftgate.