The Ford Marketing, Sales and Service Global Lifecycle Analytics team developed a range of models that help determine how to distribute cars to dealers and fleets, establish pricing and project residual values.
The Systems Analytics and Environmental Sciences group within Ford Research and Advanced Engineering harnesses the power of super-computing and advanced mathematics to mine big data, model market trends and optimize decisions.
In the U.S. market more than 90 percent of vehicle sales come from dealer stock and costs for carrying that inventory can often add up significantly. That makes it critically important for dealers to maintain the right mix of vehicles to maximize sales. Ford's Smart Inventory Management System, known as SIMS, analyzes historical sales and inventory data to generate recommended orders for dealers based on projected future inventory levels and targets.
Just as maintaining inventory is a major expense for dealers, stocking large quantities of parts is costly for factories. Just-in-time delivery of parts to assembly plants dramatically reduces manufacturing costs, and is one example of how Ford has used analytics to improve many facets of the production process, from the plant floor to component and vehicle logistics.
Ford developed the Just-in-time Execution & Distribution Information system, or JEDI, to help schedule the production and delivery of body panels from stamping plants to assembly facilities when they are needed. This minimizes premium shipping and overtime expenses when there is a mismatch between supply and demand for parts.
Optimization models developed at Ford also have helped the company understand projected consumer demands for fuel efficiency and mandated reductions in CO
emissions. "Working with these models has helped us shape our
Blueprint for Sustainability
and determine where to focus our engineering resources for the most impact," Ginder said.
One component of the Blueprint for Sustainability, the overarching framework that guides Ford's product, operational and social sustainability planning, is its CO
stabilization target glidepath. Ford calculated the glidepath required to meet future CO
emissions targets and projected the costs for various technologies – including diesel, hybrid and plug-in electric and hydrogen fuel cells – over the next two decades, then developed a strategy to meet those targets. In the near term, reducing weight and downsizing engines provides the biggest overall impact on CO
emissions for the most customers at the best value. The data suggest that as technology improves and costs come down over time, consumer interest is likely to shift more toward plug-in electric vehicles and potentially hydrogen fuel cells.
In recent years Ford's analytics initiatives have begun to incorporate big data and apply them to developing new vehicles.
"Social media and the vast amount of online conversation is helping us get a faster and more specific data set to help us make product decisions. 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
, Ford technical leader for predictive analytics and data mining.
While developing the all-new 2013 Ford Escape, the vehicle engineering team made tens of thousands of decisions such as determining the liftgate configuration. Extensive analysis of customer satisfaction data was used to decide whether to retain the flip-glass system from the previous-generation Escape, adopt a power liftgate, or both.