Optimizing Prototype Vehicle Testing at Ford Motor Company
The Problem
The prototype vehicles that Ford Motor Company uses to verify new designs are a major annual investment. Ford must construct prototype vehicles so that it can examine the interactions of systems in their operating environments. But the cost of building a prototype routinely exceeds $250,000, while complex vehicle programs commonly require over 100 full-vehicle prototypes and sometimes require over 200 in the course of product development. Using operations research and analytics to construct models capable of generating numerous iterations of possible solutions would allow the company to identify the most efficient use of the prototypes, thus cutting costs while still meeting demands for high quality.
The Analytics Solution
A team of engineering/operations research managers in a Wayne State University program taught for Ford noted that prototypes sit idle much of the time waiting for various tests, so increasing their usage was a clear path to cost savings. The barrier to sharing these idle prototypes among design groups lay in determining an optimal set of vehicles that could be used to satisfy all the testing needs. Ford and the Wayne State team developed what they called a Prototype Optimization Model (POM) to reduce the number of prototype vehicles Ford needed to verify the designs of its vehicles and perform necessary tests.
The Value
Ford uses POM and its related expert systems to budget, plan, and manage prototype test fleets and to maintain testing integrity, reducing annual prototype costs by more than $250 million. POM's first use on the European Transit vehicle reduced costs by an estimated $12 million. The model dramatically shortened the planning process, established global procedures, and created a common structure for dialogue between budgeting and engineering.
From its modest beginnings as a student project, POM eventually became an integral part of Ford's tactical and strategic planning of product development. Along the way, the team cut the time for planning and created new processes that have become global standards. The POM project also opened the door for other working-student teams to bring O.R. and analytics techniques to bear on a wide array of critical decisions and processes at Ford.
