In the automotive industry there is a continual drive to reduce cost and improve processing flexibility in the assembly plants. The competitive market place demands flexible and common processes to help reduce assembly costs and improve build quality. The selection of these common processes requires experience based knowledge to pick the best practice among a set of alternatives. This paper demonstrates how math based manufacturing analysis can be used to help in this selection process.
Within General Motors, the BOP (Bill Of Process) represents a globally templated approach to manufacturing and assembling vehicles. All vehicle programs are required to follow the common BOP. Broadly defined, the BOP is a set of common processing standards relating to datum number and selection, number of stations required to build a sub assembly, etc.
Full implementation of the BOP allows GM to leverage resources globally, reduce manufacturing costs, and shortens the feedback cycle in our learning organization.
As GM converges towards a common BOP numerous debates arise as to the suitability and selection of various processing methods. This report is an attempt to use systematic, math based, deterministic and stochastic FEA (Finite Element Analysis) methods to guide BOP development.