In this chapter we discuss the use of computer simulation in design and operation of car and truck assembly plants as well as automotive components manufacturing plants.
Most of the automotive manufacturers worldwide and, in particular, the big three U.S.-based companies (General Motors Corporation, Ford Motor Company, and Chrysler Corporation) currently require that all new and modified manufacturing system designs be verified by big three automotive companies that any new equipment purchase or manufacturing line modification costing more than several million dollars should be verified by simulation modeling before approval. Studies performed in the past are indicators of how useful simulation could be in the design and operation of production systems of all kinds, including vehicle manufacturing. Examples can be found in refs. 1 to 8.
In what follows, we discuss mainly the applications of discrete-event simulation in the automotive industry, with some discussion of the emerging role of robotics simulation. Applications of discrete-event simulation in the design and operation of vehicle manufacturing systems can be categorized in two different ways. The first classification is based on the stage of the development of the design of the system. Four categories are observed in this classification: conceptual design phase, detailed design phase, launching phase, and fully operational phase. The conceptual phase refers to the initial stage where new methods of manufacturing and material handling are tested by the engineers.
Discrete-event simulation packages with three-dimensional animation capabilities
are the popular simulation tools at this phase. The detailed design phase refers to the stage where detailed layout plans and equipment specifications are verified for the system. The principal factors considered here include equipment justifications (e.g., the number of hold tables, power and free carriers, the size of buffers), cycle-time verifications (e.g., conveyor speeds, line throughput), and line operational and scheduling issues (e.g., logic for evacuating ovens and paint booths, repairs, and product mix decisions).
Source: The Pennsylvania State University
Author: Onur Ulgen , Ali Gunal