A Leveling Production Strategy To Automotive Assembly Using Queuing Systems Software Through Simio Simulation

The automotive manufacturing industry is challenged with an unpredictable costumer demand with considerable fluctuation. In addition, the number of products (vehicles) and the complexity of them are constantly growing. These characteristics make very difficult to plan and schedule production. This Thesis presents a methodology to model with a high degree of accuracy the production floor, warehouse and material handling system of an automotive assembly facility through Simio simulation software; resulting in a dynamic model that combines all the critical variables of the system. The presented methodology includes an algorithm developed with the goal of modeling the automotive facility Kanban system. A hypothetical case study of an automotive manufacturing assembly plant is used as an example to show the method. In order to plan the production batch size, a Heijunka analysis was conducted. The presented model could be used by the automotive manufacturing assembly industry as a tool to develop the planning and scheduling strategy