Modeling and analysis applications in manufacturing system design and development
This thesis demonstrates how modeling and analysis tools, particularly simulation tools, can be used to facilitate the design of a manufacturing system. The study is based on visits performed at eight large US manufacturers during the summer 1994. It reflects on the practices encountered at these companies for the design of plants, on the tools identified and examined which help this design process, and on methodologies and policies that contribute to an efficient use of modeling tools. The main types of modeling tools identified are described in a high level way. Their benefits are stressed in light of the fundamental objectives of the development process as observed at the participating companies: risk control, time and cost reduction, quality and flexibility achievment. A particular emphasis is given to dynamic tools, or simulation. It is shown that two types of simulation can be performed and lead to different benefits: simulation for predictions or simulation for insight into a system. A model development process methodology is presented, along with recommendations on how to involve all key players in the model building phase: system people, model builders and decision makers. It is argued that this practice greatly improves the usefulness of the model, its acceptance and the learning associated with it. Characteristics of a useful model, and selection criteria for commercial tools are then examined. The fundamental conclusion drawn from the practices of the companies visited is that leverage can be achieved through more communication between model builders, and more interactions between models. It is recommended that harmonization of tools across divisions be aimed at, and that corporate standards be set for modeling tools. Integration of tools used for complementary analyses is also encouraged, and a method to facilitate it is presented. Communication through users' groups or online is suggested as a way to promote the use of modeling. Thesis Supervisor: Professor Kevin Otto Title: Assistant Professor of Mechanical Engineering