The advent of improved factory data collection offers a prime opportunity to continuously study and optimize factory operations. Although manufacturing optimization tools can be considered mainstream technology, most U.S. manufacturers do not take full advantage of such technology because of the time-intensive procedures required to manually develop models, deal with factory data acquisition problems, and resolve the incompatibility of factory and optimization data representations. Therefore, automated data acquisition, automated generation of production models, and the automated integration of data into the production models are required for any optimization analysis to be timely and cost effective. In this paper, we develop a system methodology and software framework for the optimization of production systems in a more efficient manner towards the goal of fully automated optimization. The case study of an automotive casting operation shows that a highly integrated approach enables the modeling and simulation of the complex casting operation in a responsive, cost-effective and exacting nature. Technology gaps and interim strategies will be discussed.
Proceedings Title: Proceedings of ASME 2013 International Mechanical Engineering Congress & Exposition
Conference Dates: November 11-15, 2013
Conference Location: San Diego, CA
Pub Type: Conferences
Optimization, discrete event simulation, modeling, automation, CMSD, key performance indicators