By Topic

Manufacturing modeling and optimization

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
D. S. O'Ferrell ; Siltec Corp., Salem, OR, USA

The purpose of this project was to model and optimize the personnel and equipment utilization in Siltec's Epitaxial manufacturing process. Previous attempts to model the behavior of the process through static models (linear programming and spreadsheets) had not attempted to explain any of the variability experienced in the process line. SIMAN was used to create a simple model to study the effects of crosstraining on productivity and cycle time. The model was validated using actual production data from Siltec's production line. The programming of the model was verified by comparing "boundary values" with expected behavior. The model was used to predict production volumes given various absence rates and crosstraining levels. Additional experiments investigated the effects of Kanban size, equipment failure rates, operator staffing levels, and equipment capacity increases on operator staffing requirements, production throughput and WIP, and cycle time. During periods of normal operator absence (10%), productivity is improved by about 10% and cycle time is improved by about 50% if all operators are fully crosstrained. During periods of high operator absence (20%), productivity is improved by about 30% and cycle time is improved by about 50% if all operators are fully crosstrained. In all cases, equipment utilization is improved with increased crosstraining. Additional experiments allowed determination of required headcount, equipment additions, and Kanban size for optimized production throughput, WIP, and cycle time. The general conclusion of this project is an affirmation of expected behavior. Increasing crosstraining will improve productivity, especially during periods of high operator absence. Increasing Kanban size will increase throughput minimally while increasing WIP and cycle time considerably. Moderate increases in capacity at bottlenecks will result in dramatic increases in throughput. The model has been and will continue to be used to make qualitative and quantitative decisions concerning headcount, resource allocation, and expansion plans.

Published in:

Advanced Semiconductor Manufacturing Conference and Workshop, 1995. ASMC 95 Proceedings. IEEE/SEMI 1995

Date of Conference:

13-15 Nov 1995