Name: Nathan L. Starchuk
Biographical notes:
Project Title: Modeling and Analysis of Medical
Products, Manufacturing Systems
In the
summer of 2009 I had the opportunity to work for a Canadian manufacturer and
distributor of medical products. I was hired to aid in the development of
automated equipment. During this time I decided to pursue a Masters of Science
in Engineering Management and chose a topic involving production systems and
automation to compliment my recent work experience. Fortunately, a positive
relationship was maintained with the company and they agreed to be involved in
the MSc project.
Production
systems, especially those that utilize automated equipment, are complicated
systems. Ignizio (2009) defines a factory as “a
nonlinear, dynamic, stochastic system with feedback.” Despite the complexity
noted in this definition, factory management has continued to rely on basic
methods, rules of thumb and subjective opinions. As a result many manufacturing
companies have decided to offshore or outsource their manufacturing in
developing nations in order to remain competitive. But as labor rates, fuel and
shipping costs rise companies are desperately looking for ways to improve the
efficiency of their operations. Many organizations are eager to adopt methods
such as lean manufacturing, six sigma, theory of constraints, re-engineering
etc. However, many organizations fail to achieve the benefits promised by these
methods. This research project involves the application of advanced methods to
model a real manufacturing system. Currently much of the assembly is manual but
automation projects are being actively pursued. Queuing theory and
discrete-event simulation will be used to develop a mathematical model of the
manufacturing system that accounts for process variability and stochastic
events. The model will be used to evaluate changes to the system such as
just-in-time production and the implementation of automated equipment. The
results will be used to optimize production efficiency and note the accuracy
and limits of this method.
Progress to date:
I have
recently completed all of the Engineering Management core course requirements
which have included topics in lean manufacturing and supply chain management.
During my last visit to the sponsoring organizations facilities data from a
production line was collected and a value stream map of the line was created.
This information is currently being statistically analyzed in order to model
the production line using Matlab’s Simevents software package. Changes to the model will implemented in order to predict the effects of a lean
manufacturing project scheduled for June 2010.
Expected completion
date: 2011