Name: Nathan L. Starchuk


Biographical notes:


  • BSc in Mechanical Engineering from the University of Alberta, 2009.
  • NSERC Canada Graduate Scholarship 2009
  • Worked in the oil and gas industry during most summers.
  • Spent one summer and final year of undergraduate studies as a research assistant working on flame sprayed solid oxide fuel cells.


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


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