I was awarded the 2021 Graduate Research Award by the Institute of Industrial and Systems Engineering (IISE) for my graduate thesis exploring the integration of additive manufacturing (AM) in the aerospace spare parts supply chains. My research aimed to evaluate how AM, with the ability to produce parts on-demand, could either supplement or entirely replace the traditional production and inventory management solution.

Key Objectives:
The project primarily focused on simulating the system dynamics of AM production. To achieve this, I developed a discrete event simulation (DES) model that included the microscopic operational aspects of AM, such as machine rates and build capacity as a component of part geometry, job batching, post-processing rates, operator functions, and part demand.

Additionally, I developed a benchmark warehouse inventory model based on established theories to serve as a basis for comparison. These two models enabled me to conduct a comprehensive cost/benefit analysis comparing AM-based part supply strategies against the traditional approach. I examined various production policies including machine/operator configurations and part prioritization, and ran a sensitivity analysis of the key assumptions. The analysis revealed insights into operational characteristics such as production cost, system utilization, and lead time.

Results:
The data analysis highlighted cost savings associated with employing AM on-demand, particularly in scenarios with high penalties for delayed orders. Furthermore, the results suggested that investing in additional machines rather than operators could lead to higher cost savings to meet capacity demands.

Lastly, the analysis underscored the significance of order prioritization in queue management, showcasing higher savings when considering due dates and penalty outcomes. This finding emphasizes the potential benefits of dynamic order management strategies within AM-integrated supply chains.