This graduate research project evaluated whether additive manufacturing could economically supplement or replace traditional inventory strategies for aerospace spare parts. The objective was to identify the demand, cost, and operating conditions under which on-demand production could improve service while reducing inventory exposure.

Challenge
Aerospace spare parts often combine low and irregular demand with high carrying costs, long replenishment times, and significant consequences when critical parts are unavailable. Additive manufacturing offers the potential to produce selected parts on demand, but its value depends on machine capacity, production lead time, labor, demand variability, and late-order penalties.

Approach
- Built a discrete-event simulation of an additive manufacturing operation.
- Modeled spare-parts demand, machine availability, processing times, batching, operator activity, and order due dates.
- Developed a benchmark model representing a conventional inventory-based supply chain.
- Compared make-to-stock and on-demand production strategies across multiple operating scenarios.
- Evaluated the effects of demand volume, variability, machine capacity, labor, inventory cost, and delayed fulfillment.
- Conducted sensitivity analysis to identify the conditions most strongly influencing the preferred strategy.

Outcome
The analysis found that additive manufacturing was most promising for lower-volume aerospace spare parts with high inventory carrying costs and meaningful penalties associated with delayed availability.

The research also showed that additional machine capacity could create greater value than additional labor and that dynamic order prioritization improved performance when due dates and service consequences varied.

The project demonstrated that additive manufacturing is best treated as a targeted supply-chain strategy rather than a universal replacement for stocked inventory.

Recognition
Recipient of the 2021 Graduate Research Award from the Institute of Industrial and Systems Engineers.