This late 2017 report has been professionally converted for accurate flowing-text e-book format reproduction.

To preserve national security, the United States Navy must continuously explore new technologies that can enhance warfighting capabilities, increase weapon system readiness and operate in a narrowing fiscal environment. The high cost of sustainment of military systems, coupled with extended life cycles, has compelled the Department of the Navy to find innovative ways to sustain in-service equipment. Additive manufacturing, also known as 3D printing, is one technology that demonstrates potential to provide novel warfighting capabilities and reduce sustainment costs of military weapon systems. But how can the Navy leverage the cost savings and lead-time reductions promised by additive manufacturing and simultaneously minimize the risks associated with a rapidly evolving technology? This thesis explores the technical and logistical factors necessary to identify applications of additive manufacturing for sustainment of in-service naval aviation equipment. The thesis introduces a component selection methodology to query the aviation spare-parts inventory for identification of additive manufacturing candidates. The methodology organizes the resultant data using a top-down approach that aligns technical feasibility with programmatic objectives. Finally, a discrete event simulation (DES) in Innoslate analyzes the data to provide engineers and logisticians with a decision-management framework to support the development of a business case for additive manufacturing.

I. INTRODUCTION * A. BACKGROUND * B. THESIS PURPOSE AND RESEARCH QUESTIONS * II. LITERATURE REVIEW * A. ADDITIVE MANUFACTURING (3D PRINTING) * B. ADDITIVE MANUFACTURING BUSINESS MODEL FRAMEWORK * C. SYSTEMS ENGINEERING AND ADDITIVE MANUFACTURING * 1. Spiral Model * 2. Bottom-Up Model * D. METHODS TO IDENTIFY AND PRIORITIZE AM APPLICATIONS * E. SIMULATION MODELS FOR AM COST EVALUATION * III. THE NAVAL SUPPLY SYSTEM * A. OVERVIEW * B. DOD AND NAVY AVIATION MAINTENANCE * C. ADDITIVE MANUFACTURING IN THE DON * D. IDENTIFICATION OF AM PARTS IN THE DOD SUPPLY CHAIN * IV. PROPOSED COMPONENT SELECTION METHODOLOGY * A. SCOPE OF RESEARCH EFFORT * B. OVERVIEW OF THE COMPONENT SELECTION METHODOLOGY * 1. Logistics Data Sources * 2. Technical Data Sources * 3. Data Collection for Analysis * 4. Material Delay (G-condition) * 5. Navy-Managed Spares and Organic Manufacture * C. APPLICATION OF THE METHODOLOGY * 1. Additional Assumptions * 2. Limitations of the Methodology * V. DISCRETE EVENT SIMULATION ANALYSIS * A. DATA AND COMPONENT SELECTION * B. MODEL FORMULATION * 1. General Assumptions * 2. Goals * 3. Model Parameters * C. SIMULATION RESULTS AND ANALYSIS * D. SENSITIVITY ANALYSIS * VI. CONCLUSION AND RECOMMENDATIONS * A. DISCUSSION * B. RECOMMENDATIONS * C. AREAS OF FUTURE RESEARCH