An analysis of production policy in U.S. Naval Aviation's Primary Flight training

Abstract

The United States (US) Navy struggles to sustain its ranks of aviators; it therefore seeks to produce more pilots, more quickly, without additional resources. This thesis employs the Architecting Innovative Enterprise Strategy (ARIES) framework, Factory Physics methodologies, and experimental models to investigate new policies, organizational structures, processes, and knowledge that support this imperative in the Navy's Primary Flight Training commands. It addresses promising changes to Primary and how to facilitate them. The ARIES framework, and associated stakeholder interviews, logically investigate the qualitative intricacies of Primary to illustrate its operation. Quantitative internal baseline methods suggest policies for student inventory management, student prioritization, and aircraft allocation. Each technique is tested by a joint discrete process and agent-based student model. This investigation suggests that Primary is challenged by an excessive student inventory and unclear operations policies. It asserts that these two factors create excessive wait time and resource-wasting rework that drastically reduce production performance. Experimentation results qualify the trends of these detriments and quantify their impacts on throughput and training time. The work concludes that a tightly governed start rate can be paired with three concurrent policies to raise average throughput by 62% and reduce average time to train by 52%. 1. Prioritize students by their total time in training to reduce the impacts of rework. 2. Allocate resources to the largest queues to increase peak performance and capacity. 3. Manage student inventory via a constant work in process (CONWIP) policy to reduce the impacts of rework and dampen sensitivity to resource variations. It also suggests minimally disruptive changes to Primary's architecture that aim to reduce organizational, knowledge, and process complexities while promoting sustainability, scalability, and evolvability in the enterprise. Four core concepts summarize the rearchitecting effort: 1. Employ data analytics in the current infrastructure to aid in decision making. 2. Balance organizational centralization to support flexible but consistent performance. 3. Consolidate and reinforce institutional knowledge in stable employees. 4. Promote knowledge sharing and coordination to improve organizational learning. This thesis asserts that application of these new policies and re-architecting concepts will promote production performance, organizational knowledge, and proactive management.