An Operational Value Stream Analysis for Developmental Excellence

Abstract

The aerospace and defense industry faces increasing challenges in new product development, where financial constraints and risk aversion hinder innovation. Using a multidisciplinary approach that integrates contract theory, computational fluid dynamics (CFD), and machine learning, this research explores the impacts of engineering requirements, financial alignment among stakeholders, and improved efficiencies in predictive modeling techniques for two separate air vehicle programs: A and B. A Monte Carlo analysis using SEER-H estimation software quantifies the financial and schedule impacts of engineering requirements, revealing a 10–30% cost increase due to volatility in air vehicle development design parameters. Moreover, a game-theoretic contract negotiation simulation illustrates the importance and opportunity of financial incentive alignment among key stakeholders. Additionally, predictive analytics leveraging machine learning models better capture the relevant flow mechanics, improving the circumferential distortion estimations in nacelle aerodynamics by over 10% compared to traditional heuristics. Finally, a CFD-based actuator disk source modeling approach demonstrates a 60% reduction in steady-state distortion at some portions of the flight envelope, due to the impact of the fan upstream influence on inlet flow distortion suggesting increased operational capability for the air vehicle program B. This research provides actionable recommendations to enhance the operational value stream of new air vehicle program development, emphasizing the need for pre-RFP requirements validation, advanced machine learning applications for predictive engineering, and refined CFD modeling to identify technical risks earlier in the design process.