A Model-Based Systems Engineering Approach to Product Value Optimization Among Disparate Criteria

Abstract

Model-based systems engineering (MBSE) is a popular approach for managing the technical aspects of large, complex systems. It provides a disciplined framework for capturing emergent properties and interactions early in the system life cycle, while still allowing for creative freedom at the component level. Capturing these properties is critical on large projects, but its value is often unrecognized on smaller projects. This thesis demonstrates that organizations of any size can benefit from incorporating MBSE into their development process. Specifically, product design needs from case studies in two different industries are processed concurrently using an MBSE approach to develop a single system capable of meeting both case study's needs. The resultant system not only meets the organizations' needs, but exhibits emergent properties that make it valuable in other potential applications. The first case study is a heating, ventilation, and air-conditioning (HVAC) manufacturer seeking to reduce its shipping costs through improved product design. The second case study is the National Aeronautics and Space Administration (NASA) seeking to determine the optimal contents of spaceflight medical kits based on multiple figures of merit. The thesis begins with a model-based needs analysis and system architecture design process, revealing a single, emergent, optimization algorithm that is applicable in many domains. Then, a model-based product realization and verification are conducted, resulting in fully functional optimization tools and applicable documentation for the two case study organizations. The demonstration of MBSE providing value to small-scale projects is the primary result of this study. Secondary results emerging from the process show that including shipping costs in product design can result in a lower life cycle cost, that including environmental figures of merit in spaceflight medical kit development can increase the value of the medical kit, and that multi-attribute value theory can be effectively applied in an automated optimization.