Optimizing Architecture Transitions Using Decision Networks

Abstract

Architecture selection for systems undergoing rapid technological and market change is challenging. It is desirable to select architectures that can provide cost-effective possibilities for future changes and avoid architecture lock-in. However, optimal architectures for prevailing conditions may not be changeable for future adaptation. This tension between objectives for system (product) development for both short-term and long-term competitiveness has been an enduring challenge for system architects. Here, we use time-expanded decision networks (TDNs) with time-varying costs and demands to systematically explore future architecture transition pathways and strategically identify useful designs. We demonstrate a new application for autonomous driving (AD) systems, a nascent technology, where the design and capabilities of constituent components (such as sensors, processors, and data communication links) are still evolving and significant market and regulatory uncertainties persist. In this case, we model technology costs with time-based factors to explicitly include future trends. The results show that as cost differences between architectures increase and demand for new functionality changes with time, the approach is able to identify potential transition points between architecture choices that optimize the net present value (NPV) of the system. For some of the specific scenarios analyzed in this study, the NPV with optimal architecture transitions is at least 10–20% larger as compared with fixed cases. Overall, this work presents a case for planning and partly constructing architecture transition roadmaps for new systems wherein dominant architectures have not emerged.