Charting the course: standardization of quality assurance in digital twin applications across product lifecycle

Abstract

Digital twins hold immense promise in accelerating scientific discovery and revolutionizing industries. The use cases for digital twins are diverse and proliferating, with applications across multiple areas of science, technology, and society, and their potential is wide-reaching. Despite the growing use of quality assurance approaches, standards, and frameworks in digital twins, the promise of digital twin benefits remains more aspirational than reality. Consequently, the confidence level in the prediction of these models is questionable. There is a lack of guidance on establishing standardization and interoperability as a foundation for integration of platforms, systems, and stakeholders within the digital twin ecosystem. To address this gap, we propose guidance for standardized quality assurance of digital twins in simulating product lifecycles, aiming to enhance regulatory decision-making. This proposed guidance is a step towards development of framework for standardized quality assurance of digital twins.