P7UC2022 - Enabling Digital Transformation in Engineering: Trends and Challenges

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Authors:

Donald Tolle, Director, Simulation-Driven Systems Development Practice

Journal / Event:

pSeven User Conference 2022

Abstract:

Digital transformation has become both a major business driver as well as a source of great angst within the product engineering, manufacturing and in-service operations of global enterprises. The need to connect digital models and associated engineering data of today’s complex cyber-physical systems is now seen as a key enabler in achieving the widely promised business benefits of implementing model-based engineering/digital engineering throughout the product lifecycle (i.e., connecting the digital thread). Dealing with the design complexity of cyber-physical products and smart, interconnected systems of systems is driving the need for increased use of advanced digital modeling, performance simulation, and multi-disciplinary analysis and optimization (MDAO) augmented with AI/Machine Learning technologies. - Engineers can now define and apply multi-disciplinary digital twins leveraging MDAO starting earlier in the conceptual design and development phase as well as apply these technologies in manufacturing and in operations to enable combined physics-based and ML-based “hybrid digital twins”. - Industry leaders are increasing the use of MDAO, Cloud and “low code” automation environments to enable design innovation; reduce overall product design costs, minimize rework and manufacturing defects, reduce maintenance cycle times, and optimize in-service operational performance and costs. - The “democratization” of physics-based modeling, performance simulation, design optimization, and data analytics best practices throughout the enterprise is a key enabler of digital transformation initiatives In this presentation, CIMdata will provide a high level overview of the key industry trends and business challenges related to the implementation of digital engineering technologies.

Keywords: P7UC2022, Digital Twin, Automotive, Industrial Equipment