What is Digital Engineering?
Digital engineering is an integrated digital approach that uses authoritative sources of systems data and models as a continuum across disciplines to support lifecycle activities from concept through disposal. It includes the procedures, tools, and overall process of developing new digital products from conception to completion, utilizing technology and data to enhance existing apps or create new ones.
Digital models are created that represent all of the characteristics of a complex product or system by using digital data and connectivity. These models allow for data integration across various aspects.
Digital Engineering has been widely adopted in aerospace, defense, automotive, and process manufacturing to optimize product development processes, reduce costs, and accelerate innovation. It transforms how hardware-intensive systems are built and acquired by emphasizing digital modeling techniques.
Digital Engineering is continually evolving, and several key trends are shaping its future. These are some upcoming trends in Digital Engineering;
Cloud-Native Adoption
Cloud-Native adoption is a key trend in digital engineering, with over 95% of new digital workloads projected to be implemented on cloud-native platforms by 2025, up from just 30% in 2021. Cloud-native architecture enables businesses to enhance application performance and resilience through scalability and agility.
It improves time to market through accelerated application lifecycle and shorter release cycles. It reduces total cost of ownership (TCO) due to open-source technologies, cloud backbone, and efficient scalability.
Cloud-Native adoption has transformed digital engineering by enabling organizations to modernize the applications that use public, private, and hybrid clouds. It empowers businesses to build and run scalable applications in modern, dynamic environments using technologies like containers, microservices, and CI/CD pipelines.
Generative AI & AI Integration
Businesses are set to implement AI strategies in 2024, focusing on the execution and integration of diverse AI forms like generative AI, text, code, images, audio, video, computer vision, and natural language processing. Integrating these AI forms will drive innovation and improve customer experience and operational efficiency.
Generative AI allows engineers to create various designs within set parameters. Generative AI can produce multiple design options using complex algorithms by defining the metrics. This enables precise testing for specific needs, enabling more innovative products and measuring design effectiveness before manufacturing.
AI integration is a strategic driver of business value, accelerating product development, reducing costs, boosting productivity, and managing risks. It is reshaping industries and pushing progress by complementing human capabilities. Overall, generative AI and AI integration are transformative technologies in digital engineering, enabling more efficient, innovative, and user-centric product development.
Microservices Expansion
Microservices and containerization are gaining momentum for Agile development. Microservices are a key trend driving digital transformation in engineering and other industries. Microservices represent an architectural approach where large applications are broken down into smaller, independent services that can be developed, deployed, and scaled individually.
Microservices allow engineering teams to develop, test, and deploy new features and updates faster than the traditional monolithic architectures. This enables greater agility and responsiveness to changing customer/market demands. Microservices are often paired with cloud-native technologies like containers and Kubernetes, which enhance scalability, portability, and automation.
Composable platforms built on a microservices-based, API-first, headless, cloud-native (MACH) architecture are emerging as the foundation for transforming traditional business models into dynamic digital applications.
Edge Computing
Edge computing in digital engineering refers to processing and analyzing data closer to the source of data generation, rather than in a centralized data center or cloud. This is a key trend in digital engineering, as it helps address the challenges posed by the exponential growth of data, especially from IoT devices and sensors used in digital engineering applications.
It enables faster data processing and real-time decision-making by reducing latency, as data does not have to be sent to a remote data center for analysis. It improves reliability and resilience, as edge devices can continue operating even if the connection to the central cloud is disrupted.
Edge computing is expected to pick up steam in 2024, with smaller large language models powering smarter edge devices. Use cases include predictive maintenance, real-time data utilization for self-repair or reconfiguration of machines, and decentralized quality control.
Digital Twins
It is an advanced digital twin technology for creating highly accurate virtual replicas of physical systems. Combining digital twins with AR/VR, AI, and IoT will enrich visualization and provide a more comprehensive understanding.
Generative AI techniques can help digital twins accurately simulate and predict behavior, enabling innovative ways of synthetic data generation. They allow real-time monitoring, diagnostics, and prognostics, improving operational efficiency and predictive maintenance.
Synthetic Data Generation
It is a key technique in digital engineering that involves creating artificial data to mimic the characteristics of real-world data, without using any actual real data. It is generated using algorithms and statistical models to make realistic datasets that can be used for testing, training AI models, and other applications in digital engineering.
Synthetic data generation is becoming increasingly important in digital engineering as it provides significant benefits like data quality, privacy, and the ability to generate diverse datasets for testing and AI development.
Intelligent Automation
Intelligent automation is a key trend in digital engineering that involves integrating various automation technologies like artificial intelligence (AI), machine learning (ML), robotic process automation (RPA), and business process management (BPM) to streamline and scale decision-making across organizations.
It simplifies complex processes by automating repetitive, mundane tasks using a combination of these advanced technologies. It helps free up resources, improve operational efficiencies, and enhance the customer experience across industries. In digital engineering,
intelligent automation is used to automate tasks like data analysis, predictive modeling, laboratory workflows, clinical trial processes, and more.
In summary, digital engineering combines technical aspects with cultural behaviors, driving efficiency and innovation throughout a product’s lifecycle.
These trends reflect the ongoing evolution of digital engineering, driven by technological advancements and the need for more efficient, secure, and sustainable solutions. Other notable trends include optimizing cloud costs, leveraging 5G networks, app modernization with generative AI, and sustainable manufacturing practices.
Digital engineering has revolutionized industries by enabling more efficient, sustainable, and innovative solutions integrating digital technologies and traditional engineering practices. It is a field of product engineering that takes a holistic approach to designing complex systems.
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