AWS for Industries

Understanding digital engineering and how it drives business value


Engineered systems such as aircrafts, engines, launch vehicles, satellites, automobiles, refineries, and wind turbines are complex, regulated, and quality-driven, and their development is expensive in terms of both cost and schedule. Businesses are adopting transformational concepts, such as digital engineering and digital twins to drive business outcomes around improved efficiency, agility, and quality.

Commonly used terminology such as digital engineering, digital twins, digital threads, and model-based systems engineering have broad definitions and can cause confusion. What is the difference between them and how can they benefit you? Let’s break down each term to prepare you for early stage customer discussions, so you can define business outcomes and arrive at solutions more quickly.

We define digital engineering as the deployment of information technologies and computational infrastructure to enable the workflows used during the design, manufacturing, and operation of complex equipment. These digital engineering workflows are enabled by bringing together the data, models, tools, and processes used by multiple teams, disciplines, and organizations into a common framework This helps organizations achieve their business goals of improved delivery timelines, cost performance, and quality. The cloud is the key enabler for digital engineering as it offers scalable on-demand resources, connectivity and permission-based sharing to foster collaboration across organizations, virtually unlimited storage, and analytic services to unlock new insights in a reliable, durable and cost-effective manner.

At a high-level, the asset life cycle represents the phases of a physical item from design through to decommissioning. A wide variety and large volume of data is generated throughout the asset’s lifetime. The ability to store and query this disparate data with a digital thread is a core need for digital design, digital supply chain & manufacturing, and digital twins which make up the key areas of digital engineering. In practice, the lines between the boxes are blurred as there is constant feedback between each of the phases, which we’ve represented with the circular arrows in Figure 1.

In the next few sections, we describe each of the main concepts identified in the Figure 1.

Asset Life Cycle

Engineering customers explain their digital engineering use cases relative to the asset life cycle, which helps frame the discussion around which technologies, solutions and software can be deployed. Historically, the solutions were often bespoke for each phase with loose integrations between phases; however, tighter integration across the different phases can help.

We propose to simplify the life cycle to three phases: 1/ Design; 2/ Build; 3/ Operate. These phases can represent a closed-loop where lessons from the operate phase are used to inform the next iteration of the design.

The Design Phase includes the activities necessary to define requirements, design, test, and validate the design prior to starting production of the physical system or process. This includes technical requirements, engineering analysis, simulations, software development, as well as building and testing prototypes.

The Build Phase includes the activities necessary to implement the design and create the physical system to achieve an operational state. This includes component manufacturing, procurement, supply chain management, assembly, system integration and testing, transportation to the deployment site (if applicable), and quality tests along the way.

The Operate Phase includes the activities related to operating, planning, and maintaining the physical system during its in-service life, which can span several decades. Activities can include day-to-day operational planning, alarms management, data processing, and preventative maintenance.

Engineering customer success is measured by cost optimization and profitability throughout this entire process. Success during each of these phases is dependent on multiple teams representing different disciplines working together, but using differing tools and creating unique data products. Today, these teams, tools, and data are often isolated from each other. Design escapes, requirement changes, or new information frequently cause teams to modify work they already completed. Engineers must also ensure changes are shared with other teams and tools to avoid disconnects causing more costly issues down the line. To combat these challenges, customers see the potential of cloud computing can help tie together the data and models generated in each of the phases to make them more readily available across their enterprise.

Digital thread

A digital thread is the infrastructure and framework that enables the integration and federated sharing of data among different tools, systems, and organizations spanning an asset’s life cycle, while maintaining robust permissions management. In this context, the data can be highly diverse including structured and unstructured data. This includes business case analyses in spreadsheets, product requirements in pdfs, engineering simulation models and results in native formats, logs, numerical machine instructions for manufacturing, IoT time series data, and handwritten maintenance reports, etc. The data might be stored in a wide variety of locations such as on-premise systems, edge devices, AWS data lakes, or a hybrid combination.

A key desire is the ability to fuse the data from all these data sources and the variety of data formats, communication protocols, and security requirements in near- or real-time with auditability and traceability. In addition, the digital thread can span multiple programs or companies and should maintain the intinduster-data, multi-layer and multi-domain relationships using a specified ontology. The digital thread utilizes the meta-data such as description, projects, dependencies, users, origin, and timestamps. The actual data can range widely in type, size, access management, and life cycle requirements making the creation and management of a digital thread challenging. However, the creation of a digital thread is the underlying technology that enables customers to embrace digital design, digital supply chain and manufacturing, and digital twins, to unlock business value.

Digital design

Digital design is the integration and management of the disparate tools and processes used during the system, mechanical, electrical, and software design of a complex system to help teams deliver faster and execute with more agility. A digital design environment enables customers to automate their design processes, makes sure all teams have access to the most up-to-date information, and opens the door for cross-team collaboration and innovation. For instance, during the design phase, teams can automatically propagate design changes between tools and execute simulations to assess the impact of their updates. Teams can more easily collaborate and, in turn, make more informed decisions.

Model based systems engineering (MBSE) is a subset of digital design where system engineers replace document-based approaches with digital models to describe and simulate a system of systems. Digitizing engineering products helps customers seamlessly harness multi-disciplinary engineering tools or artifacts around MBSE by utilizing domain agnostic system languages. More information can be found in the MBSE on AWS whitepaper.

As represented in Figure 1, digital design is focused on the design phase, but customers continue to see value as they move into manufacturing. The digital thread created by the digital design environment continues to propagate late design changes and offers manufacturers the insight they need into the full system design so they can more quickly diagnose issues, optimize the manufacturing, or start the design of a new system with existing data.

Digital Supply Chain and Manufacturing

Digital Supply Chain and Manufacturing is the integrated digital approach to procurement, warehousing, logistics, manufacturing, assembly, and acceptance test functions. Digitalizing these steps provides visibility into the workflow status and further improves efficiency, agility, cost performance, throughput capacity, and quality. Many similar terms are used to represent these concepts including Smart Manufacturing or Industry 4.0. An enterprise level digital transformation should not only include engineering and design functions. It should extend into manufacturing and even system operations to digitally transform the entire life cycle. More information can be found on the AWS Manufacturing page and AWS Supply Chain Management page.

Digital twin

A digital twin is a living digital representation of a physical system that is dynamically updated to mimic the structure, state, and behavior of the physical system. In simpler terms, a Digital Twin is a model of a physical item, like an asset, process, or product. This model regularly updates using data from the physical system to solve a business or technical problem. To help our customers categorize their use cases, we’ve published a blog that discusses the AWS definition and four-level index (L1-L4).

The key difference between a digital twin and existing modeling methods, such as traditional 3D modeling (CAD), physics-based simulations, and virtual worlds (3D/AR/VR), is the information flow between the digital and physical systems. Regular updating is key. It directly impacts how data is collected throughout the life cycle and how the digital twins are constructed. A digital twin must consume data to understand the present state of the system, learn from and update itself (or be updatable) with new observations, and be able to make predictions of the current and future behavior of the system.


Digital threads, digital design, and digital twins are all related but distinct concepts that make up the building blocks of digital engineering. These definitions can be a starting point as you think about your future transformations. Figure out what business outcomes you want to achieve and how AWS cloud services can help you get there

If you would like to learn more, continue reading our other blogs and whitepapers on digital engineering topics including digital twin levels (L1, L2, and L3) and MBSE.

Adam Rasheed

Adam Rasheed

Dr. Adam Rasheed is the Head of Autonomous Computing at AWS, where he is developing new markets for HPC-ML workflows for autonomous systems. He has 25+ years experience in mid-stage technology development spanning both industrial and digital domains, including 10+ years developing digital twins in the aviation, energy, oil & gas, and renewables industries. Dr. Rasheed obtained his Ph.D. from Caltech where he studied experimental hypervelocity aerothermodynamics (orbital reentry heating). Recognized by MIT Technology Review Magazine as one of the “World’s Top 35 Innovators”, he was also awarded the AIAA Lawrence Sperry Award, an industry award for early career contributions in aeronautics. He has 32+ issued patents and 125+ technical publications relating to industrial analytics, operations optimization, artificial lift, pulse detonation, hypersonics, shock-wave induced mixing, space medicine, and innovation.

Burak Gozluklu

Burak Gozluklu

Burak Gozluklu, PhD is a Solutions Architect located in Boston, MA. Before joining AWS in 2019, Burak was a post-doctoral fellow at MIT in Cambridge, MA. Burak holds a PhD in Aerospace engineering from METU and MSc in Systems Engineering from MIT.

Tom Johnson

Tom Johnson

Tom Johnson is a senior product manager at AWS where he develops new services for space customers. He has a background in designing satellites, space payloads, and flight software for government and commercial customers. Tom holds a master’s degree in Aerospace Engineering from the University of Colorado with a specialization in bioastronautics and systems engineering.