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March 29, 2023

Why Digital Transformation Matters for Aerospace and Defense Companies

Digital Transformation in Aerospace and Defense: Why it Matters

Digital transformation in the North American aerospace and defense industry is a banner initiative in 2023. 

Big Data, AI, machine learning, digital modeling, and more, help make the aerospace design & testing process more efficient. All told, this could save billions of dollars & significantly reduce time to delivery. 

Let’s take a look at the ways in which digital transformation enables aerospace companies to save costs and build more reliable engineering processes.

Why digital transformation in aerospace and defense?

At the company level, digital transformation improves operational efficiency by streamlining many traditionally time-consuming processes. 

A new McKinsey research effort, in partnership with the Aerospace Industries Association (AIA), found that advancing the digital maturity of the aerospace aviation and defense value stream could unlock $20 billion in annual EBITDA. 

This transformation is being driven by a number of factors:

  • Innovation in automation
  • Increased demand for speed and efficiency
  • Supply chain uncertainty mandates more streamlined processes
  • Cutting-edge digital capabilities as a competitive advantage

Here are some of the more specific problems that digital transformation is solving in the aerospace sector. 

Reduce production and development costs

Given the high cost of aerospace engineering platforms, physical prototyping is often cost prohibitive. The more engineers can pressure test and iterate on their models before moving to physical production, the more economically feasible these projects will be. 

What’s more, a single rocket or airplane could be the result of over a decade of R&D. There needs to be a justification for that amount of investment. By engaging in digital transformation and streamlining the modeling process, engineers can reduce the time and investment needed to generate new models, thus realigning the incentives toward more innovation. 

One specific example: for the first decade in its history, Boeing will not be releasing a new platform. Given the current state of the technology, the marginal benefits per model aren’t worth the investment needed to make those changes. 

Of course, this limitation has downstream effects. If Boeing doesn’t make a 797 this decade, then Pratt & Whitney won’t be able to develop and deploy a new engine, which has downstream effects on their supply chain

Think of it this way: if it costs $1B minimum to develop a new platform. In order to justify that investment, any new platform must present a roughly 15% or 20% improvement. 

However, if an engineer can only reasonably make an incremental change, say 5%, then they have to reduce the investment required to reach that amount. For example, SpaceX has built a new engine from scratch for a quarter of what it would cost Pratt & Whitney. This is the direct result of better engineering processes. 

Safety and compliance

Given the necessarily rigorous safety standards surrounding commercial aerospace compliance, technology and safety processes can be quite detailed. Most companies need to comply with aerospace certifications such as DO-178, DO-331 and DO-330. Digital transformation helps to limit these through a number of ways:

  • Collecting real-world data that can be incorporated into digital models in real time, removing the need for extraneous testing
  • Creating virtual environments to test the safety features of a digital model at no risk to the platform or any passengers involved
  • Leveraging VR and AR to perform necessary training before exposing them to riskier scenarios

For example, Aries is a startup providing VR-based training solutions. Fyr, another startup, has developed a head-mounted AR-based visualization system. 

Improved collaboration among teams and regulatory agencies

As DOD and other agencies expect suppliers and contractors to collaborate remotely, digital transformation provides the tools necessary to make this happen. The result: faster innovation and product development. 

Digital twin and digital threat technologies, specifically, enable secure, remote collaboration. By offering digital twins, aerospace manufacturers can grant the agency a hands-on look at concepts and works in progress.

What’s more, cloud-based applications enable collaboration and transparency among internal teams. Rather than have to wait for insight into other teams’ progress, they can access draft and in-progress models and designs. 

For example, by using virtual collaboration tools in their manufacturing functions, General Motors reduced cost by 95% compared to a traditional physical environment.

Supply chain management and manufacturing efficiencies

Supply chains remain a source of uncertainty and risk in aerospace. By improving everything from inventory management to supplier assessment, digital transformation enables manufacturers to better account for and mitigate risk. 

With AI and machine learning, companies can more accurately forecast inventory needs and assess macro global factors that can potentially impact supply chains. 

Boeing, for example, brings together data across past forecasts, current demand, availability of raw materials, capacity and quality of suppliers, and other factors to more accurately predict cost, quality, and availability of necessary supplies. 

Shorter maintenance cycles and increased uptime

By streamlining manufacturing and maintenance processes, digital transformation in aerospace can reduce system downtime. Manufacturers can provide more accurate production timelines and improve their likelihood to meet them. 

French manufacturer Thales, for example, leverages AI and Big Data to prevent failures and anticipate maintenance operations.

What does digital transformation in the aerospace and defense industry look like?

“Digital transformation” is an amorphous term that often gets thrown around too casually. What’s more, different industries and verticals engage in digital transformation in different ways.

So how can aerospace companies best gain value from digital transformation? What aerospace tools, platforms, systems, and tactics deliver the most value? 

Let’s start by looking at five ways in which digital transformation adds value to aerospace companies. Then we’ll dive into how this applies to the aerospace modeling process more specifically. 

1. Model based design

Model-based design is an approach to system design that moves the record of authority from paper, documents, and files to digital models managed in a data rich environment.

By building models, leveraging data, and running virtual tests in the cloud, model-based design helps to:

  • Cut costs by reducing physical prototyping needs
  • Increase productivity, as engineers can test and iterate in real time
  • Shorten product development cycles
  • Offer transparency into the design process across functions and geographic locations
  • Allow for smarter, data-rich design decisions - rather than trial-and-error
  • Improve odds of success in the final system - critical when the margin of error is low and overhead is high

In model-based design, teams create one central model to conduct aspects of development: including requirements tracking, design specifications, implementation, verification and validation, and deployment. 

2. Artificial intelligence & machine learning

54% of business executives say that artificial intelligence (AI) implementation has increased their productivity. Within aerospace, AI enables manufacturers and engineers to gain insights into their operations that they couldn’t access before.

This applies across the board to various functions, including:

  • Design and testing
  • Manufacturing
  • Maintenance
  • Training & education
  • Safety & compliance
  • User experience & satisfaction

As digital transformation increases the sheer scope of the datasets collected across organizations, AI can sift through that data faster than any human or group of humans. 

If companies want to leverage data to drive decision making, artificial intelligence and machine learning are critical components of digital transformation for global aerospace companies. 

3. Cloud technology

We touched on this when discussing model-based design, but a major innovation in aerospace has been the adoption of cloud technology. 

Cloud technology impacts how businesses collect, manage, and use their data. And, as we all know from the pandemic, it’s the key to collaboration among geographically distributed organizations 

A range of technologies rely on the cloud to operate, including:

  • Model-based design
  • Artificial intelligence
  • Machine learning
  • Analytics
  • Virtual reality (VR) and augmented reality (AR)

In fact, by migrating to the cloud, aerospace IT functions can save 15% of their annual expenses. Instead of spending money on physical infrastructure like on-site services, aerospace defense companies can leverage the security management of trusted data storage facilities. 

4. Automation

Automation is key to streamlining organizational productivity. From factory floors to supply chain, in-flight adjustments, backend, marketing, customer service, manufacturing and supply management, automation plays a major role in digital transformation in aerospace. 

Many legacy processes in aerospace rely on manual, complex human engagement. Automation fulfills these tasks more quickly, efficiently, and accurately. 

5. Modern cybersecurity

As digital activity explodes, cybersecurity becomes increasingly important. This is especially key for companies who contract with federal agencies, where standards are high to achieve compliance with CMMC. 

Plus, many defense aerospace companies - particularly those who work with the government such as Lockheed Martin, Raytheon Technologies, Northrop Grumman - have become big targets for cyberattacks. As such, cybersecurity in aerospace requires a layered approach that protects all aspects of the business.

Digital transformation enables aerospace companies to plan and execute key components of their cybersecurity plan, which include: 

  • Network monitoring 
  • Antivirus software 
  • Endpoint protection 
  • Edge security 
  • Access management 
  • Backup and disaster recovery 
  • Secure data protection

Digital transformation in aerospace modeling

Now that we’ve given a general idea of what digital transformation in aerospace looks like and why it’s important, let’s dive deeper into its impact on the aerospace modeling process. 

Phase 1: Preliminary design 

Building an aircraft, UAV or rocket will begin with aerospace systems engineering and a preliminary design. In this phase, the goal is to simply model the mission the system will carry out.

Because of their preliminary nature, the fidelity of these models is quite low, with only three DOF modeled, contrasted with the usual 6+. The entire system is assumed to be rigid. When constructed digitally, these models can be constructed fairly quickly, allowing for faster preliminary testing and quick movement to the next phase of the process.

Additionally, digital transformation has resulted in numerous commercially or publicly available datasets that engineers can use in lieu of proprietary test data. This enables engineers to more rapidly build and validate their preliminary models. 

Upon completion of the model, an engineer will conduct hundreds or thousands of simulation runs to inform an uncertainty analysis. The specific threshold the model needs to meet will vary based on the technology readiness level. 

For example, commercial aircraft may need to demonstrate a 97% chance of success from simulations, because we have so much available data on these technologies. Nascent technology, like hypersonic aircraft, may only need to show 50% success before moving onto more rigorous testing. 

Phase 2: Detail design

Upon exit from Phase 1, the next phase is detail design, where the majority of the engineering effort takes place. At this point, dozens of engineers involve themselves in the project (versus only two or three before). 

Detail design involves the following steps:

  • Upgrading the fidelity of each model created in Phase 1
  • Transitioning from 3 DOF dynamics to 6 DOF, then later n-DOF (6-DOF + flexible body dynamics)
  • Further testing of each subcomponent to collect more data, reducing the uncertainty of each model
  • Software release for each embedded algorithms
  • Simulation set produced and analyzed to predict likely production system performance
  • Interim program review to assess development risks 

Phase 2 ends with a critical design review, which is essentially the same as Phase 1 but with higher exit criteria. For example, high TRL systems may require as high as 99.9%+ chance of success to exit. Upon completion of Phase 2, engineers can then begin prototype construction.

With digital modeling and AI-powered information technology, engineers can exit Phase 2 and begin prototyping with astronomically higher chances of success than traditional methods. This not only makes the development cycle faster, but reduces the amount of iteration necessary in Phase 3. 

Phase 3: Full-scale design

Finally, the system is built and assessed through all missions through a process known as a production readiness review. There are still revisions and iterations that need to happen at this stage, but if engineers did their job well in the first two sections, then they should be minor tweaks. 

Exiting Phase 3 typically includes a meeting with an insurer. The cost of insurance is based on available data and predicted failure rates for the fleet based on simulations. Once everyone agrees on an acceptable cost, production proceeds. 

Final thoughts on digital transformation in aerospace

Digital transformation in aerospace has the potential to create more value in less time and at lower costs. By enabling more innovation, reliability, and cost-effectiveness, it has driven sweeping change in the industry. 

Despite this, many organizations still struggle to find the right platforms and technologies to take full advantage of digital modeling. The best modeling platforms are those that:

  • Host data and models in a single location for seamless intelligence & testing
  • Run hundreds of thousands of simulations based on digital models
  • Leverage cloud-based tools for transparency, speed, and collaboration

Collimator is the leading platform for model based engineering design, providing a unified environment to design, simulate, test, and continuously upgrade embedded systems

Learn how Collimator can help you reduce development risks & bring your products to market faster. Book a live demo with our team to get started!

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