Smart manufacturing is on the rise, and for good reason. It can help manufacturers better weather future upheavals and adapt more quickly to changes in demand, the supply chain, and other parts of the value chain. According to Deloitte’s 2021 Global Resilience study, today, 66% of leaders surveyed in manufacturing, industrial products, and construction believe that massive change, such as COVID-19, will be an occasional or regular experience from now on, suggesting that organisations need to be prepared for agility and resilience more or less continually in the future.1
Much has been explored about what’s possible and how organisations can transform their processes and capabilities. But within aerospace and defence (A&D), specifically, what’s real? What is happening now? What technologies and capabilities are actually disruptive today? And perhaps most importantly, do companies have the right strategy in place to capitalise on the disruption?
To better understand these shifts and the specific considerations and opportunities that A&D industry leaders can leverage, Deloitte used industry-specific insights, interviews with industry executives, and primary research data gathered through our experience with smart factories to examine how A&D leaders have successfully implemented smart manufacturing capabilities.
We learnt that sharing data across teams and partners can be key to advancing the industry, but it can come with risk. Another possible key to advancing the industry is technology: A&D engineers are seeing success with advanced manufacturing technologies that suggest where the highest-value investments lie. Most important, we learnt that to pursue the full potential of data-sharing or technology, A&D leaders can explore industrywide ecosystems to drive transformative change.
“I think we'll begin to see more power available as more of it becomes digitised. As the connectivity in the airplanes improves, you’ll be able to... have the capability to integrate into those systems and be thinking about fleet management, maintainability, customer management, revenue management from the initial design phase of those aircrafts.”2 – A&D industry executive
The data life cycle for A&D spans the full arc from engineering, to manufacturing, to the aftermarket, as well as the components included within—monitoring the design process, the digital supply chain, and health of systems when in use. A&D manufacturers and OEMs have a wealth of data at their virtual fingertips but can struggle with how to use it most effectively.
Data from throughout the manufacturing life cycle presents organisations with a significant opportunity to take information from the digital supply chain, smart factory, connected aircraft systems, and the aftermarket, and feed that data back into the manufacturing process to improve design, fabrication, and performance—a process known as the digital thread.3 Data democratisation, which allows all relevant stakeholders equal, unrestricted access to data, can enable ecosystem partners to use information from throughout the data life cycle to inform their own processes, thus advancing the industry.
Many leaders agree that willingness to share data across teams and ecosystem partners can be critical to true innovation, transformation and growth. Deloitte’s recent Digital Transformation Executive Survey found that, prior to COVID-19, one half of industrial products and construction (IP&C) leaders surveyed said getting better insight about their business and market from data was a key driver of digital transformation initiatives; 61% of respondents said this priority had increased as a response to the pandemic.4 Thirty-eight per cent of IP&C leaders surveyed are in the process of building more transparency across their supply chains, and 28% plan to do so.5 Further, 88% of IP&C executives asked agree or strongly agree that access to data-driven insights is making a significantly positive impact on their business.6
At the same time, however, organisations that wish to collaborate must find ways to share data safely. While connection can bring greater collaboration, deeper insights, and more intelligent decision-making, it can also create challenges for A&D companies, where intellectual property (IP), cybersecurity, customer privacy, and other concerns are paramount.7 According to a 2020 MAPI-Deloitte study, 58% of participating manufacturers express concern about privacy, IP, and cyber risks created by smart factory manufacturing ecosystems,8 while 67% of IP&C leaders surveyed say that cyber will play an important role in their digital transformation over the next 12 to 24 months, significantly higher than leaders in other industries.9
A&D leaders note that the question of data—specifically, data ownership—in the A&D industry is a sensitive one, as those who hold ownership over the data also have greater ownership over supply chains and pipelines.10 Questions regarding who owns the data are further dwarfed by the question of who owns the front-end, user-facing application, which means owning the conduit to all the information.11 Leaders also note that while shared data could unlock significant value for the end user, privacy and protection from external digital threats are critical concerns—both for customers and the aircraft itself.12 Data-sharing policies can help with this challenge, including taking a contract-by-contract approach or creating an industry-federated data store for ecosystem members to use.
“If I think about the pure manufacturing side, definitely there’s going to be a move towards automation. There has to be. It’s happened in automotive. It’s starting to happen in aviation.”13 – A&D industry executive
Smart investments in advanced technology can lead to outsize results: Those who take the leap could realise greater business value. For example, a 2019 MAPI-Deloitte study found a connection between investment in smart factory initiatives and business value, and those who implemented the most use cases saw twice as many gains in labour productivity as those who were still on their way.14 A&D executives interviewed pointed towards markedly positive outcomes where they made informed investments with a clear view towards reducing costs, improving operations, and better serving customers.
But what tech investments are worthwhile? It can be difficult to sort through what’s hype and what has true promise, what’s in use today and, beyond that, what’s truly ready to deliver, and how broad of an impact these investments can make across the full value chain. Five noteworthy technologies are seeing adoption across the industry. While each could be worthy of investment, some hold more potential value and promise than others (figure 1).
1. Industrial Internet of Things (IIoT): IIoT is perhaps the most mature and most widely used advanced technology in the industry across multiple applications and demonstrated use cases. It’s the connective glue that binds the network together, generating and communicating flows of data. Sensors and connected devices generate more data to drive insights and actions at the edge than ever before. Predictive maintenance is a common example that illustrates the value of IIoT. It monitors machine performance indicators such as amperage draw, temperature, and speed. It also enables companies to optimise the use of workers, better allocate resources, and inform and predict when a part or equipment failure may occur by comparing real-time data to historical performance.
Combining live machine status with RFID-tracked inventory can go one step further and serve as the baseline for dynamic routing algorithms. If production supervisors know when machine capacity is coming available, where critical material is physically located in real time, and what jobs are open, they can optimise machine utilisation by proactively staging material where it can best be consumed.
2. AI and machine learning (AI/ML): While still in their relatively early days in the industry, the use of AI and MLappears to be increasing and they hold significant promise across the full scope of the value chain that could potentially rival IIoT. Massive data loads from connected, advanced manufacturing systems, along with more powerful algorithms, have made AI integral to the manufacturing process. In addition, it’s also used in manufacturing processes such as computer vision, generative design, natural language processing, and autonomous decision-making. It’s also used to generate insights from production data.
Beyond the shop floor and further along the data life cycle, AI/ML is used by A&D companies to gather and analyse data about aircraft while in use for predictive maintenance and air traffic control, and to better understand air traffic patterns.15A&D executives have also pointed to the power of AI/ML for diagnosing potential problems while aircraft are in-flight, so repairs and turnaround can take place more quickly and efficiently between flights. The potential for use of ML algorithms in digital twins can help to drive greener, more energy efficient aircraft.16 AI/ML applications also helped some aerospace companies better adapt and respond to COVID-19–driven fluctuations in 2020 and 2021. Given that AI/ML does not involve the manufacture of physical parts, it is also often easier to implement.
3. Automation. While the breadth of applications of automation across the full value chain is relatively lower, the breadth of potential value of this technology is significant. Within the supply chain, autonomous robots can drive value by lowering labour costs, increasing productivity, and augmenting the performance of human workers.17 Within A&D, the use of autonomous technologies brings with it industry-specific considerations. Given the relatively smaller scale of production in A&D, the margin for error can be smaller and the business case for investment must be particularly strong. Senior A&D executives addressed the importance of scale: Rather than building hundreds of thousands of units each year, as in the automotive industry, an aerospace company might produce a few dozen aircraft annually. Therefore, using one robot for multiple processes or for multiple programs can spread costs and make a stronger case for investment.18
According to one executive, experimenting with multiple uses for automation and starting with smaller-scale pilots can help make a compelling business case.19 Executives reported employing dedicated tools, but also using flexible robots that can move between stations to do different jobs and employ multiple dedicated tools when needed.20 Automation in A&D should maintain a degree of flexibility and portability in order to be successful in the job shop, based on operations that are frequent in the industry.
4. Augmented and virtual reality (AR/VR): The value of AR/VR is well-established; however, its scope of application may not be as broad as other technologies and is focused on specific, discrete needs. It’s been used extensively in training and maintenance tasks, as well as design. In this way, data from throughout the value chain can be consolidated to better visualise and understand the physical components of the aircraft, making it useful in the early stages of design and manufacturing.
Manufacturers are also using AR/VR to drive action with 3D models. Sales executives simulate aircraft designs for customers. Manufacturing engineers create 3D work instructions that enable mechanics to work more quickly with higher quality. Design engineers can use models to collaborate with supply chain professionals to ensure manufacturability. Similarly, manufacturers are using AR to project logos and paint jobs onto fuselages, enabling customers to view and make changes to exterior designs while avoiding the costly need to repaint.
5. Additive manufacturing (AM): AM has long been part of the discussion in the A&D industry for concept modelling, prototyping, tooling, lightweighting and fabrication of complex or low-volume parts.21 These developments include the introduction of new metallic alloys, lower material costs, and faster production times.22 Further, the use of AI and ML can enable OEMs to leverage information from throughout the digital thread for generative design and parts optimisation.
At the same time, however, while AM has long held promise, it represents a point solution with a scope that is largely limited to the shop floor and manufacturing processes, and it can be challenging to implement. The certification process for AM-produced parts can be onerous; as of this writing, AM is not used to produce primary aerostructures, due in large part to the ability to prove fatigue and damage tolerance thresholds and the need for FAA approval.
“Some of the technologies have some real capabilities. Some are a long way off. What we're trying to understand is, how do we form a group of partners to go do something that's real, in a realistic timeframe, and make sure that we're out there setting realistic expectations? To enable that future, there's going to have to be some new technologies that come into manufacturing. How do you take those and scale them up so that they are more ready when that future comes?”23 – A&D industry executive
Any organisation that has piloted a new capability knows that scaling can be difficult to achieve, but taking an ecosystem approach by leveraging collaborative connections throughout the industry can make it easier. It can also help organisations extract greater value from transformation initiatives, and leverage data from across the value chain to drive smarter decisions.
Whether in nature or in business, ecosystems comprise individual components that interact in an integrated, mutually reliant system. Ecosystems enable stakeholders to coordinate, collaborate, and partner to address mutual challenges, meet shared goals, and pursue new innovations, opportunities, and business models.24 The power of ecosystems is derived from the “network effect,” which describes a phenomenon in which the value of something increases or decreases depending on the number of stakeholders that use it.25 Larger networks can correspond with greater results, as more data, insights, ideas, and resources can be shared and the benefits of advanced technologies can be amplified.
Manufacturing leaders understand how ecosystems maximise value; 85% of manufacturers interviewed believe that production ecosystems are either important or extremely important to maintaining competitiveness. They also appear to understand how critical ecosystems are to their future: Eighty-five per cent believe that ecosystems will transform how they deliver value and will lead to greater productivity and revenue.26
Manufacturers appear to be noticing faster time to market and new channels and markets for their products through these production ecosystems. Research has shown that among the companies surveyed, those that worked with external partners to build capabilities saw results more quickly.27 Further, 86% of IP&C leaders in Deloitte’s Digital Transformation study agree or strongly agree that engagement with external partners is already making a significant positive impact on their business.28
At the same time, however, some A&D leaders interviewed suggest that the ecosystem ideal isn’t quite coming to fruition, noting that the industry has not done enough to help accelerate digitisation collectively as a whole.29 Others point to challenges around data ownership, IP, regulatory concerns, and customer privacy as hurdles that are simply too high to enable a full, democratised ecosystem.
Industry collaboration to enable collective transformation, however, is not impossible. Leaders pointed repeatedly to the relatively small number of players in the A&D industry, noting that relatively fewer stakeholders could make it easier to bring enough organisations to the table to gain critical mass. Furthermore, it may be the key to true transformational change: the use of cohesive industry standards and ecosystemwide collaboration to drive success, greater adoption, and improved outcomes. The industry can work together and leverage the power of the network effect to drive these cohesive industry standards for the benefit of all, enabling the A&D industry to recognise true transformation in smart manufacturing.
Manufacturing leaders know that digital transformation—the enabler of smart manufacturing—is critically important to the industry. Seventy-nine per cent of IP&C leaders in Deloitte’s Digital Transformation study agree or strongly agree that within five years, all successful companies in the industry will have extensive digital capabilities compared with 67% of leaders in other industries.30 Getting there, however, requires an understanding of the technologies and capabilities that are possible now, how to leverage them to maximise value, and, perhaps most important, the ability to harness data and insights from across the entire ecosystem to create a whole that is greater than the sum of its parts—and propel transformation across the industry.
A capability is only successful if it’s adopted. Once a new tool or process is implemented, organisations must make it stick. But how do they do so? Leaders suggest that it’s not about the technology, but about the challenge it addresses and how easily stakeholders can use it and see its value.
Design with the human in mind. Whomever the end user is—whether an individual on the shop floor, a supplier, a customer, a regulator, or another stakeholder—they will be far likelier to adopt and use a new tool if they can see its benefit to their own needs. Beyond that, it should also be easy to use. Executives pointed to the form factor as critical to transformative change happening in smart manufacturing.32 Include end users in functional requirement building and design stages of the process so that they become invested in the transformation from the beginning.
Be willing to change. Put simply, if a process isn’t working, re-work it. Apply an agile mindset not only to the way work is done, but the workforce as well. This is an area where manufacturing organisations have their work cut out for them: Fifty-four per cent of IP&C leaders interviewed said that hiring for flexibility and adaptability was the most critical workforce trait, followed by a general technological savvy at 39%.33 At the same time, however, 48% of IP&C leaders interviewed did not think they excelled at embedding advanced technologies in core business processes to become more agile.34
Don’t start with technology. Start with the business need and invest in the technology that will accomplish that need.Leaders we interviewed noted that digital transformation for its own sake doesn’t work. It can also help not to refer to it as such; stakeholders may care less about “digital transformation” than they do about whether it works. Framing the conversation around the problem that needs solving or the goal to be achieved (for example, reducing cost or grappling with an upcoming surge in retirements) rather than taking a technology-first approach can help stakeholders not only understand the need, but also the overarching goal.35
Remember tech is not a panacea. Digital transformation should not be limited to just the technology; it should include the human and operational sides as well. Leaders emphasised that technology will not solve a problem on its own. Stakeholders must use it; it must be focussed on solving a problem or improving a process, and perhaps most important, other non tech-related changes often need to happen elsewhere in the organisation for true transformation to follow.
Take a bottom-up and top-down approach. To make change stick, it should be driven from both sides of the organisation. Executives should set expectations that change will occur while championing the benefits of the change. At the same time, change agents from staff and working team levels can provide voice-of-the-customer feedback into the process while encouraging their peers to step out of their comfort zone in the short term in exchange for long-term benefits.
Deloitte's Supply Chain & Manufacturing Operations practice helps global manufacturers translate their business strategy into practical operational plans through an effective manufacturing strategy. We help companies implement their strategy by finding ways to improve operational performance across complex value chains. This includes assessing manufacturing capacity, core versus noncore products, where products are made, core capabilities versus those that can be outsourced, where manufacturing plants are located and their supporting supply base, and even advanced manufacturing technologies.