Using the network effect

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 optimize 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 optimize machine utilization 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 analyze 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 labor 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 visualize 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 modeling, 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 optimization.

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.

Scaling capabilities externally means building an industrywide ecosystem

“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 organization 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 organizations 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 maximize 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 percent 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 digitization 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, democratized 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 organizations 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 recognize true transformation in smart manufacturing.

Manufacturing leaders know that digital transformation—the enabler of smart manufacturing—is critically important to the industry. Seventy-nine percent 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 maximize 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.