Although the Defense & Aerospace (“D&A”) sector is witnessing strong growth trends that companies are seeking to utilize in order to transition to improved business models, forge stronger partnerships and create technological advancements, the true growth potential can seemingly be held back by outdated processes, gradually-declining technology and fractured value chains across enterprises. Considering the purported benefits that Industry 4.0 and Digitalization can offer, companies can integrate newer technologies and scale up quicker by taking advantage of the advent of the true digital age.

This brief article will look to cover the challenges that D&A companies are expected to square off against whilst implementing Industry 4.0 concepts on an enterprise-level across different verticals, along with an overview of the kind of disruptive forces and breakthroughs that can impact the industry. The intent is to provide a key summary of the kind of corporate actions that must be taken in order to create a value chain that is driven by digitization and Industry 4.0, at least substantially, if not entirely.

With correct strategic execution at an enterprise / corporate level, D&A companies can harness information and data in a way that evolves their existing ‘designing, manufacturing and servicing’ processes (the “DMS Cycle”) through the creation of a digital value chain, streamlining and formulating an interconnected DMS Cycle, digitizing continuous improvement opportunities, and applying the concepts of a connected “smart” factory to improve profitability, efficiency and product outcomes. However, this adoption of Industry 4.0 cannot happen in isolation, and D&A companies must stay cognizant of the industry trends, along with having a pulse of the changing geopolitical atmosphere, customer needs, compliances, and regulations in order to be able to deliberate on the kind of strategy to be adopted in order to create value, especially when matters pertaining to national security data, intellectual property and compliance are concerned.

The process of creating value in the D&A must be undertaken in a step-by-step, gradual approach in terms of implementation, and if done right can provide the right leverage for process transformation and automation, in addition to adding a dimension of data-driven decision making at the strategic level. At the outset, any such strategy must enable companies to share and merge information across several domains and verticals, through the improvement of intelligence and decision support – both in each vertical and across the entire corporation.

• Conceptualizing the concepts of ‘digital threads’ and ‘digital twins’: In an increasingly virtual world where ‘data is the new oil’, the core components of the entire value chain of a D&A company must be tied together through a strand, ‘the digital thread’, which runs across key ideation and execution domains, including product life-cycle management, supply chain management, enterprise resource planning, and customer relationship management systems. The creation of this digital thread would enable a D&A company to access, integrate and transform data into actionable insights, feeding continuous improvement cycles and allowing for an open strategy to unlock additional new sources of value. In synchronicity with this digital thread that transcends corporate divisions, is the creation of a ‘digital twin’ – the digital / virtual manifestation of the real, physical asset which can be leveraged to visualize, test, and learn through modeling and simulation. The creation of this digital twin would allow for companies to come up with parallel, hypothetical iterations of the product without having the need to go into the physical prototype stage, providing them with feedback that saves both costs and time, thereby allowing them to expedite the transition to the manufacturing stage – which can be vital in the D&A space where time is of the utmost essence.
• Data-driven decision making: The advent of the internet of things (IoT) has helped bring disparate information sources together, providing a rich database that can be combined with machine learning and artificial intelligence to create the kind of information and intelligence that can empower information-centric decision-making processes for D&A companies. This aspect would also enable D&A companies to use data to make rapid, simultaneous improvements to both automated and manual processes across the enterprise value chain. This decision-making capability can be further harnessed through the use of video analytics, by creating automated data acquisition systems for information gathered from ‘on-field’ deployment of D&A products through sensors embedded in products and technological devices, supplementing IoT in a bid to provide real-time, practical and often implementable insights to the production and design teams.
• Digital Workplaces for Enhanced Productivity: Whilst this point stands more true now than ever as the pandemic has reared its ugly head and turned the world as we know on its head, D&A companies can benefit from tapping into the real intellectual capital at their disposal – their employees, their knowledge processes, and technical know-how. Through the implementation of knowledge management, modern productivity tools, automation, AI, and smart machines, D&A companies can create a collaborative work environment resting on the shoulders of modern technology and preserve the knowledge and know-how of the experienced members of the workforce whilst attracting the talent of the future, which would help augment the aptitude and capabilities of its intellectual resources. In this vein, technology such as virtual or augmented reality or simulations can help provide remote guidance and learning in field locations and test conditions which may otherwise be inaccessible due to a number of reasons. New workplace technology can also help meet organizational demands for digital identity management, security, and easy employee onboarding. These also enable vertical integration and improve product delivery and differentiation.

D&A companies can use the growing technological innovations in order to accelerate the timelines and better integrate their processes across the three parts of the DMS Cycle, and move towards a framework with digital customer experiences, digital business transformation services (for manufacturing, testing, assembling, and delivering products) and business model innovations in their aftermarket service offerings.

• Design Phase: The single most important concern for firms in the D&A sector is related to design and ideation aspects of their business model, as shortened time-delays in the transition from design to manufacture can provide massive momentum and strategic edge to D&A companies. As we move towards an increasingly global marketplace with a seemingly paradoxical emphasis on creating value locally, accelerating the speed to market metric is a concern for D&A firms. Industry 4.0 can be adopted by firms in order to foster collaboration in product ideation, computer-aided designs, data engineering, and management, which holds the key to shortening timelines that companies allocate to designing their products. This ‘pooling’ of information and data can accentuate learning, anomaly & problem identification, and problem resolution during their ramp-up periods of the design phase. D&A companies can seek to leverage this through three inter-linked approaches: first, through virtualization, by optimizing software and technology assets in order to enable partners to collaborate despite any topological and geographical limitations – allowing them to share and undertake development of information, technical specifications, and drawings in real-time, enabling accelerated design adoption mechanisms and agile decision making; second, through the creation of the above-mentioned digital twin through a technical simulation in order to uncover early product performance deficiencies before physical processes and products are completed, across different lines of service such as operations, manufacturing, inspections & quality control and sustainability ; third, through the implementation of High Performance Computing (HPC) capabilities that can contribute to model-based engineering in order to create and optimize theoretical and arithmetic designs, in addition to replicating existing products. This kind of analysis and design support can have a dual effect, by reducing costs while improving the quality of the eventual end product. HPC per se has a very wide set of applications, and can also be utilized by D&A companies for complex analytical processes. The adoption of these three approaches in tandem, can position D&A companies adequately well to undertake rapid prototyping through a series of feedback loops to support the prototyping effort, and also test and learn with an agility that would not be possible with traditional architecture. As competition in the D&A space hots up, the evolution of the design phase on such lines would enable a ‘fail fast’ mentality in companies with a focus on process optimization.
• Manufacturing Phase: At the manufacturing stage, the driving consideration for D&A firms is their ability to increase throughput and efficiency. With the kind of complex manufacturing and production lines that are the mainstay of the D&A sector, ensuring that the requisite components meet quality parameters and are available timely are vital considerations. In order to achieve noticeable improvements to this phase of the DMS Cycle, it is imperative that vertical integration (to provide product details at every transitional point), stakeholder/partner collaboration, and increased supply chain visibility (to enable predictive-demand driven adjustments & product improvements) are promoted. D&A firms can undertake this transformation of their manufacturing frameworks through the adoption of technology in two aspects: first, through the usage of data generated by sensors, RFID tags, and GPS for the purposes of tracking supply chain information, which can provide D&A companies with real-time, on-ground status of plans, supply chains, and inventories without having to depend on previously indispensable labor; second, by providing the procurement and logistical staff with intelligent systems such as cognitive analytics and machine learning to assess constraints and alternatives and simulate different hypothetical courses of action. The self-learning potential of cognitive automation models also means that historical data sets and information can be used to reconfigure for optimal results and also combat disruptions in supplies and logistics.
• Services & Aftermarket Support: Leveraging technology to understand customers better, improving services, and cultivating insights into current products can help D&A firms to better manage their customer service verticals. The key to effectively managing the customer experience lies in investing, creating and training the workforce to utilize collaboration platforms and tools, knowledge management, analytics, and information management systems. The data generated through the two prior phases can also help in this regard, helping to foster a culture of predictive and proactive maintenance which would enable the technicians and service staff to reduce downtime, labor, and ‘unexpected’ technical glitches / breakdowns by conducting maintenance tasks that are required immediately along with those that may be needed in the future based on the outlook generated through data analysis. For D&A clients, having a pleasant and efficacious customer experience is key, and adding a digital dimension to customer experience may help drive sales and aftermarket revenues both.

With the right modifications and strategic planning to acquire these capabilities, D&A organizations can move from linear, sequential supply chains and business models towards one that is more optimized, and which may be able to use synchronized business signals and decisions to drive improvement across an enterprise level.