Disruptions in the sector come from every angle. A foundation of digitization, AI and machine learning offers agility and resilience.

In brief

  • A&D companies spend an inordinate amount of time responding to supply disruptions and “putting out fires” because they lack proactive capabilities. 
  • Many businesses are leveraging AI, machine learning, digital twins and even blockchain to gain supply chain advantages, from forecasting to logistics. 

After the fallout from the COVID-19 pandemic, supply chains within the aerospace and defense (A&D) sector have arguably been strained more than those of any other industry. Companies have been forced to ramp up production and meet increasing demand, all while navigating high costs and limited availability of materials from at-risk suppliers — not to mention shortages of labor and more.

On top of routine risks (such as long and capital-intensive product lifecycles, with unique materials and performance specifications), A&D companies have regularly struggled to gain full visibility across supply chains. Today’s added volatility — including inflation pressures and geopolitical disruptions — shows how traditional supply chain monitoring practices are not enough and can lead to high-profile product incidents.

In commercial aerospace, supply chain challenges are exacerbated by a marginally faster pace of change and the costs of innovation developing a next-generation narrowbody aircraft could cost US$25-50 billion, with all the inherent specification limitations and design changes upon delivery. On the defense side, programs exist for decades, and ships and aircraft can serve in operational roles for three decades or more. An industry that drove technology development in the 1950s and 1960s is an afterthought for fast-changing supply chains in electronics, sensors, software and computing (among others).

As A&D challenges have evolved, so too have the strategies for confronting them: companies can go beyond supply chain monitoring and reactive mitigating tactics to more proactive prediction and preventive measures. The answers lie in increased digital enablement, data analytics, and artificial intelligence (AI) and machine learning (ML). Here is why those technologies are more important than ever — and how to begin putting them to use.

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Chapter 1

Lack of transparency in the A&D supply chain

Against a dynamic backdrop of disruption, supply chain risk management in the sector remains relatively unsophisticated.

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    In the A&D industry, supply chain risk management is crucial for mission success, yet it’s nearly impossible to have clear visibility from end to end, considering the thousands of suppliers per program and thousands or even millions of parts per aircraft. The risk of failure for any one of these parts or modules can be catastrophic. Concurrently, the economic health of suppliers can be as tenuous, susceptible to evolving macroeconomics, customer preferences, and fast-changing budgeting and procurement plans. 

     

    A&D companies rely on trusted processes and practices to track and monitor the supply base that are somewhat effective but are often not enough to avoid significant operational delays and disruptions, or even product failure. Such challenges include:

    • Scarcity of raw materials. For example, titanium became acutely limited after the war in Ukraine, creating a cascading effect that impacted the availability of essential components like forgings and castings, further exacerbated by labor shortages. Software and hardware issues contributing to the delay of one prominent combat aircraft, compounded by legal disputes with key suppliers and labor challenges.
    • Cash conversion cycles. The extension of payment terms, coupled with the unpredictability of material lead times and customer demand, has placed significant strain on the working capital of small and medium-size enterprises. Larger companies are often exerting undue pressure on their sub-tier suppliers. This financial strain is further compounded by contractual terms that fail to account for the current volatility of the supply chain, leading to unexpected cash flow disruptions.
    • A labor market in flux. Companies are facing “brain drain” as skilled manufacturing labor becomes increasingly scarce. Training delays and high attrition rates further impede the ability of suppliers to ramp up production to meet demand. The consistency of original equipment manufacturer (OEM) production schedules makes companies hesitate before investing in capacity expansion, leading to a self-reinforcing cycle of supply chain fragility.

    Some solutions are within aerospace leadership’s control — such as by improving wages and benefits to reduce attrition and offering better OEM payment terms and increased confidence in OEM production guidance to resolve working-capital deficits that torment sub-tier suppliers. But most importantly, leadership should admit that the traditional risk management practices rooted in legacy processes and reactive strategy — while providing a modicum of stability — often fall short.

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    Chapter 2

    Leverage technology for more end-to-end supply chain resiliency

    Strong investment in digital enablement, specifically in data analytics and ML, is essential for long-term growth and value.

    To reduce supply chain risk and volatility, it’s crucial to unlock the power of next-generation data analytics, AI and ML analytics capabilities, built on deeper all-around digital enablement. AI in supply chain management could help enterprises become more resilient and sustainable and transform cost structures. While it does have limitations, generative AI (GenAI) presents a multiplier in what humans and technology can achieve together in building efficient and resilient supply chains — whether in planning, sourcing, making or moving.

    Data analytics and ML is the game-changer here as it can achieve the heightened level of visibility required to predict and mitigate potential risks accurately and efficiently. ML makes it much easier to analyze vast amounts of data in real time, identify patterns and forecast disruptions that could impede production, delivery and overall operational efficiency. In an industry where precision and timely response are paramount, ML helps in proactively finding solutions, enhancing supplier reliability, reducing cost and ensuring the delivery of quality products on time. It’s essentially a digital sentry and advisor, wrapped in one, for the A&D supply chain.

    For instance, one defense contractor is now leveraging AI-driven insights to find weak links in its supply base and identify the vulnerabilities to proactively manage risks and ensure compliance. It expanded its supplier visibility from 15,000 to 500,000 suppliers using these tools and gained a competitive edge through improved operational efficiency and decision-making.

    Diverse Air Traffic Control Team Working in a Modern Airport Tower at Night. Office Room is Full of Desktop Computer Displays with Navigation Screens, Airplane Flight Radar Data for Controllers.
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    Chapter 3

    Gain real-time supply risk monitoring and mitigation

    A&D companies spend the vast majority of their time trying to mitigate risk and much less time predicting it — ratios that should be flipped, and can be.

    Digital tools and data redefine how A&D companies allocate their time effectively, orienting efforts in a more active, and proactive, direction.

    • Monitoring: Stratify and prioritize the supply base depending on which suppliers and parts pose the biggest risk to operations. Historical supplier performance is a consideration, but so are factors like single source, long lead time, extent of safety stock, and where the part falls in the build sequence. In this stage, you can determine data gaps, and when those are resolved, emerging technologies can be used to analyze cross-cutting data and performance factors. By generating preliminary key risk indicators (KRIs) mapped to each risk space, you can then implement KRI monitoring.
    • Predicting: AI can sift through publicly available data — for instance, supplier locations, labor issues, financial challenges and litigation — to trigger alerts when deliveries could be impacted. And automated data feeds (including raw material inventory, cycle time changes and quality trends) from critical suppliers’ systems can enable a high-confidence automated risk assessment. Technologies such as those used in a digital twin are also vital for testing responses to what-if scenarios.
    • Mitigating: Shift time and attention toward risk mitigation actions — from near-term supplier management to delivery and quality issues, to long-term strategies to secure supply. Given your prioritization of suppliers and potential risks, identify the mitigating strategies most likely to impact the scaling and timing necessary for your responses, and build the roadmap allocating limited resources against the prioritized issues. AI and ML are crucial toward scaling mitigating capabilities.
    Two airplane mechanics fixing helicopter engine, back view. Male and female coworkers standing on ladder and repairing jet, long angle shot
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    Chapter 4

    Use digital twins for rapid planning and rescheduling

    By digitizing supply chains and harnessing advanced technology, A&D companies can navigate sourcing challenges more effectively.

    Digital twins represent a compelling example of digital evolution. These virtual replicas can provide a comprehensive view of key facets of an organization’s supply chain or even from end to end — encompassing procurement, production and delivery processes — to be used for testing scenarios and modeling ways to optimize processes.

    Within a digital twin that runs on an organization’s data, AI-enabled supply chain visibility solutions offer real-time insights into inventory levels and supplier performance, giving risk mitigation a proactive dimension. Advanced forecasting algorithms can be integrated into the digital twin to predict and pre-empt potential delays or quality issues in critical component procurement and delivery.

    Additionally, dynamic simulation models within the digital twin can be used to optimize inventory policies, minimize stockouts and maintain adequate stock levels to meet customer demand while balancing inventory carrying costs. It can proactively identify and mitigate supply chain risks, including supplier disruptions, transportation bottlenecks and quality issues, to enhance resilience and minimize the impact of disruptions on business continuity.

    A digital twin can also be a tool to streamline processes and improve operational efficiency across the supply chain network, including scenario analysis, optimization algorithms and performance monitoring capabilities. Better mechanisms enable continuous improvement and refinements based on feedback, performance metrics and evolving business requirements.

    More broadly, digital twins can also:

    • Use predictive analytics for demand forecasting, enabled by AI. A&D companies are empowered to adjust production and inventory levels with precision, thus averting stockouts or excess inventory. One A&D manufacturer leverages an AI-based enterprise digital twin to strengthen forecasting and even gain insights into asset lifecycles and carbon footprints.
    • Enable A&D companies to model, analyze and validate new manufacturing lines virtually, thereby reducing industrialization cycle times and elevating quality standards. One aerospace leader has invested in digital twins for end-to-end modeling of product lifecycles and production systems, using predictive analytics to optimize supply chain efficiency and foster cross-departmental collaboration in a virtual space.

    The integration of Industry 4.0 technologies is another area where AI and ML are bridging gaps and accelerating processes in defense scenarios. These technologies facilitate cargo visibility, smarter warehousing and optimized transportation, thereby modernizing military logistics and supply chain security.

    Airport, Airplane, Airport Runway, Commercial Airplane
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    Chapter 5

    Don’t overlook the potential of blockchain

    The technology can enhance operational efficiency, optimize inventory management and ensure the circulation of authenticated components.

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    Blockchain technology is solving the complexities of A&D supply chains by ensuring immutable traceability of parts, fortifying against counterfeiting and facilitating seamless data sharing across the ecosystem. For example, one defense contractor has joined forces with a blockchain-enabled data network provider to implement a digitized knowledge-sharing service for its components. With blockchain-generated insights, the contractor can deftly calibrate inventory levels to mirror actual usage patterns. This data-driven approach to stock management ensures a balance between demand fulfilment and inventory cost optimization.

    Summary

    Accelerated advancements in data analytics and ML have created a massive opportunity to address longstanding A&D supply chain issues — which are crucial developments in a business landscape wrought by new disruptions and persistent challenges. By digitizing their supply chains and harnessing advanced technology, A&D companies can sharpen their forecasting, navigate sourcing challenges more effectively and meet the burgeoning demand, address logistics, and enable more strategic decision-making.

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