Connected supply chain ecosystems are bringing people, processes, data, and technology together, enabling supply chains to deliver a new era of supply chain resilience.

In today’s uncertain market, characterized by instability, inflation, and energy shortages, organizations need to build and implement intelligent, future-ready supply chains that deliver enhanced efficiency, resilience, and sustainability to the wider business.

But where do they start? This series of blogs outlines:

• How organizations are connecting their supply chains by building ecosystems that drive resilience
• How disruption and data are driving transformation efforts across supply chains
• The steps needed to deliver a truly connected enterprise
• And the rewards associated with it.

Fortifying the future of supply chain

Many enterprises are now seeking deeper levels of integrated, digital transformation across their businesses. This is driving them to connect with partners to build better ecosystems, create greater growth avenues, and foster more collaboration, and customer insights in – what we call – the Connected Enterprise.

For supply chains, these connected ecosystems add new layers of security, moving away from single source supplier models to forge stronger, end-to-end levels of resilience across the entire supply chain network.

As an industry imperative, these connected ecosystems are taking the supply chain and logistics into a new, resilient future by bringing together people, processes, data, and technology.

Exploring intelligent supply chain ecosystems

Capgemini’s Connected Enterprise approach is the key towards driving resilient, long-lasting, and sustainable business outcomes, giving organizations the flexibility and data insights necessary to adapt to market changes as they occur.

It provides the catalyst for re-thinking the core of supply chain processes. Stronger insights, coupled with digitally augmented technologies, provide a clear path towards continuous innovation. These new business models, technologies, and processes give companies deeper levels of ecosystem integration.

At its essence, an ecosystem is a true collaborative network, involving all stakeholders that operate within the value chain. This includes suppliers, manufacturers, customers, partners, and the organization itself that operates the supply chain. They partner to focus on crafting an enhanced, continuously innovating logistics model that drives enhanced business outcomes and value for the organization.

Within a supply chain ecosystem, partners integrate their strengths to give each other a multi-layered, strategic approach to overcome sudden disruption. This takes the form of diversifying procurement assets and service agreements, as well as integrating multiple partners with the latest technologies and strategies to bolster durability.

By harnessing data intelligence, technologies such as generative AI, new models of governance, and process excellence, organizations are crafting an intelligent ecosystem that gives real substance to supply chain transformation. This impacts everything, from workforce engagement and sustainability targets to customer experience.

Revolutionizing supply chain performance through end-to-end supply chain orchestration

Capgemini partners with Kuehne+Nagel to offer a one-stop solution that drives new and improved performance levels across your end-to-end supply chain through seamlessly integrating your planning and logistics management to reduce accountability, data, and intelligence mismatches.

Combining Kuehne+Nagel’s industry leading logistics management and execution expertise with Capgemini’s state-of-the-art Intelligent Supply Chain Operations capabilities delivers AI-enabled, cognitive, touchless operations and data-driven decision-making.

In the next blog in this series, we will discuss this partnership further and how it leverages and overcomes disruption to drive supply chain transformation.

To discover how Capgemini’s unique partnership with Kuehne+Nagel can help your organization drive improved, end-to-end performance levels across your supply chain, contact: joerg.junghanns@capgemini.com

Licensing changes are prompting customers to consider a move to Google Cloud. Here’s why it’s a good time to do so.

Legacy systems are costing businesses millions of dollars in maintenance – and that is increasing every year.

Preserving legacy IT infrastructure also brings other challenges, including security vulnerabilities, hardware failures, data corruption, outages, stagnating performance, and efficiency limitations.

Many of these scenarios became clear to VMware clients earlier this year when the company, recently acquired by Broadcom, changed its licensing models, resulting in increased fees and reduced flexibility. For those considering modernization or a move, the licensing change has come with opportunities to rethink their on-premises VMware strategy and look at alternate solutions to reduce costs and increase agility.

Mitigating the cost-agility squeeze

With licensing costs rising significantly for on-premises VMware solutions, users now find themselves at a strategic crossroads: to make the leap into modernization or continue to maintain legacy infrastructure in spite of mounting challenges.

Rising licensing costs are not the only financial impact here. Consider the reality that, in moving from an a-la-carte pricing model to the all-in-one package deal that was recently introduced, VMware users may end up paying for services they don’t necessarily need. It’s more money for potentially less value.

What’s more, legacy infrastructure and aging platforms restrict both technological and strategic business agility, making it challenging to scale up or down with fluctuations in workload demands and changes in marketplace needs. Consider retail clients, who face significantly higher online demands during the holiday shopping season. An agile, cloud-based platform allows them to scale up and down as demand fluctuates, which effectively streamlines resources.

Most businesses today are also concerned about security, which is another risk factor to consider on aging platforms – not only due to the potential for breaches but also because support for legacy infrastructure eventually phases out due to a lack of resources and relevant skill sets.

Introducing the Google Cloud VMware Engine

Google Cloud VMware Engine (GCVE) enables enterprises to migrate and update their VMware-based workloads onto Google Cloud. The platform allows users to retain their current VMware licenses, management controls, tools, teams, and expertise, while adding all the benefits of a cloud-based platform: scalability, performance, security, and more. GCVE empowers customers to expedite their business transformation process and modernize applications by leveraging advanced functionalities such as Google Cloud analytics and GenAI capabilities.

Through GCVE’s unique Committed Use Discount (CUD) and licensing controls, clients are able to move away from licensing lock-in and benefit from 15 to 30 percent discounts based on a flexible three-year commitment that isn’t tied to a particular tool or configuration. The pay-as-you-go pricing and lower hardware requirements result in significant cost reductions and flexible benefits that enable businesses to modernize their workloads in the way that works best for their organization – and budget.

As workloads are migrated and modernized, GCVE provides VMware operational continuity, allowing customers to shift to cloud consumption payment models while lowering their total cost of ownership. Customers can reduce infrastructure costs and improve the efficiency of IT staff and application development teams while taking advantage of Google Cloud’s built-in scalability to match individual workloads with the right mix of RAM, CPUs, and storage to optimize end-to-end performance.

As a long-standing Google Cloud Partner, Capgemini is pleased to support our VMware clients in moving to GCVE with migration options that enable businesses to move at their own pace and comfort level. For Auto Club Group, for example, consolidating its data centers and product suite to Google Cloud’s infrastructure effectively saved the organization $500,000 per year. It also helped ensure business continuity thanks to a more stable, secure, and future-proofed technology stack.

Take advantage of GCVE’s benefits for your business

Increases in the total cost of ownership impact an organization’s ability to remain competitive, drive innovation, and future-proof the business. Replacing legacy infrastructure ensures your organization retains the ability to be competitive and innovative.  

Migrating to Google Cloud or moving VMware on AWS to Google Cloud solves these issues and more. It improves workflows, reduces downtime, and opens up the opportunity for innovation. You simply can’t leverage the benefits of the cloud unless you modernize your legacy systems.

Capgemini provides step-by-step support in moving to GCVE, leveraging existing environments as we extend them into Google Cloud. Find out more about how we work with our VMware clients to thoroughly assess their needs, estimate ROI, and create a secure, seamless and supported migration plan.

Interested in exploring the VMware to GCVE migration process? Contact us for more information and an assessment – googlecloud.global@capgemini.com

Combining the experience economy, where experiences matter more than products, with the circular economy, focused on reducing, reusing, and recycling, leads to a generative economy. This shift is vital to meet global needs while preserving the planet. To succeed, enterprises must evolve beyond current digital transformations.

The generative economy model creates continuous value through innovation, while also being a powerful means to address social and environmental challenges. Given that knowledge and innovation are important foundations in this model, it’s no surprise that thriving in a generative economy will require enterprises to leverage data to make better decisions.

This requires providing every team member with access to comprehensive data and knowledge to power innovation, within a collaborative environment that enables people to share results and act across the enterprise. This democratization of knowledge-driven decision-making will empower people and accelerate innovation, fostering more agile organizations and freeing up time to focus on the company’s largest, mission-critical challenges.

At Dassault Systèmes, we believe the virtual world will extend and improve the real world – and we anticipate experiences powered by virtual twins will be at the heart of this transformation.

Beyond the digital twin

Many companies – across all sectors, but led by those in aviation, automotive, and general manufacturing – are familiar with digital twins.

There’s no question digital twins have proven their worth. As the Capgemini Research Institute noted in its 2021 report Reflecting reality, organizations using digital twins enjoyed, on average, a 15% growth in key sales and operational metrics, an improvement in system performance of more than 25%, and a 16% boost to sustainability.

That said, digital twins rely on data generated in the past, even as they’re used to predict the future. Virtual twins are extending the possibilities. Virtual models powered by generative AI allow us to contextualize and elevate data into a unified, normalized representation of complex objects, systems, or factories.

In addition to providing a common referential for understanding, learning, and predicting, this approach unleashes the power of simulation to enable “What if” scenario modeling.

Data, decisions, and operations

Virtual twins are a blend of three important components:

• The digital model – an advanced representation of the product, the factory, or the enterprise. This model can be a 3D representation, a system model, or an ontology.
• Real-world data from across the enterprise’s ecosystem and beyond, fully contextualized, projected on the virtual twin, and available anytime, anywhere, via any device.
• People and processes, through powerful built-in collaboration methods and tools.

This ability to reduce the gap between data, decisions, and operations is the key that will enable companies to imagine, build, and deploy innovative new products and services.

Virtual twins are already transforming innovation at companies in many industrial sectors. Here are a couple of examples drawn from clients who are using the NETVIBES solution.

Superior supply management: In the manufacturing sector standards, regulations, and competition are raising expectations for automotive manufacturers. Every design decision is weighed against large sets of critical KPIs, and designers must balance price, weight, CO2 emissions, safety, and other vital criteria. With MOD/SIM/DATA and AI, designers can easily understand the impact of their decisions on all criteria. They can leverage knowledge, supplier value chain content and catalogues, procurement, logistics, best practices, new material specifications and other data to select the most relevant combination to create a better product. AI-enabled virtual twins help navigate this complexity and provide a new level of synthesis to guide decisions. Dassault Systèmes provides a major global automaker with a unique combination of artificial intelligence, machine learning, collaborative business processes, and an enriched single 3D data model of each vehicle. This helps the client better manage the business impacts of market volatility. The automaker can aggregate equipment designs, configurations, historical data, and forecasts to test different design scenarios in a virtual twin. The company can understand, anticipate, quantify, and optimize vehicle price and cost, and improve equipment-purchasing negotiations by sharing these insights with other stakeholders.

Better project oversight: In the infrastructure & cities sector – encompassing nuclear, oil and gas, and renewable energy – we use virtual twin experiences to help clients elevate data, from engineering and construction to operation and maintenance. Virtual twins are a game changer, providing science-based models for interpreting, understanding, and contextualizing real-world data from sensors. AI is accelerating this by helping customers learn from the past to navigate the future through predictive models, such as anticipating deviation risks in construction phases. An example of this is our work with India-based L&T Hydrocarbon.

“Virtual models powered by generative AI allow us to contextualize and elevate data into a unified, normalized representation of complex objects, systems, or factories.”

Maximizing the virtualization of knowledge

The virtual twin experience is the enabler to transform implicit information and know-how into explicit and actionable knowledge. At Dassault Systèmes, we provide our customers with virtual twin experiences that help them think and operate in a generative way and create new, net positive business models. Our virtual twin solutions build on our AI-augmented Industry Solution Experiences by leveraging the current acceleration of AI to maximize an enterprise’s ability to virtualize knowledge. This provides clients with fresh opportunities to leverage their employee know-how and other corporate assets to drive innovation.

The generative economy will extend the experience economy with sustainability and other imperatives. This will have a major impact on the way organizations work and will require connections between silos of people who don’t share the same backgrounds and expertise. The virtual twin experience will not only trigger this transformation, it will enable it to succeed – and organizations must start implementing such solutions now to ensure they’re prepared for future growth.

Innovation takeaways

The innovation imperative

Sustainability is making innovation even more important – and is just one factor disrupting current economic models. Companies must embrace solutions like virtual twins powered by AI to succeed in this rapidly evolving environment.

Turn data into a corporate asset

Companies must provide all employees – not just leaders and experts – with the ability to make informed, knowledge-driven decisions. Virtual twins leverage data and AI to enable a common understanding, shared learning, and coordinated, effective action.

Enterprises don’t exist in a bubble

True innovation and competitive advantages will increasingly require companies to understand information in context, then deploy solutions specifically designed to help leverage the insights derived from this contextualized data.

Interesting read?

Capgemini’s Innovation publication, Data-powered Innovation Review | Wave 8 features contribution from leading experts from Capgemini and esteemed partners like Dassault Systèmes, Neo4j, and The Open Group. Delve into a myriad of topics on the concept of virtual twins, climate tech, and a compelling update from our ‘Gen Garage’ Labs, highlighting how data fosters sustainability, diversity, and inclusivity. Embark on a voyage of innovation today. Find all previous Waves here.

Periodically, transformative technology emerges that instigates profound changes across multiple industries, redefining the way we live our lives and conduct business. Distributed ledger technology (DLT) is one contemporary example.

DLT operates on the principle of decentralization, structured upon layered protocols and frameworks. At its core, it distributes transaction data across numerous points, all connected to a shared ledger acting as the golden source – removing many reconciliation efforts. This shared ledger, updated collectively by “nodes” utilizing diverse consensus mechanisms, forms the backbone of DLT’s architecture, with blockchain technology driving its mechanics.

Tokenization and physical assets

One of DLT’s most striking applications is the tokenization of physical assets, creating what are known as digital assets. From tokenized securities to cryptocurrencies and central bank digital currencies (CBDCs), these digital representations revolutionize the trading landscape. Tokenization empowers investors to transact assets with unprecedented speed and fractional ownership, fostering liquidity and lowering bureaucratic hurdles.

Impact on centralized clearing parties (CCPs)

Through DLT, counterparties engage in direct trading, bypassing traditional intermediaries. This raises pertinent questions about the future role of CCPs in this DLT-driven paradigm shift. But before we look into the future, let’s examine some current developments in this arena.

What’s the latest in the industry?

BlackRock, a globally leading asset manager, has revealed plans for a digital fund leveraging Ethereum’s blockchain, while simultaneously acquiring a stake in Securitize, a platform facilitating asset tokenization. This strategic move underscores their commitment to tokenization infrastructure and a shift in the approach.

Meanwhile, Cleartoken are a new industry disrupter in this space, who recently raised $10 million in seed investment. Their declared mission is to be one of the first entrants in the CCP space for digital assets. The plan is to establish a central clearinghouse that will mitigate risk and encourage wider institutional adoption of crypto currencies by creating a more secure trading environment.

As the industry forges ahead, regulators and governments starting to recognize the potential of digital assets. Treasury Secretary Janet Yellen advocates for US leadership in the crypto space, while regulators from the UK, Singapore, Switzerland, and Japan collaborate to explore digital asset use cases. Additionally, the US Securities and Exchange Commission’s approval of Bitcoin ETF earlier this year highlights the evolving regulatory landscape.

Opportunities for traditional CCPs

Despite the potential threat emerging to the role of traditional CCPs – they also have opportunities to take advantage of. Many parties will require guidance and assistance to navigate the digital assets infrastructure space. CCPs are uniquely placed with their existing relationships to both help inform regulators, and guide parties through the new regulatory landscape. CCPs could also take a lead in setting up technical infrastructure within organizations to make digital asset trading possible. Clearing parties’ familiarity with the market, individual participants and the regulators mean they are uniquely placed to be at the forefront of change if they have the right strategy. This would allow them to continue their role as facilitators, with less control of the processing, but more influence within the individual parties. There will also be a demand for hard copies of ledgers, at fixed points in time. As leaders on the blockchain with high stakes in various chains, existing CCPs could be able to produce this.

Whether participants strongly believe in the power of DLT or have lingering doubts, one thing is clear: DLT is here to stay and it’s changing the world of post-trade financial services forever.

As we talk about Artificial Intelligence (AI) and how it’s changing, I’m reminded of a book written by Marvin Minsky in 1986 called “Society of Mind”. 

 Minsky’s ideas about how our minds work are still very relevant today.

In his book, that we had to read at the University when studying AI, Minsky proposes that our minds aren’t just one big, single thing. Instead, they’re made up of many smaller parts, like tiny agents, that work together to help us think and behave. This idea is similar to what’s happening in AI right now, where many small, independent agents work together to solve problems.

The idea of Agentic AI, where AI systems can make their own decisions and act on their own, is also connected to Minsky’s ideas. Each agent in a multi-agent system is like a small, individual person with its own goals and tasks, working together to make the system smart and useful.

When we think about creating Artificial General Intelligence (AGI), Minsky’s ideas are helpful. Instead of trying to create one super-smart AI, we might be able to make more progress by building many smaller, specialized AI agents that work together. Minsky’s book encourages us to think differently about how we build AI. By focusing on small, specialized agents that work together, we can create more powerful and flexible AI systems. His ideas remind us that intelligence, whether it’s human or artificial, comes from many small parts working together.

For me it is a Deja Vu (as I read this book many times), but Minsky’s ideas are important to remember. They help us understand that intelligence is about many small parts working together, not just one big, single thing.

For those of you needing a suggestion for (re-)reading, here´s one classic for your summer time!

Marvin Minsky’s “Society of Mind”, 1986

From data deluge to insights: How Gen AI is transforming document search and knowledge management in financial services

Organizations, particularly in the financial services sector, have long mastered the management of structured data within relational databases. These firms have honed their expertise in data storage, ensuring data quality, and leveraging this data for applications, reporting, and analytics. However, the advent of Gen AI has transformed the handling of unstructured data, unlocking new possibilities in knowledge management and search capabilities across enterprise processes and workflows.

While structured data benefits from centralized storage and easy retrieval through tables and keys, managing unstructured data presents unique challenges. Ensuring that documents are not duplicated across various storage platforms like SharePoint, Teams, and Content Management Systems is less straightforward. Although some progress has been made in solving storage issues, the rigor seen in relational databases is often lacking.

The time-consuming process of gathering and auditing information from large collections of documents can significantly hamper productivity. The complexity increases when integrating structured and unstructured data to provide a seamless and efficient user experience for business, technical, and operational purposes. The value of advanced, Gen AI-powered search and knowledge management systems becomes evident, offering speed, accuracy, and scale, thus enhancing overall organizational efficiency.

Approaching the problem from multiple fronts

Now that we’ve examined the challenges and business value of this organizational capability, let’s discuss how to address it from multiple angles. The following chart offers an overview of the key dimensions involved in building this capability. In the subsequent sections, we will delve into the specifics of how AI and advanced techniques can be effectively implemented across the organization.

1) Information Stewards for feedback loop

The role of Information Stewards in ensuring ongoing data readiness is crucial. Information Stewards are responsible for monitoring and managing the quality, security, and compliance of the data environment. Their oversight ensures that the data remains accurate and secure. Additionally, integrating feedback from Information Stewards is essential for continuously improving data quality and AI model performance. This ongoing process helps maintain a high standard of data readiness and enhances the effectiveness of AI implementations.

The organizational structure of the financial services firm will determine the specific responsibilities of each Information Steward. For example, every line of business (LOB) and operational horizontal, such as contact centers, back-office operations, and strategy teams, will have designated stewards. If the firm uses disparate content management systems, additional effort will be required to standardize unstructured data governance processes, ensuring the integrity of the unstructured data landscape.

2) AI-augmented data enhancement

To ensure the quality, accuracy, and completeness of data, several capabilities are essential. Deploying classification algorithms to automatically identify document types and topics is crucial for effective data classification. Tag generation and metadata management play a significant role by automatically generating metadata tags for roles, topics, and divisions. Additionally, adhering to data standards is necessary to ensure that documents are reviewed and approved before publishing.

Document standards, such as mandatory sections for an intended audience, role-based security permissions, and change audit trails, must be strictly enforced. Approaches need to be developed to automate data augmentation from system logs, incorporating this information into service desk tickets to record which systems were accessed for resolving issues. The goal is to enhance human entries with automated data from logs and other sources, thereby reducing user friction and improving the accuracy and completeness of information.

3) Database for unstructured data augmented with structured data

Combining structured and unstructured data involves several key strategies. Implementing vector databases for dynamic indexing of unstructured data significantly improves the speed and accuracy of search queries. Enhancing unstructured data with structured data, such as document metadata and access permissions, adds valuable context.

Adding user role-based context makes large language models (LLMs) more effective in addressing queries. By including roles and their key performance indicators (KPIs) as additional context, Gen AI applications can better understand the motivations behind specific questions. This enables them to respond to general queries, such as “What are the top three things I should worry about today?” with greater expertise and relevance.

Additionally, exploring advanced techniques like combining Retrieval-Augmented Generation (RAG) architecture with knowledge graphs can further augment the enterprise context, providing a more comprehensive and efficient data management solution. GraphRAG approaches add an extra advisory layer that helps identify related document chunks specific to the document repository being queried.

4) Hybrid search vector + semantic search

To enable quick and effective data search and presentation to end users, a hybrid search and agentic architecture is essential. This approach combines the precision of vector search with semantic search to enhance search accuracy. Result enhancement is achieved through ranking fusion techniques, which merge results from both search types.

Additionally, the ability to call APIs across multiple domains, such as CRM, document repositories, service desks, and requirements, further enriches the search capabilities. An agentic architecture, with libraries for specific functionalities, ensures an improved customer experience (CX). This architecture allows AI libraries to augment Gen AI applications’ capabilities, such as performing mathematical calculations, rendering reports, and creating SQL queries against specific databases.

This evolution is crucial as it enables applications to explore areas like intelligent decision-making, rules execution, and product recommendations. The goal is twofold: first, to enhance enterprise context retrieval, and second, to augment Gen AI with AI and other APIs to deliver a superior customer experience.

5) Establish process for alerts for missing information with workflow

To automate continuous monitoring of processes and workflows, it’s essential to integrate systems for alerts and monitoring. Establishing a monitoring and alerts system allows for the oversight of data quality and completeness, promptly notifying teams of any anomalies or gaps.

Once alerts are triggered, workflow automation is used to respond efficiently, with predefined workflows in place to address and rectify identified data issues. This ensures timely and effective resolution of data quality problems.

Given that this is an ongoing effort, there is a pressing organizational need to keep the data fresh, up-to-date, and complete with the highest level of quality. This dedication to data integrity ensures that users receive the best possible information when they need it.

Bringing it all together

While financial services firms have long excelled in managing structured data within relational databases, the advent of Gen AI has opened up transformative possibilities for handling unstructured data. This evolution is crucial for enhancing knowledge management and search capabilities across enterprise processes and workflows. Managing unstructured data poses unique challenges, including preventing document duplication across various storage platforms and ensuring data accuracy and completeness.

To overcome these challenges, the problem needs to be approached from multiple fronts:

• Information Stewards ensure data quality, security, compliance, and continuous improvement of AI performance.
• Classification algorithms and metadata management ensure data quality and adherence to standards.
• Combining structured and unstructured data with vector databases and RAG architecture improves search accuracy.
• Incorporating hybrid vector and semantic search, ranking fusion, and API integration further refines search precision.
• Monitoring and alert systems with automated workflows maintain data quality and completeness.

By addressing these challenges from multiple fronts and leveraging advanced AI techniques, financial services firms can unlock the full potential of their data, driving superior decision-making and operational efficiency.

Want to learn more?

Check out the latest reports from the Capgemini Research Institute, packed with cutting-edge insights on Generative AI. Explore topics such as Turbocharging software with Gen AI, Harnessing the value of Gen AI, and Why consumers love Gen AI.

Click here to download a PDF copy of this expert perspective.

Sustainability has evolved from just a buzzword to the backbone of aerospace strategy.

Orders for new aircraft are flooding in, pushing production lines to their limits. Airlines, emerging from pandemic-induced slowdowns, are racing to meet rebounding travel demands. Yet, even as manufacturers scramble to ramp up output, they face a complex, evolving, regulatory landscape to reduce emissions.

This collision of forces – surging production needs and stringent sustainability requirements – is reshaping the aerospace landscape. In boardrooms and on factory floors, industry leaders grapple with a complex equation: how to deliver more aircraft, faster, while simultaneously slashing carbon footprints and reducing environmental impacts.

What’s fascinating is how this challenge is fostering unprecedented collaboration within our industry. In the International Aerospace Environmental Group (IAEG), we’re witnessing something truly remarkable: experts from different aerospace companies, often competitors, are coming together to share insights and work on sustainability solutions. Imagine Airbus and Boeing sitting at the same table, working collaboratively – it’s a testament to how critical this issue has become for all of us.

This collaborative approach isn’t born from mere goodwill, but from necessity. The complex web of Science-Based Targets, rigorous reporting requirements, and the sheer scale of the sustainability challenge have made it clear: no single company can solve this alone. We need collective action, shared knowledge, and industry-wide innovation.

Three key areas have become the focus of these collaborative efforts: sustainable aviation fuels, circular economy, and decarbonization. These interconnected domains offer significant potential for reducing the industry’s environmental impact while promoting sustainable practices across the value chain.

Sustainable Aviation Fuels

In the race to decarbonize aviation, Sustainable Aviation Fuels (SAFs) have emerged as the industry’s most promising solution to significant emissions reduction without requiring overhauls to existing aircraft or airport infrastructure.

The urgency surrounding SAFs stems from mounting pressure on the aviation sector to cut its carbon footprint. With air travel demand projected to grow substantially in the coming decades, SAFs represent a critical bridge, allowing the industry to reduce emissions while longer-term solutions like electric and hydrogen propulsion mature.

The potential of hydrogen as a renewable energy source is promising, but its adoption requires a comprehensive evaluation of the global aviation ecosystem. The complexities of hydrogen fuel implementation extend beyond aircraft design to encompass airport infrastructure and operational procedures. Safety considerations are paramount as well. At Capgemini, our strength lies in our ability to collaborate across industries to support our clients in finding the right balance between innovation and practicality, and the right ecosystem of stakeholders to scale these solutions.

The industry recognizes that time is of the essence. Collaborative initiatives are accelerating SAF development and deployment, from securing sustainable feedstocks to establishing robust supply chains. Cross-sector partnerships, including those with agricultural and waste management industries, are being forged to ensure a steady, sustainable supply of raw materials for SAF production.

Circular Economy

The aerospace industry stands at a critical juncture where environmental regulations and business imperatives converge. Circular economy practices offer a powerful solution to meet stringent sustainability targets while simultaneously transforming our business model. This shift from a linear “take-make-dispose” approach to a circular “reduce-reuse-recycle” model is not just an environmental necessity—it’s a strategic imperative.

Regulatory pressures, including Science-Based Targets and rigorous reporting requirements, are pushing the industry to rethink its approach to resource use and waste management. Simultaneously, the potential for cost savings, new revenue streams, and enhanced supply chain resilience make circular economy principles attractive from a business perspective.

Collaboration has emerged as the key to unlocking these opportunities. By working together, aerospace companies are finding innovative ways to overcome the technical, logistical, and economic barriers to circularity. One exemplary initiative in this collaborative landscape is the Lifecycle Optimization for Aerospace platform, developed through a partnership between Capgemini and Amazon Web Services (AWS).

This platform addresses a critical pain point: the management of aircraft and parts maintenance history. By digitizing this process, it unlocks new possibilities for parts reuse, repair, and recycling. Leveraging cloud computing and AI, the platform analyzes maintenance records with unprecedented efficiency, identifying opportunities for reuse and flagging components for recycling when repair is no longer viable.

The collaboration extends beyond Capgemini and AWS, with industry leaders like Air France and Safran joining as early adopters. This multi-stakeholder approach ensures the solution addresses real-world needs across the aerospace value chain.

While the platform is a significant step forward, it’s just one example of industry-wide circular initiatives. Companies are also collaborating on developing recyclable materials and designing components with disassembly and reuse in mind.

The transition to a circular economy in aerospace faces challenges, including the complexity of aircraft systems and high-performance requirements. However, the potential benefits—both environmental and economic—make this transition imperative.

Decarbonization

Decarbonization in the aerospace industry extends far beyond reducing emissions from aircraft operations. It now encompasses a holistic approach that addresses the entire value chain, from sustainable manufacturing practices to innovative aircraft design.

While reducing carbon emissions from fuel combustion remains a primary focus, the industry recognizes the critical importance of decarbonizing production processes. Sustainable manufacturing has become a key priority, with companies aligning their operations with environmental goals such as reducing energy consumption, waste, and water usage at production sites.

The Energy Command Centre in India is a great example of our industry’s commitment to sustainable manufacturing. This integrated platform monitors, optimizes, and controls all building assets consuming energy, including data centers, seamlessly blending digital and physical systems across a diverse set of connected devices. By leveraging best-in-class technologies, advanced AI, machine learning, and IoT, the center optimizes energy performance at all levels. This includes offline reporting, dynamic real-time monitoring, and proactive control and optimization. Organizations utilizing this platform can achieve up to 30% in energy savings.

Central to the center’s capabilities is the implementation of digital twins. These virtual replicas of physical assets, including entire facilities, allow for real-time simulation and optimization of manufacturing processes. By linking production decisions with key performance indicators such as lead times and sustainability metrics, the digital twins provide invaluable insights for enhancing both operational efficiency and environmental performance.

Decarbonization efforts extend to aircraft design and material selection as well. The industry is collaboratively exploring lightweight, aerodynamic designs that minimize fuel consumption. Simultaneously, there’s a push towards adopting sustainable materials to reduce the overall environmental footprint. Advances in material sciences, particularly in the development of advanced composites, are yielding significant efficiency gains and contributing to the decarbonization of the entire aerospace supply chain.

Racing against time: the power of collaboration in aerospace sustainability

The aerospace industry’s journey towards sustainability is a race against the clock. We cannot expect to have hydrogen-powered aircraft certified and flying tomorrow, since the technologies are barely mature. It’s a complex and lengthy endeavor, but the pressure is on.

Regulatory deadlines for emissions reduction loom on the horizon. Market demands for more sustainable air travel are intensifying. Meanwhile, the climate crisis itself adds urgency to our efforts. Yet, the inherent complexity of aerospace innovation – from concept to certification – means that transformative changes often unfold over decades rather than years.

However, this challenge is precisely where the power of collaboration comes to the forefront. By working together, aerospace companies can significantly accelerate the pace of innovation and implementation:

1. Shared Research and Development: Collaborative R&D efforts allow for the pooling of resources, expertise, and data. This can dramatically speed up the maturation of new technologies, from sustainable aviation fuels to electric propulsion systems.

2. Streamlined Certification Processes: Industry-wide cooperation, including partnerships with regulatory bodies, can help streamline and expedite certification processes for new sustainable technologies without compromising safety.

3. Rapid Scaling of Solutions: Once innovations are proven, collaborative networks can facilitate their rapid adoption across the industry. This is particularly crucial for infrastructure-dependent technologies like hydrogen fuel.

4. Knowledge Sharing: Open exchange of best practices and lessons learned can help companies avoid redundant efforts and overcome common hurdles more quickly.

5. Supply Chain Alignment: Coordinated efforts across the supply chain can ensure that sustainable practices and technologies are implemented coherently and efficiently.

We must run together in the race to a sustainable future in aerospace. Through continued collaboration, we can accelerate our progress, turning the obstacle of time into an opportunity for unprecedented innovation and transformation in our industry.

Learn more:

Digital Continuity in the Aerospace Industry

Digital Twins in Aerospace and Defense

Intelligent Supply Chain for the Aerospace and Defense Industry

Lifecycle Optimization for Aerospace and Defense