With many CIOs reporting to CFOs, the emphasis has largely shifted to cost efficiency over proactive innovation. This gap opens the door for digitally minded, innovation-driven executives like CDOs to bridge IT, engineering and manufacturing, enhancing data integration, accessibility and actionable insights. This coordination across Product Lifecycle Management (PLM), Enterprise Resource Planning (ERP), Material Requirements Planning (MRP) and other core systems supports a holistic approach to data-driven innovation.

A 2023 Deloitte Insights report highlights three factors driving CDOs’ effectiveness in transforming business performance: aligning their vision with business strategy, controlling data management practices and cultivating influential relationships to extend their reach. Approximately 67% of Fortune 500 companies now have a CDO role, underscoring the priority of data leadership across industries.

This article examines how the CDO elevates IT’s mandate, aligning data strategies with engineering and operational needs to make data a driving force behind manufacturing and product innovation.

The CDO drives end-to-end data integration

In manufacturing, the integration of PLM and ERP is not just a technical requirement; it is an essential component of strategic growth. The end-to-end flow of data between product design and production (facilitated by PDM and PLM) and operational processes (led by ERP, MRP and MES systems) boosts collaboration, shortens time-to-market and improves product quality —ultimately unlocking greater innovation potential through real-time, data-backed insights across the product lifecycle.

The CDO approaches this integration challenge with a strategic vision. By dismantling data silos and enabling seamless information flow between PDM, ERP, MRP, MES and CRM repositories, the CDO empowers cross-functional teams with real-time insights that support timely, informed decision-making. This effort involves more than just integrating systems; it requires fostering a culture of continuous collaboration and improvement. The CDO mandate is therefore more about people and stakeholder alignment than about data analytics and AI governance.

Addressing some key questions can drive value from cross-functional alignment, including:

• Are design and engineering teams optimizing products for customer needs and regulatory standards?
• How effectively are production teams implementing efficient manufacturing strategies?
• Are procurement teams securing cost-effective and sustainable supplier partnerships?
• Is the sales strategy aligned with product capabilities and customer feedback?
• How efficiently are we responding to market changes based on real-time data?

The CDO role goes beyond data integration and technical alignment, fostering a culture where data serves as the foundation for innovation and responsiveness in product development. With well-managed and accessible data, innovation flourishes, enabling organizations to meet market demands proactively and competitively.

The CDO bridge the engineering-IT divide

The CDO role in bridging engineering and IT is central to achieving seamless collaboration across all stages of product development and manufacturing. Engineering and manufacturing teams often rely on specialized tools and platforms for design, simulation and testing, which may not always be compatible with traditional IT infrastructure. This disconnection can isolate engineering efforts from broader strategic objectives, slowing down innovation and creating operational silos. Here, the CDO brings a strategic approach to data integration, aligning specialized engineering tools with enterprise systems and ensuring that engineering efforts contribute directly to overarching business goals.

By establishing data pathways between engineering and IT, the CDO makes real-time insights available across departments, enhancing collaboration and ensuring engineering data supports PLM and ERP solutions. This integration goes beyond technical alignment. It fosters a shared understanding and sense of purpose across functions, allowing engineering teams to innovate with a clear view of how their efforts impact end-to-end product strategy. When engineers can seamlessly access critical information and align their objectives with broader business needs, the organization can drive faster time-to-market, improve product quality and make more informed decisions at every stage of the product lifecycle.

A 2023 survey published by the Harvard Business School reported that “The CDO role is poorly understood and incumbents of the job have often met with diffuse expectations and short tenures. There is a clear need for CDOs to focus on adding visible value to their organizations.” Furthermore, the authors, Davenport et al., highlighted that “Of the CDOs surveyed, 41% said they define success by achieving business objectives —significantly more than those who measured success in terms of change management or culture shift (19%), technical accomplishments (5%), prevention of serious data problems (2%), or an equal combination of these factors (32%).”

As the CDO aligns data strategies to break down engineering silos, they create a connected ecosystem where engineering insights actively inform operations, design and customer needs. By prioritizing and advocating for the distinct data needs of engineering, the CDO elevates this function from an isolated operation to a core, strategic component of the organization. This alignment allows the business to respond more rapidly to customer demands, ensuring that innovation efforts stay on track and deliver tangible value across departments.

Leveraging technology adoption to create value

While CIOs are often tasked with “sweating the assets” and managing technical debt, the CDO can advocate for engineering’s unique data requirements, highlighting their role in broader strategic goals. An effective CDO can reshape the traditional IT role, transforming it from a cost center focused on operational maintenance into a proactive, strategic partner in product innovation and manufacturing excellence. By aligning IT functions with product development and manufacturing priorities, the CDO facilitates a shift from reactive data management to data-driven growth and continuous innovation.

The CDO fosters digital transformation by building a data governance framework that prioritizes data quality, accessibility and security—empowering IT to act as an enabler of data-driven insights rather than just a gatekeeper of digital resources. Through advanced analytics, machine learning and cloud-based solutions, the CDO enables IT to leverage data assets for predictive maintenance, supply chain efficiency and agile product design. As a result, IT contributes actively to achieving the organization’s strategic goals, moving beyond maintenance to deliver predictive insights that inform every stage of the product lifecycle.

Collaboration between the CDO and CIO is also critical in identifying and prioritizing technology investments that drive the highest business impact. This synergy ensures that while the CDO focuses on strategic, data-driven growth, the CIO maintains a stable and secure IT foundation—balancing innovation with operational resilience. By harmonizing their objectives, the CDO and CIO can allocate IT resources more effectively, emphasizing data initiatives that deliver substantial returns while safeguarding the organization’s core infrastructure.

In times where agility and innovation determine market leadership, the CDO stands as a visionary force, aligning data, technology and people to redefine business potential. By embedding data-driven strategies at every level, the CDO not only bridges operational silos but also empowers organizations to drive sustainable growth, anticipate change and maintain a competitive edge. In the digital age, the CDO leadership is not just valuable—it is essential for turning data into the organization’s most powerful asset.

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With a foundation already set in AI and generative AI capabilities, Siemens is taking a strategic leap to deepen its offerings in areas such as Product Lifecycle Management (PLM) and Digital Twins.

Yet, the acquisition raises critical questions: Is Siemens advancing its strategic edge by embedding next-level AI and knowledge graph technologies, or is it scrambling to keep up in a landscape that is moving faster than ever?

Elevating AI-driven PLM and digital twins

Siemens’ integration of Altair’s powerful AI, simulation and high-performance computing tools into its PLM tech suite, particularly within Teamcenter and Simcenter, offers a potential transformation in how digital twins and simulations are used across engineering and manufacturing. Altair’s deep expertise in physics-based simulations, including mechanical and electromagnetic modeling, could allow Siemens to develop more sophisticated digital twins that not only represent physical products but also predict behaviors and outcomes with high fidelity.

With Altair’s technology, Siemens can push digital twin capabilities beyond basic visualization and monitoring, creating a system that incorporates real-time data, predictive analytics and adaptive simulations. This would enable manufacturers to make informed, AI-driven decisions at every stage of the product lifecycle, from design and development to production and maintenance.

However, despite Siemens’ existing portfolio, which includes substantial AI and generative AI tools, the acquisition raises a critical question—how effectively can Siemens embed these capabilities as a core, transformative feature within its PLM platform? Without a clear path to seamlessly integrate AI across its offerings, Altair’s capabilities risk being relegated to auxiliary add-on features, potentially limiting their business impact. For Siemens, this move is more than just adding tools; it’s about embedding intelligence deeply within the end-to-end PLM framework, making AI a central component of its digital transformation strategy.

Enhancing digital twins with HPC

Siemens is marketing itself as a leader in digital twin technology, primarily through its Xcelerator platform, which integrates real-time operational data to improve asset management, production efficiency and product quality. Altair’s HPC capabilities could significantly enhance Siemens’ digital twin offerings by allowing more complex, detailed, and faster simulations—an essential component of predictive maintenance and optimization for manufacturers.

The integration of HPC into Siemens’ digital twin ecosystem could be transformative, enabling simulation models that accommodate an unprecedented scale of data and complexity. For instance, manufacturers could simulate entire production lines or supply chain networks, gaining insights that help them optimize operations, reduce energy consumption, minimize downtime and predict implications from product changes. This is particularly relevant as industries move toward more sustainable and resilient operations.

However, leveraging Altair’s HPC across Siemens’ existing infrastructure poses some challenges. HPC solutions typically require specialized infrastructure, substantial processing power and technical expertise. Siemens will need to carefully consider how to bring HPC capabilities into mainstream use within its portfolio, including positioning within its maturing SaaS offering. The risk here is that without a robust integration plan Altair’s HPC tools may remain isolated and less affordable, providing limited impact and reducing the transformative potential of this acquisition.

Knowledge graph technology: connecting data with digital thread

Altair’s recent acquisition of Cambridge Semantics, a developer of knowledge graph and data fabric technologies, brings new dimensions to the integration of enterprise data across complex manufacturing ecosystems.

Knowledge graphs provide a framework for Siemens to unify and contextualize vast amounts of data from disparate systems—an essential step for effective AI-driven insights and accurate digital twin models. With knowledge graphs, Siemens could break down data silos, connecting information from PLM, digital twins, and other systems into a cohesive whole, creating a seamless digital thread across the lifecycle.

Incorporating Cambridge Semantics’ knowledge graph technology into Siemens’ portfolio could lead to a new era of “data-rich” digital twins, where structured and unstructured data come together to provide a more comprehensive, actionable view of products, assets and operations. By grounding generative AI models in real-world data, knowledge graphs could improve response quality and deliver contextual insights, allowing engineers and operators to make better, faster decisions.

Yet, the question remains: can Siemens adapt this advanced data integration technology effectively in an industrial setting? Cambridge Semantics’ data fabric has been proven in sectors like defense, life sciences, and government. Adapting it for manufacturing will require Siemens to navigate industry-specific complexities. Without careful implementation, the risk is that knowledge graph technology will be underutilized—merely another tool rather than a strategic game-changer in Siemens’ PLM and digital twin offerings.

Strategic opportunity or catch-up?

The acquisition of Altair could empower Siemens to lead in AI-driven PLM, high-fidelity simulations and data-enriched digital twins. But the road ahead demands more than technological additions; it requires Siemens to deeply integrate these capabilities within its core platforms and ensure they serve as transformative, essential components rather than optional add-ons.

For Siemens, the challenge lies in more than simply acquiring AI—it’s about operationalizing it. By embedding Altair’s and Cambridge Semantics’ technologies as central pillars in its software ecosystem, Siemens has the opportunity to redefine industrial intelligence in manufacturing. Can Siemens realize this vision to become a true leader in AI-driven industrial software, or will it struggle to fully leverage these assets, ending up as a late entrant in a rapidly advancing field?

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At the Farnborough Airshow in July 2024, Siemens and BAE Systems announced a five-year collaboration to accelerate digital innovation in engineering and manufacturing. Using Siemens’ Xcelerator platform, this partnership seeks to transform processes within BAE Systems’ Air sector through FalconWorks, its Research and Development (R&D) division. The R&D center fosters an open innovation ecosystem, connecting suppliers, SMEs, governments, research organizations, and academia to “accelerate the innovation of future air power through the development of technology and capabilities.” It unites approximatively 2,000 experts across 11 sites in the UK.

This agreement builds on a longstanding relationship, deploying Siemens’ advanced digital software, such as NX and Teamcenter to enhance sustainability, industrial digitalization, and supply chain modernization. Leaders from both companies emphasized the collaboration’s potential to drive Industry 4.0 advancements and achieve significant digital transformation in aerospace manufacturing. Iain Minton, BAE Systems’ Technology Capability Delivery Director, noted, “Siemens understands the complexities of our operating environment, so we can very quickly mature an idea to the point where it is put into practice, for example when we are looking to implement and optimize new engineering, support, or manufacturing capabilities.”

A digital engineering ecosystem for open innovation

BAE Systems’ FalconWorks is not only looking at solving today’s challenges; “it is the agile innovation powerhouse driven by […] technology teams that will develop the game-changing technologies of the future.” Simply put, it focuses on scanning the technology horizon to identify and develop groundbreaking building blocks of the future in the Aerospace and Defense sector. Maintaining an edge in such competitive landscape implies developing industry standards, working with regulators to ensure these are acceptable to the society from a safety and sustainability perspective, while focusing on effective routes to market for successful commercialization.

Fostering an open innovation ecosystem, the company embarked on a multi-year strategic investment in Digital Engineering (DE) to digitalize its systems engineering and integration capabilities, “investing in digital infrastructure and virtual, collaborative Digital Engineering Capabilities Labs (DECL) to drive rapid innovation, state-of-the-art digital technologies, and cloud migration.” This includes collaboration with SMEs, academia, legislators, and industry leaders, along with co-funding start-ups to develop new technologies.

Per a 2020 whitepaper, BAE systems elaborated on its Advanced Integrated Data Environment for Agile Manufacturing, Integration and Sustainment Excellence (ADAMS) reference architecture to fulfil this vision: “this digital enterprise is built on a model-based, integrated development or data environment that supports multi-disciplinary, multi-organization stakeholders and leverages product-line reference architectures and a shared model library to develop, deliver, and sustain a system through its lifecycle.” Clearly, the digital ecosystem is only an enabler, part of a data layer foundational to drive process and product innovation.

Aerospace digital twins and data management

PLM serves as the backbone, integrating technologies, data, and processes to ensure seamless information flow across business functions and the entire product lifecycle—from concept and design to manufacturing, maintenance, and recycling. PLM processes require connected data flows across the manufacturing and extended enterprise ecosystem. Through integration and workflow automation, all product data, from design to production, must be digitized and interconnected, facilitating seamless communication between systems, machines, and teams. Such integration allows for real-time monitoring, data-driven decision-making, and automation, ensuring that the factory operates efficiently and can quickly adapt to changes in demand or production requirements.

Additionally, PLM supports continuous improvement by enabling feedback loops from the factory floor back to design and engineering, leading to optimized processes and product quality. For instance, this includes the implementation of advanced manufacturing techniques, such as additive manufacturing, 3D printing, and automated assembly, connecting CAD and software data with production processes by ensuring that all design and manufacturing data are centrally managed and accessible. In the context of BAE’s vision, PLM can facilitate the integration of Digital Twins, virtual representations to allow real-time monitoring and optimization of manufacturing processes—ensuring that the factory can respond dynamically to changes and demands. Aerospace Digital Twins are crucial for driving Industry 4.0 by enhancing efficiency, reducing costs, driving quality adherence, compliance, and sustainability. The top five Digital Twins essential for this purpose include:

1. Product Digital Twins: Represent physical aircraft or components throughout their lifecycle, enabling real-time monitoring, predictive maintenance, and performance optimization to reduce downtime and extend asset lifespan.
2. Process Digital Twins: Model and optimize manufacturing and assembly processes, allowing for quick identification of inefficiencies, waste reduction, and overall production quality improvement.
3. Supply Chain Digital Twins: Provide a real-time, end-to-end view of the supply chain, managing disruptions, optimizing logistics, and ensuring timely delivery of components.
4. Operational Digital Twins: Monitor in-service aircraft and systems, enabling optimization of flight paths, fuel consumption, and maintenance schedules for better performance and reduced costs.
5. Human Digital Twins: Simulate interactions between humans and machines, optimizing human factors, enhancing training, and improving safety by modeling human responses to various scenarios.

Connected, sustainable asset optimization

A connected intelligent factory is a data-driven manufacturing environment that uses advanced automation, real-time analytics, and interconnected systems to optimize aerospace component production, assembly, and maintenance. The Aerospace industry strives to balance cutting edge innovations to foster competitive advantage with through-life optimization of complex assets to effectively capitalize long-lifecycle products. Asset compliance traceability and throughout monitoring is essential to enable Aerospace and Defense, and other heavy regulated operations, supporting new business models—from product development to full in-service operations management.

To that effect, BAE Systems’ Digital Intelligence division acquired Eurostep in 2023 to accelerate the development of its digital asset management suite, PropheSEA, a platform to “consolidate and share […] complex asset data securely, allowing assets to be managed proactively, reducing operating costs and maximizing asset availability.” Mattias Johansson, Eurostep CEO, highlighted that “Eurostep has collaborated with BAE Systems for many years with […] ShareAspace sitting at the heart of Digital Intelligence’s Digital Asset Management product suite [to help organizations] securely collaborate across the supply chain and cost effectively manage their assets through life.” Regulators also require through-life carbon footprint measurement, which can be difficult to forecast with products whose asset life can span 40 to 50 years.

As presented in one of the ACE conferences championed by Aras in 2016, Kally Hagstrom, then Manager of Information Systems with BAE Systems, explained why complex long-lifecycle products require a PLM strategy that enables high-level of resiliency. BAE Systems then initiated the implementation of Aras Innovator alongside its legacy Teamcenter platform to consolidate several PLM business capabilities, from requirements to change management, systems engineering, supplier collaboration, process planning and MBOM management, document and project, as well as obsolescence management. Clearly, based on the recent Siemens partnership extension, the legacy Teamcenter environment is also there today at BAE Systems, regaining ground in the maintenance, repair and overhaul (MRO) space and/or expanding further into downstream manufacturing digitalization. Furthermore, it would be interesting to hear if/how BAE Systems is possibly driving the coexistence of multiple PLM platforms in its DE ecosystem to drive open innovation and manufacturing, possibly leveraging its 2023 investment in Eurostep.

To paint the full picture, it would be necessary to dig more into how BAE Systems collaborate with its supply chains and manage its intellectual property. This would also comprise a broader understanding of how the OEM connects the dots across its PLM, ERP and MES landscape to drive a truly end-to-end digital and data connected landscape. By enabling sustainable design and efficient resource management, integrated PLM can help reduce the environmental impact of aerospace manufacturing. This aligns with BAE’s broader goals of innovation and sustainability, ensuring that BAE Systems’ Factory of the Future is both technologically advanced and environmentally responsible.

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In simple terms, digital transformation is about using modern technology to make organizations run better and drive more value to their customers, employees and partners. It often encompasses improvements in decision-making, operations and customer service, and it opens opportunities to rethink and reshape how a business operates. Whether through Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), Product Lifecycle Management (PLM), Manufacturing Execution Systems (MES), or advanced technologies like Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT), digital transformation helps redefine business operations for the future. However, the term is often used loosely, becoming synonymous with general technology adoption.

On the other hand, business transformation goes beyond simply implementing new tools. It involves structural changes, strategy overhauls, and redefined goals in response to evolving opportunities, challenges, or long-term visions. Digital tools may be central to enabling these shifts, but the key to successful transformation is in rethinking how the business operates.

To stay competitive, businesses need to undergo both digital and business transformations in tandem, as technological advancements often necessitate cultural, workflow, and strategic changes to fully unlock their potential.

Value drivers for digital transformation

Digital transformation rests on four foundational pillars that enable organizations to improve and innovate. These pillars include:

1. Process and operating model transformation: Redesigning how work gets done across the organization to improve effectiveness, efficiency, agility, speed and value delivery.
2. Technical debt improvement: Reducing reliance on outdated processes and systems that are expensive to maintain and limit the business’s ability to innovate quickly.
3. Integrated ecosystem and data flow: Breaking down silos to ensure seamless data sharing across systems, departments, and possibly organizations—improving decision-making and operational alignment.
4. Data analytics and insights: Leveraging data as a strategic asset for real-time decision-making, predictive analysis, and improved business outcomes.

These four pillars, when combined, create a holistic approach to digital transformation that looks beyond short-term gains. The focus should be on creating a long-term roadmap that acknowledges the complexity of transformation, ensuring the initiative is aligned with the business’s strategic objectives and future growth. It is essential to see digital as “transformational” as it not only about managing technical upgrades but reshaping the entire business ecosystem and ways of working.

Linking digital and business transformation

Despite the overwhelming focus on technology, from cloud to AI, people are the true heroes of successful transformations. For digital initiatives to work, business and technology subject matter experts need to be fully engaged, and open to adapting to new processes and tools. Without their buy-in, even the most advanced technologies or processes will fall short. Effective leadership and a strong change management approach are essential to foster the cultural shifts required for successful digital transformation.

Digital transformation is inherently linked to business transformation because the introduction of new technologies often necessitates changes in strategy, workflows, ways of working, and business models. This is not a one-time effort but an ongoing process that involves constant adaptation. As businesses evolve to remain competitive, digital tools provide the platform for that transformation, enabling greater flexibility, agility, and innovation.

The importance of cultural factors cannot be overstated. Digital transformation initiatives that fail to address the human element—training, collaboration, mindset shifts—often struggle. A culture of continuous learning and openness to change is crucial to realizing the full potential of any digital transformation effort.

Digitalizing the end-to-end product lifecycle

PLM, or whatever it is referred to in the organization, is not just about technology; it is a strategic approach that spans the entire enterprise. From initial concept and design to production, service, delisting, and circular economy, PLM ensures a seamless flow of information and processes throughout a product’s lifecycle. It powers collaboration, automation, and data-driven workflows that are essential to innovation. However, while PLM focuses primarily on managing product data and development processes, it complements other enterprise solutions like ERP, which handles broader financial, procurement, learning and development, and several downstream operational tasks.

Contrary to common misconceptions, PLM and ERP are not in opposition; they are complementary and often integrated. I am on the view that the wider PLM scope spans across ERP and into MRP/MES. Altogether, these disciplines and associated digital solutions enable organizations to manage both product-specific and operational data, creating a holistic view of the business. The scope of PLM has expanded, and many solution providers now refer to their offerings as part of digital transformation rather than PLM specifically. This reflects a broader strategic focus that includes upstream (idea and design phases) and downstream (customer service and product updates) functions, blurring traditional boundaries between departments and roles.

The broader appeal of digital transformation

At the executive level, the term “digital transformation” resonates far more than PLM because it encompasses a broader range of business functions and opportunities, including:

• Broader business impact across customer experience, operations, and innovation—whereas PLM is often seen as limited to product development and management.
• Strategic alignment with long-term business goals like growth, agility, and competitiveness, making it more relatable to board-level discussions focused on overall business strategy.
• Customer-centric focus: While PLM is product-centric, digital transformation places greater emphasis on improving customer experience, which is a top priority for board members looking to drive revenue and loyalty.
• Cross-departmental relevance as digital impacts all parts of the organization, from finance and marketing to supply chain and HR, making it a more comprehensive initiative that engages the entire leadership team; PLM often remains primarily associated with R&D functions.
• Future-proofing the business by adopting emerging technologies like AI, IoT, and data analytics that create new opportunities for innovation.
• Easier to communicate and measure at the executive level—such as improved operational efficiency, cost savings, and better customer satisfaction—compared to the more specialized and technical benefits of PLM.

While PLM is often seen as a specialized (engineering rooted) discipline, digital transformation covers everything from improving customer experience to streamlining operations and driving innovation. In many cases, PLM’s associated technical complexities make it harder for board members to grasp its full value, whereas digital transformation offers a more expansive narrative that aligns with the company’s broader goals. Key questions about PLM ownership at board level remain prominent in many organizations and across industries, from the Chief Technology Officer (CTO) to the Chief Data (or Digital) Officer (CDO), and across with other management board executives.

Vendors and analysts have capitalized on this shift, with all enterprise platform providers positioning their offerings under the banner of digital transformation. This shift often emphasizes transitioning from legacy systems to new technologies like digital threads, digital twins, and cloud platforms. Once again, true transformation goes beyond technology—it comes from the adoption of these tools by the business and the realization of their value in day-to-day operations.

Digital transformation goes beyond the adoption of new tools; it is about enabling and reshaping the entire business. In this journey, PLM plays a critical role, but its contribution is part of a larger, more interconnected strategy that involves every aspect of the enterprise. Ultimately, for businesses to thrive, they need to embrace the full scope of transformation—leveraging technology, people, and processes together to create lasting value.

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As organizations embark on their transformation journey, they face barriers such as technical debt, change fatigue, trade-offs between business and IT imperatives, and various challenges related to costly PLM and ERP implementations. Technical debt from outdated systems and processes can impede progress, while change fatigue can overwhelm teams, making it crucial to manage the pace of transformation effectively. Additionally, the high implementation costs, integration and upgrade complexities of new enterprise platforms can strain resources, suppliers and partners. By addressing these challenges, organizations can set achievable and tangible ambitions aligned with their long-term goals. Focusing on clear objectives and incremental improvements enables companies to modernize operations and foster a culture of continuous improvement, ultimately empowering them to innovate effectively and achieve sustainable growth.

The continuous nature of digital transformation

Digital transformation is an ongoing process—and perhaps even a “state of mind”—that continuously evolves how businesses operate and deliver value. It is not a one-time initiative but an infinite project that requires organizations to be in a state of perpetual readiness for change. This involves reshaping everything from business models to customer interactions. For example, moving from a traditional retail model to an e-commerce platform involves a complete overhaul of operations, including inventory management, logistics, and customer service. This constant state of evolution can be challenging, but it is necessary to stay competitive in a world where customer expectations and technologies are always shifting.

To succeed in this environment, organizations must accept that transformation does not have a single fixed endpoint, considering the following key questions:

• Are we prepared for continuous adaptation? How well-equipped is our organization to respond to ongoing changes in technology, market conditions, and customer needs?
• What does our desired future state look like? Have we defined a clear vision of where we want to be in the next 3-5 years, and how will this transformation help us get there?
• How do we prioritize transformation efforts? Which areas of our business are most critical to transform first to achieve our long-term goals?
• What capabilities do we need to develop or acquire? Do we have the necessary skills, technologies, and processes to support continuous transformation?
• How do we measure the success of our transformation? What metrics and KPIs will help us track progress and ensure that our transformation efforts are delivering the expected value?

Reflecting on these questions can help organizations set a clear direction for their transformation journey and ensure that they are prepared to navigate the complexities and challenges that come with continuous evolution. It is a continuous journey where the ability to adapt to new technologies, changing market conditions, and evolving customer needs is essential. This ongoing evolution requires a mindset shift from seeing transformation as a project with a start and finish to viewing it as an integral part of the business strategy.

Balancing change with stabilization and adoption

While ongoing transformation is crucial for staying competitive and embracing technological advancements, it’s equally important to balance it with periods of stabilization. Continuous change without the opportunity to solidify and adopt new processes can lead to chaos and overwhelm teams. Successful organizations alternate between phases of significant change and stabilization to ensure that new processes are fully integrated, and employees are not stretched too thin.

During periods of change, the focus is on implementing major initiatives such as adopting new technology platforms, restructuring business processes, or launching new products. These are high-impact changes that often disrupt the status quo. However, after these changes are implemented, it is essential to allow time for stabilization. This phase involves integrating the new processes into daily operations, training employees, and refining workflows. By allowing time for adoption, organizations ensure that the changes deliver their intended benefits and do not negatively impact performance.

Finding this balance is key to maintaining momentum without overwhelming the organization. A clear, flexible roadmap that outlines when to drive change and when to stabilize can help manage expectations and ensure that teams are not overburdened. This approach allows organizations to make impactful changes while ensuring these changes are fully adopted and optimized before moving on to the next big initiative.

Integrating continuous improvement with transformation

Continuous improvement is about making small, incremental enhancements to refine and optimize existing processes. It complements digital transformation by focusing on optimizing what is already in place. When integrated into the broader transformation strategy, continuous improvement helps ensure that organizations are not only making big changes but also continuously evolving and improving.

During stabilization phases, continuous improvement can focus on fine-tuning new processes and systems, ensuring they deliver maximum value and efficiency. For example, after implementing a new technology platform, continuous improvement efforts can identify and resolve any issues, streamline workflows, and enhance user training. This helps solidify transformational changes and prepares the organization for the next phase of evolution.

Organizations can also leverage continuous improvement to prepare for future transformation. By encouraging a culture where employees are constantly looking for ways to improve, businesses can identify areas ripe for transformation and make smaller changes that pave the way for larger initiatives. This creates a virtuous cycle where transformation and continuous improvement feed into each other, driving sustained innovation and growth. Successfully navigating the continuous transformation journey requires strategic planning and thoughtful prioritization. Organizations should consider the following key questions to effectively balance change and stabilization while driving long-term success:

• What are our long-term strategic goals? Understanding the end goals helps prioritize transformation initiatives and identify areas where continuous improvement can drive the most value.
• How do we measure the impact of transformation and improvement efforts? Establishing clear metrics and KPIs for both transformation and continuous improvement is essential for tracking progress and aligning efforts with business objectives.
• How will we support our teams during periods of change? Effective change management, including clear communication, training, and ongoing support, is crucial for minimizing resistance and ensuring successful adoption.
• What is the right balance between change and stabilization for our organization? Assessing the organization’s capacity for change helps determine how much transformation can be absorbed at once without overwhelming teams and disrupting operations.
• How do we maintain momentum without overwhelming the organization? Setting realistic timelines and milestones for transformation and improvement initiatives can help keep the organization focused and energized without leading to burnout.

Building resilience and adaptability: embracing continuous transformation

The ability to continuously transform while maintaining operational stability is a key factor in long-term success. Organizations that embrace change as a constant and foster a culture of adaptability are more resilient and better equipped to handle disruptions and seize new opportunities. Preparing for the unexpected means having systems, processes, and a mindset in place that allow for quick adaptation to new challenges.

A culture of adaptability and responsiveness to change enables learning organizations to see transformation as an opportunity rather than a threat. When employees are empowered to contribute to continuous improvement and are supported through periods of change, they are more likely to embrace new ways of working. This resilience is essential for navigating the complexities of the transformation journey and achieving sustainable success.

Digital transformation is not a destination but an ongoing journey that requires constant adaptation and improvement. By balancing periods of change with stabilization and integrating continuous improvement into the transformation strategy, organizations can navigate this journey more effectively. Embracing such cyclic evolution and fostering a culture of adaptability enables businesses to not only manage disruption but also thrive amid continuous change. Organizations that approach digital transformation as a continuous journey—rather than a series of disconnected projects—position themselves for sustained innovation, growth, and long-term success.

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Containerization, particularly through technologies like Kubernetes, Docker, and OpenShift, has become a significant trend across various sectors to streamline application deployment and infrastructure management. Platform editors are progressively adopting containerization to enhance the scalability, flexibility, and reliability of their services, making them more adaptable to modern cloud-based infrastructures. With Gartner predicting that 75% of container instances will be deployed in public cloud environments by 2026, Kubernetes is becoming a standard for container orchestration.

Despite growing interest, the adoption of containers in Product Lifecycle Management (PLM) application development has been relatively slow and not widely publicized. For example, there are only a few posts about containerization on the PTC community and Siemens websites. In April 2024, Outscale, a Dassault Systèmes brand, announced the acquisition of Satelliz, a French company specialized in the development and operation of Kubernetes services. Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) editors, such as SAP and Salesforce, are more openly sharing their container management strategies for enabling cloud transformations.

Kubernetes, Docker and container orchestration

Kubernetes and Docker are at the forefront of containerization, which is revolutionizing how applications are deployed and managed. Kubernetes is a portable, extensible, open-source platform designed to manage containerized applications across clusters of machines. It automates the deployment, scaling, and management of these applications, providing robust solutions for complex workloads.

On the other hand, Docker is a platform for developing, deploying and running containerized applications. Docker defines a container as “a standard unit of software that packages up code and all its dependencies, so the application runs quickly and reliably from one computing environment to another, […] a lightweight, standalone, executable package of software that includes everything needed to run an application: code, runtime, system tools, system libraries and settings.”

Advantages of container technologies include:

• Portability: Containers can run consistently across various environments, from development to production.
• Compatibility: Containers are compatible with different operating systems and cloud environments.
• Scalability: They can be easily scaled up or down depending on demand.
• Consistency: Containers ensure that applications run the same regardless of where they are deployed.
• Efficiency: Containers use system resources more efficiently than traditional virtual machines.

However, there are challenges:

• Orchestration complexity: Managing containers, especially at scale and in multi-cloud or hybrid environments, can be complex.
• Networking complexity: Networking containers across different environments and maintaining security can be challenging.

Gartner highlighted that container management related services, “Associated technologies include service mesh, orchestration and scheduling, service discovery and registration, image registry, routing and networking, service catalog and management user interface, and API.” Anna Belak, Principal Research Analyst at Gartner, noted in 2018 that: “Containerization decouples the application and its dependencies from the underlying infrastructure. As a result, issues caused by differences in operating system distributions and core infrastructure are removed.”

Containers: a growing interest in the PLM landscape

While containerization might seem like a highly technical concept, its practical benefits are clear. Containers are driving significant changes in software development and IT system management, making companies more agile, scalable, and efficient. Some ways containers are making an impact include:

• Microservices architecture: Containers allow different parts of a software application to be developed, deployed, and scaled independently. This not only makes the development process faster but also simplifies the management of complex systems.
• DevOps and CI/CD pipelines: Containers provide consistent environments for software from development to production, reducing the risk of errors and speeding up the release process.
• Hybrid and multi-cloud deployments: Containers enable applications to run smoothly across various cloud environments—public, private, or hybrid—providing flexibility and optimizing costs.
• Modernizing legacy applications: Containers help older applications run in modern environments without needing extensive rework, which extends the life of existing investments and eases the transition to newer platforms.
• Edge computing: Containers are ideal for deploying lightweight applications close to data sources, reducing latency and enhancing real-time data processing.

These examples show that containerization is more than just a technical trend—it is a critical tool for modernizing IT infrastructure.

Integrating containerized solutions can address key challenges faced by PLM systems, such as managing complex product data across global teams and ensuring industry compliance. As organizations adopt cloud-native architectures, containerization may become essential for modernizing PLM platforms to meet Industry 4.0 and digital economy demands. Large OEMs in the Aerospace and Defense industry in the US and Europe are already leading the way in containerizing PLM systems, influencing software editors to make the shift to modern architectures; and this is just the beginning.

Despite the clear advantages of containerization, PLM editors have been slower to adopt these technologies. Traditional PLM systems are often built on legacy infrastructure with complex data models, making the shift to containers challenging. The need for stability, long-term data integrity, and seamless integration with existing modules adds complexity and risk to this transition. High costs and complexity of deploying and maintaining private-cloud applications have also contributed to a cautious approach to adopting modern container orchestration technologies in the PLM landscape.

The integration of containerized solutions can address key challenges faced by PLM systems, such as managing complex product data across global teams and ensuring industry compliance. As organizations adopt cloud-native architectures, containerization may become essential for modernizing PLM platforms to meet Industry 4.0 and digital economy demands. Large OEMs in the Aerospace and Defence industry are already at the forefront of driving containerization in the PLM landscape, and this is just the beginning.

Accelerating cloud and digital transformations

The shift to containerized has been gradual, but developments such as Outscale’s acquisition of Satelliz could accelerate this transition. As containerization and orchestration technologies evolve, they are likely to play a crucial role in modernizing PLM systems and enhancing business agility. Potential benefits of this shift include:

The shift to containerized PLM systems has been gradual, but developments such as Outscale’s acquisition of Satelliz could accelerate this transition. As containerization and orchestration technologies evolve, they are likely to play a crucial role in modernizing PLM systems and enhancing business agility. Potential benefits of this shift include:

• Faster deployment: Containerized PLM systems could significantly reduce the time required to deploy applications, allowing businesses to respond more quickly to market changes.
• Improved scalability: Containers allow PLM modules to scale easily based on demand, helping businesses manage resources more efficiently.
• Enhanced integration: By utilizing containerized infrastructure, PLM systems can better integrate with other enterprise platforms like ERP, CRM, and MES, ensuring smoother operations across different functions.
• Support for digital transformation: Containerized PLM systems are well-suited to support initiatives like Digital Thread and Digital Twin, which require seamless data exchange and real-time collaboration across the entire product lifecycle.
• Increased resilience: Containers isolate failures, ensuring that issues in one part of the system don’t disrupt the entire operation, thus enhancing system reliability.

While containerization in PLM is still emerging, its potential to drive cloud and digital transformations is becoming more evident. As organizations embrace cloud-native architectures, containerized PLM solutions could offer the flexibility and scalability needed to thrive in a competitive, fast-paced market. Outscale’s acquisition of Satelliz represents a significant step toward broader adoption and innovation in the PLM landscape

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Companies pursuing digital transformation often face a tough choice: prioritize rapid innovation or maintain stability? This tension is embodied in the concept of technical debt, which refers to the costs and constraints associated with quick-fix solutions or legacy systems that have outlived their usefulness. While technical debt can sometimes enable short-term gains, its unchecked accumulation can stifle growth, reduce agility and derail digital transformation efforts.

In a previous post, I discussed the fact that technical debt—often misunderstood as a solely technical issue—is actually a broader business challenge linked to gaps in process governance and data ownership, making it a significant barrier to digital transformation. It can either enable innovation or hinder progress if left unmanaged, much like financial debt. Companies frequently rely on makeshift solutions such as spreadsheets to handle complex tasks., Over time, this exacerbates technical debt and obstructs effective digitalization. By adopting a comprehensive end-to-end PLM strategy (beyond only tool or architecture considerations), businesses can address these issues, align technology with strategic goals and maintain a resilient, innovation-supporting technology landscape.

Understanding technical debt in the context of digital transformation

Technical debt is often seen as an unavoidable consequence of the fast-paced nature of digital transformation. Companies frequently prioritize speed over quality, leading to the adoption of temporary solutions that later become long-term liabilities. This issue extends beyond IT departments—while IT sees it as a coding or system problem, business leaders often view it as a barrier to strategic goals. This disconnect can lead to misaligned priorities and ineffective management strategies, including:

• Piling up quick fixes: In the rush to implement new capabilities, temporary workarounds like using spreadsheets for data management or bypassing governance processes become permanent solutions, adding to the technical debt over time.
• Inconsistent data management: Legacy systems often result in fragmented data management, creating data silos that hinder cross-departmental collaboration and prevent a unified business view.
• Increased maintenance costs: As technical debt grows, so do the costs associated with maintaining and updating these patched systems, diverting resources away from innovation.
• Decreased agility: Layering new technologies on top of outdated systems or disconnected capabilities complicates future upgrades and integrations, making the organization less responsive to market changes.

Legacy systems, for example, pose significant challenges. Outdated tools and processes are difficult to modify and lack the flexibility to adapt to new business needs. The cost of maintaining these legacy systems and sub-optimum ways of working consumes a large share of resources, leaving little room for strategic initiatives. Additionally, these systems often create data silos, where crucial information is isolated within departments, making it difficult to achieve a unified view of the business. In a digital-first world, where data-driven innovation and decisions are paramount, this is a serious drawback.

In the rush to digitally transform and leverage new tech such as analytics, IoT, AI, ML, and SaaS enterprise platforms, businesses often implement quick fixes to meet immediate needs. While these solutions offer short-term value or relief, they frequently evolve into long-term problems due to incoherent roadmaps, perhaps doubled with constraining asset capitalization accounting. This leads to a patchwork of tech solutions that are not fully integrated, making future transformations even more complex and costly. As new technologies are layered onto this unstable foundation, the complexity and cost of managing technical debt increase exponentially, creating a vicious cycle that hampers innovation and agility.

• Are short-term gains compromising long-term digital transformation goals?
• How much of your technology landscape is made up of quick fixes that have become permanent?
• Can your current systems handle the integration of new technologies like AI and IoT without extensive rework?
• Is your tech spending more on maintaining legacy systems than on driving strategic innovation?
• How well are your technology investments aligned with your business transformation strategy?

Moreover, technical debt can significantly slow down time-to-market for new products and services, putting companies at a competitive disadvantage. Instead of focusing on developing new capabilities, teams are often stuck maintaining outdated systems. This reduced capacity for innovation can make it difficult for organizations to keep pace with digital natives and adapt to changing market demands. High levels of technical debt also lead to escalating costs, as companies are forced to invest in maintaining, automating and updating legacy systems rather than driving strategic growth.

Strategies for managing technical debt

To navigate technical debt related challenges, organizations need a strategic approach that addresses both immediate and long-term implications. Prioritizing incremental modernization is another key strategy. Instead of trying to eliminate technical debt all at once, companies should identify high-impact areas where technical debt is most disruptive and address these first. Using agile methodologies can help organizations make these improvements iteratively, without disrupting ongoing operations. Emerging technologies like cloud computing and microservices architectures also play a crucial role. They offer more flexible and scalable solutions, reducing the maintenance burden and making it easier to update and integrate systems over time.

Furthermore, fostering a culture of continuous improvement is essential. Technical debt should be made visible and discussed as part of the organization’s strategic priorities. Leadership needs to communicate its importance and incentivize efforts to reduce it. By aligning IT and business teams around shared goals, companies can ensure that technical debt management supports broader digital transformation efforts, incorporating key incentives to enable tech-enabled change:

• Make technical debt visible and part of digital planning: Use metrics and dashboards to highlight technical debt, making it a visible part of business discussions and strategic planning.
• Reward process efficiency and effectiveness: Encourage teams to proactively address technical debt by integrating it into performance goals and rewarding those who contribute to its reduction.
• Foster cross-functional asset lifecycle strategies: Align IT and business teams around shared goals to ensure that technical debt management supports the broader digital transformation strategy.

Technical debt is an inevitable part of any organization’s technology landscape, but it doesn’t have to be a barrier to digital transformation. By understanding its impact, prioritizing its management and embedding it into a strategic roadmap, companies can navigate this double-edged sword effectively. Balancing innovation with stability will enable organizations to leverage their technology investments fully, ensuring that technical debt remains a manageable and calculated investment rather than a roadblock to growth and innovation.

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Rivian and Volkswagen Group (VW) have announced a joint venture to develop next-generation software-defined vehicle (SDV) platforms for their future electric vehicles, with VW investing up to $5 billion, starting with an initial $1 billion. This collaboration aims to leverage Rivian’s advanced electrical architecture and software expertise to create a superior SDV technology platform. The venture will accelerate software development, enhance scalability, and reduce costs for both companies.

Through this partnership, both companies envision launching vehicles equipped with the new technology by the latter half of the decade, and VW will use Rivian’s existing platform in the short term. The joint venture is expected to finalize in the fourth quarter of 2024, pending regulatory approvals.

By pooling their expertise and resources, OEMs can accelerate collaborative return from innovation. Organizations can join forces in multiple ways. For instance, an established OEM can acquire or invest into a niche start-up. This can be achieved through creating a new legal entity like a joint venture (JV), investing into another organization to access given capabilities or capacity, or acquiring a competitor or a supplier to gain access to specific technologies. In this context, PLM strategies play an important role in realizing value from such co-innovation partnerships.

Let’s explore how PLM and ERP facilitate real-time collaboration, design reviews and iterative testing, ensuring swift integration of innovations into production.

How PLM powers collaboration, expansions and acquisitions

Organizations grow through both organic transformations and inorganic expansions. Inorganically, business acquisitions and venture capital investments present growth opportunities through access to new capabilities, markets and technologies. The initial challenges from acquisition are multifold and can be summarized in five key PLM questions:

1. How to leverage or scale one organization’s capabilities to drive value across one another?
2. How to drive synergies across organizations without damaging competitive advantage?
3. How to protect and expand on each organization’s intellectual property (IP)?
4. How to integrate PLM and ERP capabilities across two partner organizations to foster effective collaboration, building on each other’s strengths?
5. How to harmonize and consolidate business capabilities across parent and child entities, or across partnering entities, to remove duplication and address gaps?

Specific answers to these questions will depend on multiple strategic factors, including commercial agreements between companies. It’s also a matter of defining the relevant strategy to enable cross-organizational collaboration, drive business integration, leverage best practices across organizations, consolidate enterprise capabilities and ultimately seek simplification.

The first PLM capabilities to consolidate typically relate to core product and project data: from BOMs, materials, software, xCAD, quality standards, compliance requirements, business processes, supply chain integration—aligning processes and associated systems of record/engagement repository. Initial steps towards building a common PLM backbone often relate to data exchange alignment based on common formats and processes. Other the other side, ERP foundation includes aligning procurement, product costing, compliance, sustainability, financial and other core enterprise requirements.

How PLM fosters portfolio alignment and data protection

Partnering across organizations seeking to co-innovate implies a robust commercial alignment to capitalize on respective investments and related business commitments. Such partnership is often characterized by driving product portfolio synergies, sharing resources and value assets, sharing benefits and return on investments.

Business acquisitions and associated investments translate in five strategic perspectives:

• Market expansion: Acquiring businesses to expand into new markets or geographical areas to gain a competitive edge.
• Product diversification: Investing in acquisitions to diversify the product portfolio—including adding new product lines, enhancing existing products, co-developing new variants or product lines, or integrating complementary products to meet broader customer needs.
• Technology advancement: Acquiring businesses to gain access to new technologies, IP, or technical expertise—staying at the forefront of innovation and maintaining a competitive advantage in the market.
• Operational synergies: Focusing on acquisitions that offer operational efficiencies and cost savings—streamlining processes, achieving economies of scale, reducing redundancies and improving overall operational effectiveness.
• Strategic partnerships and alliances: Forming strategic partnerships or alliances through acquisitions to strengthen the company’s position in the industry—enhancing collaboration, share resources and drive mutual growth.

In PLM terms, this translates to integration of new market requirements and regulatory standards into the product development process. It involves managing a range of products and variations within a PLM ecosystem with broader access control to ensure cohesive lifecycle management. From a technical standpoint, stronger collaboration requires updating and integrating new technologies and knowledge into existing PLM frameworks to support innovation and product enhancement. Furthermore, it entails harmonizing processes and systems across merged entities to streamline operations and reduce PLM-related costs.

How PLM strategies support mergers and acquisitions

PLM strategies play a critical role in supporting business acquisitions by providing a structured framework for integrating and managing the combined entities’ product development processes. PLM value drivers contribute to business mergers and acquisitions in multiple ways:

1. Unified product data management: PLM systems consolidate product data from both acquiring and acquired companies, ensuring consistency and accessibility. This unified approach reduces data silos and enhances collaboration across teams.
2. Streamlined product development: By integrating the product development processes of both organizations, PLM strategies ensure that best practices are shared and adopted, leading to more efficient and innovative product development cycles.
3. Enhanced compliance and quality control: PLM processes help manage compliance with industry standards and regulations by maintaining comprehensive records of materials, processes, and product specifications. This ensures that all products meet quality and regulatory requirements.
4. Efficient change management: PLM strategies facilitate effective change management by providing tools to track and manage changes in product design, development, and production. This helps in quickly addressing any issues that arise during the integration process.
5. Improved resource utilization: PLM processes enable better resource planning and utilization by providing visibility into the capabilities and capacities of both organizations. This leads to optimized use of resources and improved operational efficiency.
6. Accelerated time-to-market: By harmonizing processes and leveraging system synergies, PLM strategies can significantly reduce the time required to bring new products to market. This is crucial for maintaining a competitive edge in fast-paced industries.
7. Innovation and IP protection: PLM systems ensure that intellectual assets from both organizations is protected and leveraged effectively, from products to data assets, processes, resources, etc. First and foremost, this fosters innovation by providing a secure environment for collaboration and knowledge sharing.
8. Scalable and flexible integration: PLM strategies provide scalable and flexible integration frameworks that can adapt to the evolving needs of the business. This ensures that the integration process is smooth and can accommodate future growth and changes.

By implementing robust PLM strategies, organizations can effectively manage the complexities of business acquisitions, driving value creation, innovation, and long-term success. This involves continuous simplification and consolidation, balancing control and flexibility to ensure cohesive operations while maintaining agility to respond to market changes and new opportunities.

Consultants often debate the challenges of PLM and ERP implementations as they are complex by nature, but these changes present opportunities to learn and break the status quo. This is especially crucial for companies serving multiple customers through hybrid PLM ecosystems or those aiming to enable mergers and acquisitions by building modular, “plug-and-play” processes, data flows and systems.

Robust PLM strategies align product development processes, leveraging the combined strengths of acquiring and acquired companies to unlock new value streams, enhance productivity, and reduce time-to-market. They provide a collaborative platform fostering continuous improvement and creativity, while ensuring access to a unified knowledge base and shared IP. Effective data management, compliance adherence, and quality control within PLM frameworks lay a strong foundation for sustainable growth, maintaining high product quality and meeting regulatory requirements.

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Jaguar Land Rover (JLR) was acquired by Tata Motors Ltd. (TMC) in 2008 for about US$1.5 billion (£1.15 billion). Following a two-year separation process from Ford Motor Co. (FMC), initiated its new Product Lifecycle Management (PLM) journey in partnership with Dassault Systèmes. The initial vision was for a full scope transition from a very disparate ecosystem inherited from Ford and previous mergers to an integrated PLM architecture with 3DExperience at its foundation.

Perhaps independently of its PLM strategy, JLR reported strong results in the previous financial year. As of end-March 2024, JLR declared record Q4 and FY2024 revenue of $9.9 billion (£7.9 billion) and $36.6 billion (£29.0 billion) respectively, with profit before tax of $836 million (£661 million) and $2.7 billion (£2.2 billion) for the same periods—the highest since 2015.

Building a credible, holistic PLM strategy and associated implementation roadmap from the ground-up is no easy task. It often translates to multi-year business transformations that must be championed at the board level. For JLR, it has been a 15-year journey in the making. It initiated with a greenfield vision in 2010 which gradually evolved into a hybrid bluefield approach to mitigate transition risks while addressing technical readiness and adoption gaps. A bluefield approach implies combining greenfield and brownfield elements.

Contextualizing it to the JLR story, this post discusses why solving complex PLM challenges requires Agile-based problem solving, adaptive change management and realistic strategic realignment to cope with unpredictability and uncertainty.

Reality check: from greenfield to bluefield

Greenfield PLM implementation strategies often initiate from bold/unconstrained ideas, sometimes combined with unqualified wishful thinking and the need for speed (e.g., unrealistically fast deployment ambitions). When left unvalidated, this approach adds challenges in successfully completing digital transformations—particularly in the context of PLM and other enterprise solutions which carry high-level of ambiguity. Such challenges typically relate to a mix of 5 characteristics:

1. Unrealistic expectations: overestimated benefits and underestimated complexity often fueled by senior leadership’s lack of appreciation and ownership of the PLM scope.
2. Inadequate planning and preparation: amplified by insufficient analysis and the lack of a detailed roadmap due to various technical and business unknowns.
3. Unexpected resistance to change: underestimating organizational ability to embrace change, underestimating communication and training needs.
4. Resource misallocation: budget overruns and inadequate skillsets, assuming existing experts can handle new technologies without external hiring or upskilling.
5. Neglecting delivery risk management: failure to monitor and adjust—wrongly assuming the initial plan will work perfectly without the need for ongoing real-time adjustments based on feedback and evolving circumstances.

JLR’s initial PLM strategy was crafted in 2010; it candidly aimed at a perfect alignment, seeking to connect all enterprise capabilities together following a series of greenfield solution deployments. It later realigned its strategy to refocus on an integrated engineering/development toolset—the product data management backbone of innovation—with BOM, CAD, CAE and virtual twins management at its core.

Legacy coexistence and technical debt

Transitioning from the Ford legacy PLM ecosystem was certainly a significant endeavor for JLR. Similar to Volvo Cars, JLR faced several knowledge gaps in managing the complexity and technical debt it inherited from its previous parent company. With hundreds of tools and customized systems, the integration and data migration landscape required selective dual-track solution development and concurrent support to facilitate the transition. The situation was amplified by different practices and core data sets used across two distinct brands, Jaguar and Land Rover.

Progress was at last reported in 2019, by John Kitchingman, who at the time lead EuroNorth at Dassault Systèmes: “Just over 10 years after the project was initiated, the company has finally rolled out a first solution that covers an entire vehicle program: the Defender model. There is still a way to go, but the good news is that this rollout finally happened.” The hard reality is that it took JLR more than twice the time it initially planned to finally roll out a first solution of 3DExperience that covered an entire vehicle program. The journey with Dassault Systèmes continues as JLR now seeks to complete the deployment of 3DExperience across all vehicle programs worldwide. Only then, JLR would be able to initiate the decommissioning of the remaining components of its redundant Ford-based legacy systems.

There is clearly no single quick fix, silver bullet or magic wand that can address all challenges associated with heavily customized and poorly integrated PLM legacy. Limiting old-new solution coexistence and minimizing customization are typical objectives of every PLM initiative. The last mile of JLR’s PLM transformation will consist of scaling its 3DExperience adoption across the entire product portfolio, finally closing the door on years of functional transition and data migration. Subsequently, this will open the door to further opportunities for enterprise capability improvements.

Leveraging digital twins

JLR clearly aims at end-to-end collaboration around its PLM backbone, reaching cross-functionally beyond design and concurrent engineering supply chains. JLR said that “More than 18,000 users across all JLR business areas and suppliers will make use of virtual twins to increase efficiency, improve production management, save time and reduce waste and costs.”

Laurence Montanari, Vice President, Transportation & Mobility Industry, Dassault Systèmes, remains optimistic about hitting the next significant milestone in this 15-year transformation journey: “JLR is utilizing the 3DExperience platform to enhance its virtual twin experience, creating software-defined vehicles that seamlessly integrate both hardware and software development. […] After five years of partnership, we are opening a new era of collaboration beyond engineering and manufacturing through a trusted partnership, where teams from JLR and Dassault Systèmes work closely in short iterations to address JLR and its ecosystem’s challenges.”

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Paris-based Valeo manufactures mobility technologies, providing transportation electrification, lighting, driving assistance and connected solutions to improve user experience onboard vehicles. The company has been using software solutions from Dassault Systèmes for decades, from Catia for design and CAD engineering, to Envia MatrixOne for its product lifecycle management (PLM) and Delmia for digital manufacturing.

In June 2024, Valeo announced a partnership with Dassault Systèmes to upgrade its legacy PLM systems to the 3DExperience platform. More specifically, “Valeo will rely on Dassault Systèmes’ Global Modular Platform and Smart, Safe & Connected industry solution experiences based on the 3DExperience platform to accelerate the digital transformation of the Group’s research and development activities.” While the benefits of this digital transformation are substantial, it is important to address the potential challenges and risks associated with such a significant technological overhaul.

Upgrading legacy PLM is no small feat

Despite the undeniable advantages of maintaining an evergreen system, the process is riddled with potential pitfalls. A poorly executed major implementation can cripple a company, while even a successful one often spans years and demands substantial resources. This is a critical aspect that frequently escapes the spotlight in discussions about digital transformation.

These upgrades are not only about software, but also about the people involved, including business change, testing and training. Transitions typically involve not just a technical upgrade but are often coupled with a fundamental shift in workflows, processes, data model evolutions and readaptation of configurations and customizations. Legacy systems, deeply entrenched in a company’s operations, come with their own set of complexities and dependencies. Replacing them requires meticulous planning, robust change management strategies and a clear vision to avoid operational disruptions.

Even when migrating to the latest PLM release within a single vendor ecosystem, the process is far from straightforward. Companies might assume that staying within one ecosystem simplifies the transition, but the reality can be more complex. Newer systems often introduce advanced functionalities that necessitate retraining staff, reconfiguring integrations and revisiting data governance policies. This learning curve can be steep, especially for teams accustomed to older systems. Therefore, it is imperative for organizations to approach such migrations with a strategic mindset, ensuring they have the necessary support and expertise to navigate the transition smoothly.

Upgrading from one software version to another typically brings challenges related to legacy data migration, system integration, de-customization, re-customization, change management, user training and cost of ownership. However, the advent of software-as-a-service (SaaS) solutions provides a promising avenue to mitigate these risks. SaaS platforms are designed to minimize these complexities by offering scalable, flexible and continuously updated environments. Key questions remain about the frequency of upgrades, the capacity to realign configurations and integrations and the ability to adapt to change.

The imperative of digitalizing R&D

Despite the challenges, the need to digitalize R&D activities is more pressing than ever when driving product innovation. The benefits of a well-executed digital upgrade can be transformative, outweighing the risks associated with implementation. For Valeo, deploying Dassault Systèmes’ 3DExperience platform is a strategic investment aimed at future-proofing its operations in an increasingly competitive automotive landscape.

As an existing user of Catia, Enovia and Delmia, one might expect a straightforward upgrade process within the Dassault Systèmes portfolio. The press release does not specify if Valeo is considering transitioning to a SaaS version of 3DExperience or a more traditional cloud-managed implementation. Nevertheless, it likely involves a pre-configured industry solution tailored for the automotive sector, compatible with ISO 26262, ASPICE and MBSE development.

By connecting thousands of users across various departments into a cohesive virtual ecosystem, Valeo is not merely upgrading its tools but reimagining its approach to innovation. The PLM platform’s ability to capitalize on legacy data to drive intelligent decision-making is a game-changer. It ensures that every step of the R&D process is informed by the most accurate and up-to-date information, thereby accelerating development cycles, driving margin improvement and reducing costs. Companies like Valeo must stay ahead of the curve by continuously innovating and optimizing their R&D processes. As a Tier 1 supplier to the automotive industry, Valeo likely shares data across multiple PLM systems when interfacing with its OEM customers.

Christophe Périllat, CEO of Valeo, emphasized the significance of this partnership, stating, “At Valeo, we are proud to be the key innovation partner of our clients. Our more than 20,000 engineers develop innovative solutions combining hardware and software and leveraging AI to make tomorrow’s mobility safer and more sustainable. Thanks to our partnership with Dassault Systèmes, our teams will have more efficient solutions enabling digital continuity to support our world-leading R&D activities.”

Navigating the path forward

For Valeo, the partnership with Dassault Systèmes represents a critical step towards establishing itself as a tech-driven leader in the automotive industry. The 3DExperience platform is set to streamline Valeo’s R&D activities, offering a robust foundation for developing next-generation mobility solutions.

Pascal Daloz, CEO of Dassault Systèmes, succinctly captured the essence of this transformation, highlighting, “Creating new mobility usages and universes of experiences requires proven capabilities for styling, electrification and software-defined vehicles. Our 3DExperience platform is this differentiator. It leverages the power of generative AI to connect models from science to data from experience, along the full lifecycle of the vehicle.”

Interestingly, Valeo has also been using Teamcenter through its Powertrain Systems business which integrated Valeo Siemens eAutomotive (VSeA) in 2022. This illustrates the complexity and breadth of Valeo’s digital transformation efforts. It also poses several questions regarding the group’s PLM ecosystem:

• Will it transition to a SaaS platform to leverage future upgradeability?
• How much integration and re-customization are expected and how complex will the transition to 3DExperience be?
• Will Valeo use this opportunity to converge its PLM landscape towards a consolidated platform based on 3DExperience?
• Alternatively, will the group continue to use multiple PDM and PLM systems in the foreseeable future?

These questions underscore the challenges and strategic decisions Valeo faces as it embarks on this significant technological overhaul.

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