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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PTC is making waves in the Product Lifecycle Management (PLM) landscape, earning multiple recognitions from independent research analysts such as ABI Research and Quadrant Knowledge Solutions. These accolades highlight PTC’s leading role in various PLM-related areas like Mechanical CAD Software-as-a-Service (SaaS), Enterprise PLM for large manufacturing, and Enterprise Augmented Reality (AR).

Here’s an overview of the awards and their implications:

• ABI Research’s 2023 MCAD SaaS: PTC’s Creo was named a leader in the Mechanical-CAD SaaS category for large enterprises and manufacturing at scale (December 2023).
• Quadrant Knowledge Solutions’ 2024 Enterprise AR: PTC’s Vuforia earned the top spot in this assessment (February 2024).
• ABI Research’s 2024 Enterprise PLM: PTC’s Windchill and Arena were recognized as the leading solutions in this category for large manufacturing (April 2024).

Market analysts’ assessments of PLM software providers, such as those recognizing PTC’s comprehensive solutions, play a significant role in shaping technology decisions for many organizations. To understand the credibility of these assessments, let’s explore the evaluation process, the transparency of the criteria, and the potential impact on new adopters’ technology choices.

Understanding Market Analyst Assessments

Market analysts like ABI Research, Gartner, or Forrester use structured methodologies to evaluate PLM providers, providing valuable insights to the industry. These methodologies generally involve a mix of data collection, key criteria assessments and comparative analyses.

Data collection: Analysts gather information from a range of sources, including surveys, customer interviews, case studies, and direct interactions with vendors. This comprehensive approach allows analysts to understand a provider’s capabilities, market presence, and customer satisfaction. It also offers a balanced view of the provider’s strengths and weaknesses.

Key criteria: The evaluation criteria often cover aspects like innovation, implementation, functionality, integration, scalability, and customer support. These criteria are crucial for assessing how well a PLM provider meets the diverse needs of its customers and whether it can keep pace with industry trends. Analysts may also consider the adoption of emerging technologies, such as augmented reality, virtual reality, and generative AI, to gauge a provider’s forward-thinking capabilities.

Comparative analysis: Assessments typically involve comparing PLM providers against each other, identifying strengths and weaknesses, and ranking them based on overall performance. This comparative approach helps identify the market leaders and those with unique value propositions.

More specifically, ABI Research’s PLM assessment refers to leading solution providers in terms of both innovation and implementation, highlighting what is perceived as unique value proposition. Similarly, Quadrant Knowledge Solutions’ SPARK matrix evaluates vendors based on technology excellence and customer impact. Such assessments claim to focus on factors like:

• Deployment offerings, which consider the flexibility of on-premises, hybrid, SaaS scalability.
• Vendor performance and competitive positioning, including market share among industry providers and geographies.
• Integration capabilities with emerging technologies like AR, VR, and generative AI.
• Industry specific, reusable, and customizable templates.
• User experience or ease of use, including modern user interface and technology readiness.
• Industry compliance adherence, keeping up with regulatory standards, sustainability tracking and submissions.

There are however limited detailed information available about these reports, unless perhaps purchased at source from the analysts. In addition, it is important to note that some analysts are more specialized than others in PLM. Some are PLM subject matter experts, whereas others are generalist marketing specialists.

Influence on Technology Decisions

The credibility of these assessments depends on several factors, including the reputation of the analyst firm, the transparency of the evaluation process, and the objectivity of the analysts. Reputable firms generally have established track records, experienced analysts, and clear methodologies, contributing to the reliability of their assessments. However, there can be some concerns:

Transparency: If the specific scoring process and weighting of criteria are not clear, it can be challenging to understand how providers are ranked. Lack of transparency might lead to questions about the assessment’s objectivity.

Objectivity: The relationship between analysts and vendors can affect impartiality. While leading firms typically have strict guidelines to ensure unbiased evaluations, some degree of subjectivity might persist.

Recognitions from respected market analysts can significantly impact technology decisions for potential new adopters. Here’s how these recognitions might affect their choices:

Validation: Recognition from a reputable market analyst serves as a form of validation, indicating that the PLM provider meets certain industry standards and has a proven track record. This validation can give new adopters confidence in choosing a recognized solution.

Benchmarking: These recognitions allow organizations to benchmark their technology choices against industry leaders, helping them assess whether their current or planned solutions are competitive. This benchmarking can guide organizations in making more informed decisions.

Influence on stakeholders: In larger organizations, technology decisions often involve multiple stakeholders. Recognitions from respected analysts can help build consensus among decision-makers, providing an external perspective that supports the selection of a specific PLM provider.

Reduced perceived risk: When considering new technology, companies often want to minimize risk. An analyst’s endorsement can reduce the perceived risk by demonstrating that reputable sources have vetted and approved the provider’s solutions. This can be particularly valuable for companies hesitant to adopt new technologies.

While market analysts’ assessments play a crucial role in shaping technology decisions, it’s essential for organizations to approach these recognitions with a balanced perspective. Assessments can guide decision-making but should be complemented by direct interactions with vendors, customer feedback, and an evaluation of specific organizational needs. By combining these insights with internal analysis, companies can make more informed technology decisions while accounting for their unique requirements.

It’s also important to compare feedback from different sources, including through direct peer-to-peer feedback and return on experience. This balanced approach can help organizations choose PLM solutions that align with their specific goals and challenges, ensuring a more successful implementation and long-term success.

The post Making sense of market analyst assessments appeared first on Engineering.com.

As announced in April 2024, Dassault Systèmes, HD Hyundai Heavy Industries Co., Ltd. and HD Korea Shipbuilding & Offshore Engineering signed a memorandum of understanding (MoU) for HD Hyundai’s Integrated design-production platform. The three organizations aim to “establish an integrated platform for design-production based on virtual twin technology, enhancing production efficiency, and fostering innovation.” With a total workforce of about 14,000 people, HHI operates globally across three business units: shipbuilding & offshore, naval & special ships, and engine & machinery.

“We aim to firmly establish the foundation of a Smart Shipyard by building and operating a digital twin based on 3D and digital assets, rather than past design methods,” said Seungho Jeon, Chief Technology Officer of HD Hyundai Heavy Industries. Taejin Lee, Director of Digital Transformation Innovation at HD Korea Shipbuilding & Offshore Engineering, said the shipbuilding production site, where every vessel is custom produced according to customer orders, is the optimal place where the ultimate goal of the fourth industrial revolution, mass customization, should be realized.

The marine and offshore industry is composed of multiple interrelated sea-related segments, bringing together multiple trades and supply chains. This post elaborates on how product lifecycle management (PLM) and related digital solutions, such as Dassault’s 3DEXPERIENCE, support the drive towards smarter shipyards.

How digitalization enables the future of shipyards

By design, shipyards are heavy industry building sites combining multiple trades and contractors. They are constructed near the open sea or rivers to allow for easy access and sea transportation of supply and final finished good delivery. The shipbuilding, marine and offshore industry is very global and competitive, addressing very niche demands and markets. The effectiveness of a shipyard, particularly in terms of material flow from the stockyard to the building berth, is a critical factor in overall productivity and efficiency. Shipyards are typically tailored to the ship size, ranging from small vessels to large super-tankers, from commercial vessel series to one-off military frigates, each with unique requirements and facilities.

The potential for digitalization and smart automation for maritime companies is significant. The shipbuilding sector remains to-date very document-driven with a lot of information being created and managed across product development and production stages, and shared across suppliers, owners and operators. Digital Twins, or “virtual twins” as Dassault Systèmes like to refer to them, have a great potential to augment master digital data models to combine mechanical/electrical hardware and software with other cyber-physical systems such as propulsion control, heavy duty equipments, or dynamic positioning of the ship.

HHI built one of the world’s largest shipyard in 1974, becoming the world’s top shipyard within a decade of its establishment. (Image: Hyundai Heavy Industries)

A vision for the “future of shipyards” has been elaborated in a 2020 research report published by EcoProdigi as the road to shipyard 4.0, promoting a policy-innovation roadmap for digitalising eco-efficiency shipbuilding operations. The roadmap was composed of 20 elements, from real-time digital planning to welding simulation and real-time monitoring, smart processes and documentation, contractor management, ship hull coating paint optimization, task management improved by augmented and virtual reality, 3D simulations and digital asset-driven inspection, AI-driven warehouse inventory control, additive manufacturing for spare part 3D printing, etc.

Why Digital Twins in the shipbuilding industry

More specifically, the EcoProdigi report discussed why Digital Twins and 3D imaging technologies can drive value when used for virtual inspections, both at the time a ship is delivered, as well as during retrofitting and maintenance: “Many modifications to- and deviations from- a ship’s original design are made during construction. This results in the reality that a delivered ship never has the exact specifications as initially planned. […] In such instances, shipyards are required to re-work the plan and begin producing the parts when the actual ship comes into the yard, resulting in expensive delays. Creating a Digital Twin of the ship as it is being constructed would give shipowners and yards better starting points for planning retrofits and rebuilds.”

From a PLM standpoint, shipyards require a combination of enterprise capabilities to support product development, material handling, inventory management, process coordination and scheduling, component tracking, workflow integration across supply chains and partners, stockyard layout management, path optimization, waste identification and reduction, and cross-functional data analytics. Leveraging smart automations requires a solid data backbone for product visualization, virtual vessel building and operations, democratizing cross-functional data analytics—including leveraging new technologies like Artificial Intelligence (AI).

Digital Twins can help drive green innovation and sustainability, with for instance electric propulsion systems, low emission fuels, autonomous ships, connected fleet management systems, advanced material utilization to enhance product performance and efficiency. Digital Twins and BOM modularity can facilitate organizations to become product-driven rather than document-based, leveraging integrated change management, product platforms and variants, facilitating template-based personalization—from customized vessels to specialized offshore solutions such as wind farms or mining platforms.

What can HHI expect from 3DEXPERIENCE

Over the years, Dassault Systèmes developed a range of industry-specific best practices that helps manufacturers becoming data-driven—leveraging learning across industries. The French software editor supported the NAVIAS project (New, Advanced and Value-added Innovative Ships) which initiated in 2018 and completed in 2022. The European research initiative brought together 18 organizations around the “challenge of developing a platform-based modular product family approach for shipbuilding.” It mobilized shipbuilding manufacturers like Damen Shipyards, universities and research institutes, technology and engineering suppliers, marine and offshore associations took part in the initiative.

The 3DEXPERIENCE platform was used in the co-development of modular solutions to facilitate ship design and manufacture. “The project aimed to improve the design standardization and the design reuse capability focusing on ship energy efficiency to minimize the design impact on the environment.” Among other things, the project built on the Model-Based Systems Engineering (MBSE) methodologies and Digital Twins strategies developed and adopted more commonly in the automotive, aerospace and defence industries.

“HD Hyundai Heavy Industries is driving the establishment of a Smart Shipyard under the vision of Future of Shipyard (FOS) […] Through collaboration utilizing Dassault Systèmes’ excellent integrated 3DEXPERIENCE platform, we hope to expedite the achievement of HD Hyundai Heavy Industries’ goals,” said Seungho Jeon.

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The concept of the Triple Bottom Line approach was coined by John Elkington in 1997 in his book “Cannibals with Forks: The Triple Bottom Line of 21st Century Business.” Elkington, a British entrepreneur and business strategist, introduced the idea to broaden the focus of businesses beyond purely financial metrics to include social and environmental considerations. Since then, the Triple Bottom Line framework has gained significant traction and has become widely recognized as a guiding principle for sustainable business practices.

Makersite commissioned Forrester to assess sustainability’s state and impact in Product Lifecycle Management (PLM) via a survey of 493 product design and sourcing decision-makers from manufacturing firms. The study revealed that decision-makers are at a crucial point in embracing integrated, sustainable practices to meet the needs of a dynamic global market. Key findings emphasize the importance of streamlined access to reliable data for efficient product design and sourcing, while organizations leveraging detailed product lifecycle intelligence gain a competitive edge with faster time-to-market and more profitable products.

Product lifecycle intelligence: the rise of analytics to drive sustainability?

Simply put, Forrester outlined five key areas manufacturers anticipate will significantly affect their products, prioritizing regulatory compliance, growth and sustainability:

1. Evolving customer expectations.
2. Growing emphasis on advancing a circular economy.
3. Heightened demand for transparency in supply chains.
4. Economic constraints.
5. Challenges related to supply chain shortages.

First and foremost, deriving business value hinges on accessing pertinent data promptly to inform decision-making. Forrester highlighted organizations’ focus on key initiatives such as bolstering sustainability reporting for compliance and sales support as well as optimizing reliability and efficiency in material sourcing. Additionally, Forrester identified challenges related to data, including maintaining data in existing systems/libraries and measuring environmental impact.

According to the survey results, key initiatives across various industries to enhance efficiency and effectiveness in product design and sourcing included the following top five priorities:

1. Improving the quality of data informing environmental impact assessments.
2. Enhancing collaboration among stakeholders with a unified product and supply chain data model.
3. Enabling ingestion of material, component, and supplier intelligence from multiple sources.
4. Improving stakeholders’ understanding of sustainability and adopting additional tools to populate existing material libraries.
5. Improving integration between data sources and PLM systems.

Broadly speaking, it’s a matter of building and maintaining an integrated product lifecycle ecosystem with cross-functional analytics that can be trusted, and ultimately linked to PLM processes and associated operational governance to drive effectiveness and efficiency at source.

Organizational maturity: a gap between vision and execution?

Beyond the availability and quality of data, functional data silos and the lack of integration were reported as critical maturity gaps for organizations aiming to drive product lifecycle intelligence to the next level. The least mature organizations are likely to suffer from excessive manual processes that are not fit-for-purpose, coupled with limited or sub-optimal automations, with a focus on efficiency rather than effectiveness.

On the other hand, more advanced organizations tend to prioritize the strengthening of regulatory compliance and sustainability improvement through the “ability to learn how to build high-performance, cost-effective, sustainable products […]” However, it was also reported that “less than half have an automated process in place for tracking a variety of metrics, including scope 1, 2, and 3 GHG emissions, the rate of change of material cost, waste generated and the time to market for product variants or new models.”

Clearly, basic data usage in sustainability initiatives often falls short of manufacturers’ aspirations, creating a gap between vision and execution. Key areas of deficiency include inconsistent monitoring, underutilization of advanced analytics and the need for more granular data.

Beyond the symptoms, what are the root causes?

Forrester pointed out that legacy PLM solutions frequently lack essential material, component, and supplier intelligence, posing challenges for manufacturers in meeting regulatory and customer sustainability and resilience requirements, ultimately impeding product innovation success. Furthermore, this raises several questions: Is it a data, process, or governance gap? Is it a system capability gap, an implementation or adoption gap, or a result of organizational maturity? Are the root causes a combination of these factors?

This aligns with the need for manufacturers to elevate their PLM practices, as emphasized in Forrester’s recommendations:

• First, it is about “modernizing product innovation processes and platforms” to empower designers with product lifecycle intelligence.
• Then, it is about “reclassifying sustainability as a performance initiative” to drive revenue and margin growth.
• And in doing so, “developing a business case for product lifecycle intelligence” to quantify its impact on competitiveness, time to market, and pricing.
• While “ensuring consistent enterprise-level ESG data” to streamline innovation and regulatory reporting processes.

The challenge of driving sustainability strategies forward may ultimately boil down to business change and agility. While high-level recommendations like “Stakeholders across the organization have access to a single source of truth for product design and sourcing” seem straightforward, the reality is more complex:

• Is the lack of PLM maturity primarily attributed to deficiencies in data management, inefficient processes, or inadequate governance structures within organizations?
• Are PLM maturity gaps a result of limitations in system capabilities of legacy PLM solutions, or are they indicative of broader organizational maturity issues such as resistance to change or insufficient investment in training and development?
• To what extent do PLM maturity gaps stem from a combination of factors, including outdated technology, organizational culture, and the complexity of global supply chains?
• How do PLM maturity gaps affect decision-making processes within manufacturing organizations, particularly in terms of product innovation, regulatory compliance, and sustainability initiatives?
• Are PLM maturity gaps reflective of a disconnect between strategic objectives and operational capabilities within organizations, and if so, what steps can be taken to realign these elements effectively?

Embracing business change and establishing supportive governance based on a structured data model across an integrated ecosystem are essential. There clearly is no one-size-fits-all solution. Business change should not be on par with trying to reinvent the wheel, but rather adopt good practice and accordingly adapt the organization’s operating model to maximize value.

This underscores the need for PLM and ERP editors to take the lead in adopting consistent, pre-configured industry-specific proven solutions to tackle the challenge, rather than providing IT toolboxes as a ‘blank canvas’ for building bespoke apps or dashboards with low-code no-code, or other citizen development options. Manufacturers are certainly less interested in building sustainability tracking software and associated system infrastructures—preferring to focus on innovation, commercialization, and portfolio optimization.

The post Maturity impacts manufacturers’ ability to link sustainability and PLM appeared first on Engineering.com.

In April 2024, BMW and Rimac announced a long-term partnership to co-develop high-voltage battery technology for electric vehicles. This strategic alliance signifies a notable shift in the automotive landscape, traditionally dominated by original equipment manufacturers (OEMs) operating in silos. This new collaboration has the potential to reshape how car manufacturers create and produce innovative electric vehicle (EV) components.

In conjunction with this, BMW recently announced the extension of its collaboration with Dassault Systèmes to consolidate its product lifecycle management (PLM) strategy around the 3DEXPERIENCE platform. As Rimac Technology, known for its high-performance supercars, enters this partnership with BMW, it signals a transition toward mass-production standards. Let’s explore how PLM is likely to enable such transformation and examine the broader implications for the industry.

Rimac’s evolution from niche to mass production

Rimac Technology is part of the Rimac Group, a company with a reputation for building high-performance electric supercars and advanced electrification components. The Rimac Group includes the Bugatti-Rimac joint venture established in 2021, demonstrating the company’s capacity to work closely with other luxury automotive brands. Beyond developing its own supercars, Rimac has supplied electrification components to automotive OEMs such as Porsche, Hyundai, Kia, Renault, Jaguar, Aston Martin, SEAT, Koenigsegg, and Automobili Pininfarina.

Nevertheless, the partnership with BMW represents a significant milestone for Rimac, as it seeks to expand its manufacturing capabilities to meet the demands of a mass-production OEM. This collaboration will require the setup of dedicated automated production lines for high-voltage batteries at the Rimac Campus near Zagreb, Croatia. Mate Rimac, Founder and CEO of Rimac, sees the partnership as a full-circle moment, saying “My business journey began with a 1984 BMW 3 Series that I converted into an electric car in my garage at the age of 20, so it’s a perfect piece of symmetry to partner with the BMW Group today.”

This move from niche supplier to tier-one partner underscores the evolving nature of automotive partnerships in the electric mobility era. Rimac’s technical expertise in high-performance electric vehicles provides a solid foundation, though transitioning to mass production is likely to require a different level of operational and technological capabilities.

Aligning systems and data

Collaboration between OEMs and their suppliers can lead to significant innovations; it also comes with challenges, especially in aligning PLM systems and practices. When companies like BMW and Rimac partner to co-develop high-voltage batteries, they must harmonize their PLM ecosystems around a common interface to ensure a smooth exchange of information and efficient project management.

Disparate data formats, proprietary software platforms, and often outdated legacy systems can pose obstacles to seamless collaboration. To overcome these challenges, BMW and Rimac must agree on common data standards, implement secure data-sharing protocols, and integrate their PLM processes—from BOM to CAD, digital manufacturing and other enterprise capabilities. This alignment will allow both companies to maintain a consistent flow of information across the product development and manufacturing lifecycle.

Given Rimac’s expertise in high-performance electric vehicles, the company has the technical knowledge to meet BMW’s high standards for quality and performance. However, transitioning from a niche supercar manufacturer to a supplier for a mass-market OEM requires robust PLM integration for traceability, quality assurance, compliance with industry norms and cost targets. By addressing these PLM challenges early, BMW and Rimac can pave the way for smoother collaboration, ultimately focusing on driving innovation and technology component adoption in the EV market.

Streamlining supply chains is key to success

For the BMW-Rimac partnership to succeed, optimizing the supply chain is crucial. Efficient supply chain operations are key, especially when transitioning from high-performance components to high-volume manufacturing. By embracing open innovation, BMW and Rimac can engage with a broader network of suppliers and stakeholders, enabling collaborative innovation and accelerating the development and certification of new technologies.

However, several challenges can disrupt the supply chain. Legacy enterprise systems and data silos can reduce supply chain visibility and complicate traceability, leading to inefficiencies and increased risks. To address these challenges, BMW and Rimac should focus on digitizing their operations around a common data framework and harnessing real-time insights to drive improvements. This could involve implementing advanced data analytics to track inventory, automate logistics and simplify product costing.

Collaborative platforms that enhance connectivity across the supply chain can further boost efficiency by streamlining communication and coordination. These platforms facilitate seamless data exchange, enabling BMW and Rimac to coordinate their supply chains more effectively. By fostering a culture of open collaboration and adopting technologies that promote seamless data exchange, they can reduce lead times, ensure component reusability and mitigate risks associated with disruptions.

A digitized and interconnected supply chain will not only increase efficiency but also strengthen BMW and Rimac’s ability to respond to market changes and customer demands more rapidly. This approach will ultimately support their shared goal of driving innovation in the electric vehicle market.

Despite the shared vision of the BMW-Rimac partnership, several challenges lie ahead. Cultural disparities, technological integration issues and data security concerns can create hurdles. Promoting cross-cultural communication, fostering openness and ensuring robust cybersecurity measures are critical to overcoming these challenges.

Additionally, regulatory compliance and legal complexities add another layer of difficulty. Staying informed and aligned with industry standards, data privacy regulations and intellectual property laws will help BMW and Rimac maintain a successful partnership based on trust and accountability.

The BMW-Rimac partnership marks a significant milestone in the journey toward electric mobility. By addressing PLM challenges, streamlining supply chain operations and embracing open innovation, these companies are poised to set new industry standards. Although the road ahead has its challenges, the collaborative spirit of BMW and Rimac suggests a promising future where innovation drives the transition to electric mobility.

The partnership between BMW and Rimac is certainly a step toward a new era in automotive collaboration, one where high-end electric drive technology meets mass production. The success of this partnership depends on effective PLM alignment, efficient supply chains, and a shared commitment to overcoming challenges. As the automotive industry moves toward a more electrified future, the BMW-Rimac collaboration serves as a model for how co-development and co-production can drive innovation and reshape the industry landscape.

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The value of connecting product innovation to consumers through engineering, procurement, sales and marketing feedback loops: exploring the handshake between PLM and CRM (Image: Salesforce and Siemens.)

Product lifecycle management (PLM) is about connecting data threads across the business. This includes integrating feedback loops into the product development process with insights related to compliance, quality, financial performance, supply chain delivery, and customer. Customer relationship management (CRM) solutions are designed to help businesses effectively manage interactions and relationships with their customers throughout the acquisition process and product usage journey.

Connecting PLM and CRM to drive value

Combining insights from CRM data with PLM related processes, businesses gain a comprehensive understanding of customer needs, preferences, and behaviors throughout the entire service lifecycle. Product-service alignment ensures that product development is aligned with customer needs and preferences identified using CRM data. In this way, businesses can gain a comprehensive view of their customer’s journey, from initial inquiry to post-sales support.

This enables businesses to develop products that better meet market demand, prioritize changes based on customer feedback and provide a seamless service experience from initial inquiry to after-sales support. It’s a matter of strengthening collaboration: integrated data threads facilitate seamless communication and interactions between sales, marketing, product development and customer service teams. Connected data ensures that everyone is working with the same up-to-date information, fostering alignment and efficiency across departments.

With data-driven decision-making at its core, the integration of PLM and CRM systems empowers businesses to drive innovation, increase customer satisfaction and optimize sales and operations for long-term success. By combining CRM and PLM data, businesses can gain valuable insights into customer behavior, market trends, and product performance. Such connectivity enables data-driven decision-making across departments, leading to more informed data strategies and better business outcomes—addressing business needs through cross-functional personas and complementary lenses across the enterprise, such as:

• Engineering: How is the product performing within the wider portfolio?
• Manufacturing: What product improvements can be implemented into iterative innovation, manufacturing and service cycles?
• Procurement: How to optimize manufacturing and supply chain costs?
• Finance: How are product development efforts amortized?
•  Sales: How to generate new revenue streams from product and service data?
• Services: How does the product perform in the field and how much maintenance is required?

Adopting a “design for service” approach

By leveraging data insights and fostering cross-functional collaboration, businesses can optimize sales and operations, increase customer satisfaction and achieve long-term success in a number of ways.

For engineering teams, PLM-CRM integration can provide valuable insights into how products perform within the wider portfolio, enabling them to make informed decisions about product development and innovation.

Manufacturing teams can leverage CRM and PLM data to implement product improvements into iterative innovation, manufacturing and service cycles, enhancing overall efficiency and quality.

Procurement teams benefit from PLM-CRM integration by optimizing manufacturing and supply chain costs through data-driven decision-making.

Finance departments can amortize product development efforts more effectively, while sales teams can identify new revenue streams from product and service data, driving business growth.

For service teams, PLM-CRM integration enables better understanding of how products perform in the field and how much maintenance is required, leading to improved customer satisfaction and loyalty.

Kerri Doyle, Senior Marketing Manager, Siemens Digital Industries Software, discussed in a Siemens blog post the importance of adopting a “design for service” approach to embed customer-centric feedback loops into product maintenance and sustainability requirements.

He also highlighted how design for service practices contribute to wider operational efficiency.

“PLM software manages information around the customer’s product, including design, engineering, and manufacturing data, how the product should be serviced and when, service requirements and service planning data with the right resources and skillsets, service documentation with rich 3D visuals and animations, service history, etc.,” Doyle said.

Leveraging customer-centric servitization

Interestingly, Siemens defines servitisation as “an outcome-based relationship based on service offerings that fit with your unique challenge and ultimately have a financial benefit.” The approach becomes even more relevant in asset-intensive industries focusing on increasing profitability and creating new revenue streams.

Customer-centric servitization revolves around placing the customer at the heart of business operations and offerings. It entails understanding the intricacies of customer needs, preferences and challenges through comprehensive data analysis and feedback mechanisms. By going beyond mere product sales, businesses embrace a holistic approach, offering a spectrum of services that complement and enhance the value of their products. These services span from installation and maintenance to personalized training and ongoing support, ensuring that every aspect of the customer experience is optimized for satisfaction and success.

In this model, personalization and customization play a pivotal role, as businesses tailor services to meet the unique requirements of individual customers. Through continuous engagement across the entire customer lifecycle, from initial consultation to post-sales support, companies foster long-term relationships and loyalty. Moreover, customer-centric servitization emphasizes value co-creation, involving customers in the design and refinement of products and services. This collaborative approach ensures that offerings evolve in tandem with changing customer needs, ultimately driving mutual success and satisfaction.

Central to customer-centric servitization is the commitment to measuring and enhancing customer satisfaction and impact. By consistently gauging customer feedback and evaluating the effectiveness of services, businesses refine their offerings to deliver maximum value and relevance. This iterative process not only strengthens customer relationships but also positions companies as trusted partners dedicated to meeting and exceeding customer expectations. Overall, customer-centric servitization represents a shift towards more customer-focused business models, where the customer journey is meticulously crafted to deliver growth through new revenue lines and differentiation in the market.

Beyond the recent Siemens and Salesforce collaboration announcement, other enterprise solutions—such as Propel Software which is directly built on the Salesforce platform—are also promoting value from connecting PLM and CRM.

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