Navigating the Path: Opportunities and Obstacles for PLM in Engineering Digital Transformation

Navigating the Path: Opportunities and Obstacles for PLM in Engineering Digital Transformation

Digital transformation is changing the world. It comes to our everyday lives and businesses. In my earlier articles I touched multiple aspects of how digital transformation strategy can change manufacturing businesses. When it comes to engineering and manufacturing systems (for me they are both under the umbrella of PLM), I can see one of the most fundamental change is transformation of a single source of truth concept. We are moving from systems and applications to data. The old concept in managing SSOT is hard achieve, therefore, I can see how data-driven strategies can help manufacturing companies to solve the problem of complexity and to support the digital evolution and digital transformation initiatives from siloed data to new interconnected holistic product models. For the last decade, CAD and PLM industry also learned a lot from developing of the first generation of cloud based systems.

In my article PLM Intelligence and how to explain digital transformation, I outlined main elements of the future PLM platform software.

  1. Multi-Tenant Network Data Model
  2. Digital Thread and Process Orchestration
  3. Online Service Discovery (design, manufacturing, supply chain, etc.)
  4. Device Connectivity
  5. New “business” model for services and data access

Prof Dr. Martin Eigner published an interesting post in his LinkedIn account discussing opportunities and obstacles in digital transformation strategy of engineering systems. Here is an interesting passage:

Product Lifecycle Management (PLM) is a strategic concept that lays the foundation for the digitalization of engineering. It addresses organization, processes, methods, infrastructure, and tools for managing a product over its entire lifecycle. With the constant expansion of the functional scope of PLM, AI, and new software technologies, there is high implementation potential with opportunities for product and process optimization.

However, there are obstacles to achieving the objective of implementing an interdisciplinary engineering backbone along the entire lifecycle. Providers promise too much, and users tend to select “best in function” instead of “best in integration” and customize their system in detail like a German midrange car- The constant conflict between PLM and ERP for sovereignty over the overall process further complicates matters.

In today’s rapidly evolving technological landscape, engineering industries are experiencing a profound digital transformation. Product Lifecycle Management (PLM) systems play a pivotal role in this paradigm shift, enabling organizations to streamline their processes, enhance collaboration, and optimize product development from conception to disposal. However, with opportunities come challenges. Let’s explore five opportunities and five obstacles for PLM in engineering digital transformation.

Digital Transformation Opportunities:

Here is the list of five opportunities I can see for digital transformation efforts in engineering and manufacturing systems (aka PLM):

  1. Product Knowledge Graph Model: In the era of digital transformation, the importance of knowledge graphs for Product Lifecycle Management (PLM) cannot be overstated. By providing a structured way to organize and analyze complex data, knowledge graphs can help to improve mechanism to model complex systems and understand product data semantics. Legacy PLM platforms are limited in their semantic capabilities, and query scale as well as the ability to scale organizationally across multiple companies and supply chains. Demand from engineering disciplines and computer engineering to improve a manufacturing process will trigger interest in AI and will drive even higher interest in new capabilities of knowledge graph and graph databases.
  2. Online Services and Data Openness : Digital transformation demands moving from documents to data, introducing data granularity and abilities to connect systems together. Moving from monolithic legacy PLM architectures to connected and integrated PLM platforms is an opportunity to include data from multiple systems together to facilitate transparency and creation of digital threads.
  3. End-to-End Integration: PLM systems offer the opportunity to integrate various stages of the product lifecycle seamlessly. From initial design and engineering to manufacturing, distribution, and even post-sales support, PLM can provide a mechanism to connect all processes. This integration enhances efficiency, reduces errors, and accelerates time-to-market.
  4. Data-Driven Decision Making: With the proliferation of online data services and IoT enabled devices, PLM systems can harness vast amounts of data generated throughout the product lifecycle. Connecting engineering with services and leveraging advanced analytics and machine learning algorithms, organizations can derive actionable insights from this data. These insights enable informed decision-making, driving innovation and competitive advantage.
  5. Agile Product Development and Collaborative Innovation: PLM facilitates collaboration among cross-functional teams, including designers, engineers, suppliers, and customers. Multi-tenant cloud-based PLM platforms offer real-time access to product and project data, enabling geographically dispersed teams to collaborate seamlessly. Traditional product development processes often suffer from rigidity and lengthy development cycles. Online services enable organizations to adopt agile methodologies, allowing for iterative development, rapid prototyping, and frequent feedback loops.

Digital Transformation Obstacles:

Below you can see obstacles that can slow down digital transformation initiatives in business processes an business models related to product lifecycle management (PLM).

  1. Legacy Systems Integration: Manufacturing teams are struggling with existing systems and how to introduce digital technology to current processes. Integration of modern SaaS platforms and legacy systems is a challenge for many organizations. Legacy monolithic systems may use outdated technologies or lack interoperability standards, posing compatibility challenges. Migrating data from legacy systems to PLM platforms without disrupting ongoing operations requires meticulous planning and execution.
  2. Change Management: Changes are hard. Even if your target is better than current status quo, implementing digital technologies involves significant organizational change, impacting workflows, roles, and responsibilities. Resistance to change among employees, coupled with inadequate training and communication, can slow down PLM adoption. Effective change management strategies, including stakeholder engagement and cultural alignment, are essential to overcoming resistance and fostering adoption.
  3. Data Security Concerns: Modern SaaS and cloud based technologies as well as introduction of digital transparency and openness can raise concerns about data security and intellectual property protection. As organizations digitize sensitive product information and collaborate with external partners in supply chain management, they become vulnerable to cyber threats and data breaches. Implementing robust security measures, such as encryption, access controls, and threat monitoring, is crucial to safeguarding confidential information.
  4. Interoperability Challenges: PLM systems often need to interface with multiple other enterprise systems, such as ERP, CRM, and MES. Ensuring seamless interoperability between these systems requires new openness, open APIs and data communication. Interoperability challenges can slow down and disrupt data exchange and synchronization, leading to inconsistencies and inefficiencies across the organization.
  5. Cost and ROI Considerations: Manufacturing businesses invested large amount of resources into implementation of existing PLM platforms. To replace legacy technology can take time and investment. While digital transformation in PLM offers numerous benefits, it also trigger upfront costs and ongoing investments. The challenge in new business models is to calculate tottal cost of ownership in enabling innovation and to provide business leaders a sustainable model and business value. Calculating the return on investment (ROI) of PLM initiatives requires quantifying both tangible benefits, such as cost savings and revenue growth, and intangible benefits, such as improved collaboration and innovation.

What is my conclusion?

Digital transformation brings opportunities to PLM software. The biggest opportunity is to capitalize on data openness, integration, collaboration and future benefits of AI. The core element of this opportunity is modern data management architecture allowing to connect multiple systems together, to capture data semantics and enable building of digital thread. One of the critical elements to capture this opportunity is to establish a business model to support system openness. The biggest obstacles of digital transformation are related to organizational change management, lack of APIs and complexity of integrations.

By capitalizing on opportunities such as end-to-end integration, data-driven decision-making, and collaborative innovation, organizations can unlock new levels of efficiency, agility, and sustainability. Obstacles such as legacy systems integration, change management, and data security concerns requires proactive strategies and diligent execution.

Ultimately, successful PLM implementation for digital transformation can only rely on on a holistic approach that addresses technological, organizational, and cultural dimensions of digital transformation.

Just my thoughts…

Best, Oleg

Disclaimer: I’m co-founder and CEO of OpenBOM developing a digital-thread platform with cloud-native PDM & PLM capabilities to manage product data lifecycle and connect manufacturers, construction companies, and their supply chain networks. My opinion can be unintentionally biased.


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