As we enter 2024, the world of product development and manufacturing continues to be shaped by a rapidly evolving technological landscape. Product Lifecycle Management (PLM) has been at the forefront of this transformation, enabling companies to improve their product development processes and increase efficiency. As we look ahead to the next few years, emerging technologies like artificial intelligence, visual collaboration tools that provide data in context, and the continuing adoption of cloud-PLM and the Digital Thread will proliferate. These will continue to revolutionize the way we approach PLM. In this blog, we'll take a closer look at some of the most significant technology trends shaping the future of PLM.
Prediction #1: Products will need passports
The EU’s new Digital Product Passport will provide information about products’ environmental sustainability. This information will be easily accessible by scanning a data carrier, including attributes such as durability and reparability, recycled content, or the availability of spare parts of a product. It should help consumers and businesses make informed choices when purchasing products, facilitate repairs and recycling, and improve transparency about products’ environmental lifecycle impacts. The product passport should also help public authorities to better perform checks and controls.
Prediction #2: Digital Thread vision becomes Threads reality
Digital Threads will continue to proliferate but primarily as multiple partial threads initiated and owned by distinct parts of the organization because the crucial role of a Digital Thread is the ability to generate a Digital Twin for a particular, narrow purpose. That Digital Twin may be a 3D assembly, but it may also be a dynamic status or an interactive dashboard. For example, these non-3D Digital Twins monitor or understand product delivery risk in a specific market. They will be created by aggregating all product elements and contributing factors impacting them, such as geopolitical developments, weather conditions, cataclysmic events (e.g., volcano eruptions), etc. This approach will challenge the current thinking around a single all-encompassing Digital Thread capable of defining any Digital Twin, in any context, for any purpose.
Prediction #3: Product and process complexity pushes PLM out of data centers
In 2024, companies will continue to drive their Digital Thread strategy, pushing PLM systems to implement complex functionality using the wealth of newly available, connected data. AI, Digital Twins, immersive technologies (AR and VR), and other new strategies will increase global collaboration, system integrations, and security requirements, creating significant challenges for operational personnel to adapt to system environments quickly.
As the complexity of just about every area of the product development process increases, so does the size and complexity of managing the environment in which the system operates. PLM calls for high volumes of restricted data, often residing across multiple applications for complex processes serving users worldwide. Altering architecture to support the complicated and growing environment requires highly skilled resources with deep knowledge of the system infrastructure and how it best works with the specific PLM solution to support the demanding requirements. With these resources' high cost and limited availability, companies will continue to offload these IT functions to PLM solution specialists typically found through SaaS deployments.
Prediction #4: AI will play a leading role in regulatory compliance
Identifying the right set of regulatory compliance requirements when designing software-defined products will be done with the reliance on AI and LLMs built from all requirements sources anywhere throughout the world. PLM platforms will manage the process and the results as part of the product’s Digital Thread.
As the product functionality becomes more software-driven, there is a mounting problem of properly identifying the right regulatory compliance requirements from various sources, including stacks of documents (e.g., PDFs) and various data repositories (e.g., DOORS or Jama). This differs from design-specific requirements from the stakeholders, system models, and general design space exploration. Without the ability to identify the exact set of product-related and country-specific Regulatory Compliance requirements, manufacturers increase the risk of either overdesign (too many requirements selected), failure to get approved (missed requirements), or life-threatening accidents. Since the sources of these requirements are so vast and the requirements themselves keep changing all the time, it is critical for the manufacturers to show that they worked with the right set of requirements during their compliance verification and audits.
Prediction #5: PLM platforms will be increasingly embraced as a collaboration platform of choice
The ever-increasing system and functional complexities of products necessitate enterprise-wide collaboration. The collaboration must span engineering domains (system, simulation, mechanical, electronic, electrical, and software) and teams like documentation, compliance, manufacturing, suppliers, maintenance, and others. This is needed to understand complexities, optimize implementation details, reduce risks, increase quality, reduce cost and time, and more. PLM platforms will become critical for collaboration by enabling management of the design intent (the Why), all data that represents the product (the What beyond physical BOM), and all processes used to define, design, test, manufacture, and maintain the product (the How). Since collaboration happens at all stages of the product lifecycle, the information must address the initial informal approach (no need to track everything) to a much more formal one (where everything needs to be tracked).
Prediction #6: AI’s potential to deliver value to PLM and digital engineering will depend on the Digital Thread
There is no doubt that artificial intelligence (AI) holds transformative potential for PLM and digital engineering. But AI runs on data. And if engineering data remains siloed, as it is within many organizations, even the most powerful AI tools will yield limited value. The key to unlocking this potential is to create an engineering and product data infrastructure that establishes context and traceability, for both structured and unstructured data. The digital thread concept refers to an approach to managing data relationships that connects disparate systems and workflows into a cohesive digital ecosystem. With its ability to process and analyze vast amounts of data, AI will integrate seamlessly into the digital thread to support decision-making across sectors. For instance, in manufacturing, AI could predict maintenance needs, optimize supply chains, and even drive product innovation by analyzing customer feedback and market trends. In healthcare, AI-integrated digital threads could lead to more personalized patient care and faster medical breakthroughs by aggregating and interpreting complex medical data. Check out this recent discussion on the topic with Fraunhofer and AI Marketplace.
Prediction #7: Data governance requirements will evolve faster than ever
The proliferation of AI and analytics has created an insatiable demand for data. Regulatory bodies have reacted by introducing new requirements for compliance. Meanwhile, the world noticed that the outputs of generative AI are not always accurate. The implications are clear: companies must understand the sources of their data, have a responsibility to ensure its quality, and must control how it is used downstream. Traceability will be more important than ever - new business insights are useless if derived from inaccurate or noncompliant data.
Thanks to the resident Aras experts who contributed to this blog: Bruce Bookbinder, Pawel Chądzyński, Josh Epstein, Rob McAveney, and Patrick Willemsen.
Have your own predictions you’d like to share? Let us know in the comments below or via LinkedIn.