Over the past two decades, product lifecycle management (PLM) has become increasingly important to ensure not only the effectiveness of a company’s supply chain management but also the ultimate success of products in the marketplace. In the year ahead, intelligent, cloud-based PLM digital solutions—incorporating the use of technologies like artificial intelligence, machine learning, and the Internet of Things (IOT)—are also expected to play a crucial role in fostering product optimization and innovation, especially in the face of continued economic challenges.
PLM Digital Solutions
Used by manufacturers to manage the development of complex products, such as medical devices, PLM traditionally may refer to both the strategic process of managing a product’s journey from conception through the end-of-life (EOL) stage and the software used to manage the data and processes for each lifecycle stage across the supply chain. Today, PLM strategy and software are inextricably entwined, and there is greater urgency than ever for companies to adopt effective software solutions to gain dependable, 24/7 access to the complete range of data associated with a product—at all lifecycle stages.
According to data gathered by PTC, “the majority (63%) of manufacturing organizations believe that, without upgrading or reinvestment, their enterprise software systems will remain competitive for roughly two to three years. This means that any organization that upgraded in 2020 before, or at the start of, the pandemic, will likely be seeking to reinvest in its software solutions, [including] around product lifecycle management.”
As companies increasingly upgrade to cloud-based software-as-a-service (SaaS) platforms, they can ensure that a product’s sustainability and cybersecurity needs are addressed and incorporated into every product lifecycle stage, from ideation to EOL.
For example, PLM software tools can help ensure that products are “sustainable by design,” according to PTC, “using no more material than is physically necessary,” and the use of artificial intelligence can ensure that only the essential materials are being used to create the strongest design. Such technology tools can also help ensure that components are designed to facilitate their disassembly, reuse, and recovery at EOL.
In today’s environment, design- and engineering-focused product teams also require strategic insights for managing risks in their supply chain, particularly with an eye toward components or parts scheduled for EOL, according to manufacturing services company Jabil, which noted that “predictive analysis helps to identify the technology changes that are on the horizon so that necessary modular redesigns get traction early enough to help the OEM maintain leadership and competitiveness in their market.”
PLM Use Cases
Heading into 2023, executives are recognizing that PLM is perhaps more important than ever, as companies continue to face economic uncertainty due to war, inflation, lingering challenges from the receding COVID-19 pandemic, and ongoing supply chain issues.
A recent article by engineering.com notes that the use of intelligent PLM software and processes helps companies achieve and access data standardization and consolidation, cross-business integration, improved user experience, and cross-platform analytics—all necessary to drive innovation and sustainable business resilience.
According to experts like Oracle, PLM software and IOT are being used for a variety of use cases, which include:
- Seamlessly integrating data and processes in supply chain systems to develop a holistic product development strategy
- Driving faster innovation to launch by more efficiently designing, developing, and managing new product introductions and engineering change requirements
- Enforcing product compliance and tracking changes throughout a product’s lifecycle to adjust to changing global standards
Additionally, both Oracle and engineering.com note that PLM will play an important role in supporting the use of digital twins, or the digital representation of products, for tasks such as running virtual what-if scenarios, predicting the cost or benefit of product modifications, and ensuring optimal product performance.
“Going forward, virtual twins are becoming more realistic, more accurate, more predictive, more dynamic, and more timely representations of the real,” said engineering.com, noting that effective digital twin management implies robust lifecycle management.
This approach to using technology to monitor product lifecycles, especially with an eye toward EOL, is at the heart of what Dynamic Technology Solutions does when it evaluates and recommends technology solutions to maximize the lifespan of components used in mission-critical systems. To find out how this could work for your products, watch Dynamic’s Product Lifecycle Management case study video and download its Technology Asset Lifecycle Management Solutions fact sheet (PDF).