PLM

Product lifecycles vary significantly, ranging from short (e.g., mobile devices or laptops) to long (e.g., aircraft or naval vessels). Regardless of their total lifespan, all products progress through the same four stages: product development, growth, maturity, and decline. At each stage, PLM can offer benefits that support the overall lifecycle.

The rapid pace of change and complexity in your business can result in siloed teams looking skeptically at product data and processes. That lack of trust amplifies complexity and confusion—which can seriously undercut efforts to be innovative and agile in the marketplace.

Traditionally, distinctions like role, department, geographic location, software, or hardware platforms are reasons why data can’t be shared. But the digital thread makes these distinctions arbitrary—removing barriers and creating opportunities to share and use data in new ways. And all data is treated with accessibility, traceability, and credibility. The result is that you not only empower teams across your organization with more data and insights, but you’ve also increased their trust that the data is accurate and actionable.

PLM provides the foundation needed to deploy your digital thread strategy. PLM maintains the data integrity and builds the confidence you need to keep pace in fast-changing, competitive markets. And PLM integrates with complementary technology (e.g., industrial connectivity, IoT, AI/machine learning) to establish a universal data flow between IT/OT systems—needed to implement the digital thread at full enterprise scale.

A digital twin is a virtual representation of a physical product, process, person, or place that can understand, measure, and even manage its physical counterparts.

PLM brings together the three components of a digital twin: a digital definition of its counterpart, operational/experiential data of that counterpart (gathered from Internet of Things data, real-world telemetry, and beyond), and an information model (dashboards, HMIs, and more) that correlates and presents the data to drive decision making.

A digital twin is much more than a simulation, which is merely a data-driven prediction for how a physical entity will behave. A digital twin spans the full product lifecycle, reflects evolving data, and serves engineering, manufacturing, and service use cases.

Managing the product lifecycle effectively requires managing each stage’s data, process, and team needs. It begins with creating a central hub for digital product definition and maintaining comprehensive records of engineering specifications, service procedures, and manufacturing process definitions for each version of every component. This ensures control and understanding of exact product configurations, as well as traceability and governance over all changes throughout a dynamic, evolving product lifecycle.

PLM systems are designed to standardize, automate, and scale this management process, making it data-driven and applicable even in complex environments. Managing the product lifecycle involves different requirements and provides unique benefits to each team. Collectively, this results in significant improvements in efficiency, quality, service, and the overall product lifecycle.

A supply chain leader benefitting from a properly managed product lifecycle will realize resource savings by tapping into the universal data flow between PLM and ERP – eliminating the pitfalls of manual data entry. An engineering lead will experience efficiency gains across their entire organization – enabling them to ramp up capacity and fund more projects. And because product designers are collaborating with advanced planners early in the cycle, they can work together to beat cost targets. Manufacturing teams can move into active production faster with accurate, complete, and up-to-date data. Service leaders are also better prepared – having benefitted from participation in NPI and change processes.