PLM (Product Lifecycle Management)

The five phases of product development are concept and design, development, production and launch, service and support, and retirement. These phases all depend on PLM furnishing standardized, reliable, and role-specific data—exactly when it’s needed.

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.

At the senior level, PLM’s impact and value is evident; leaders who see outcomes like shortened change control cycle times don’t need to be sold on the virtues of PLM. But many of PLM’s benefits rest on formalizing processes that originate with engineering teams (such as more sophisticated product definitions). And if the positive impacts of these changes aren’t clearly communicated, engineers will understandably prefer not to add more checkpoints and validation steps or revise familiar workflows. To secure rapid adoption and success, the following tactics are recommended:

• Build executive sponsorship from the outset to secure budget and reinforce to employees that PLM processes are a business priority.
• Cascade strong communication down from the executive level. Middle level management buy-in is key to preventing adoption roadblocks.
• Allocate time and resources for teams to properly adjust, absorb, and understand the value of change.
• Locate the right champion in the business. Rally teams behind a voice who can articulate the end-state to build a culture of contagious enthusiasm.
• Recognize that transformation is a process. Reward success and respond to challenges with support.
• Support teams during the transformation period. Be sure engineers are included in the details of key milestones. Provide resources to build a culture of active collaboration and shared goals.
• Communicate success after deployment. Help individuals recognize how they are contributing to shared company success. Create a sense of ownership and pride in these improved business outcomes.

Effectively managing the product lifecycle involves multiple enterprise systems, including PLM and ERP. The precise boundaries between these will depend on your business model and complexity of your products.

Digital assets (i.e., the intellectual property output of product development) are typically lifecycle managed in PLM. Downstream systems can modify within reason, but these cannot change any form, fit, or function that is defined in the BOM sent from PLM. PLM is known as the source of truth; ERP augments that truth to add components that enhance the data.

Production planning, forecasting, sourcing, cost tracking (physical assets) is typically managed in ERP – transaction-oriented information. ERP is well-positioned to augment PLM data but should never change the definition of source data. ERP is not able to maintain complex configurations. ERP adds "costing" items, subcontract, vendor data, add part alternatives, etc.

Changes are released all together and always must flow back to the PLM system, including data from manufacturing, service, and engineering. This ensures the implications of all part or product changes are visible and well understood.