Even though a critical solution for product-centric companies, product lifecycle management (PLM) is often misunderstood. Common myths exist around its scope, cost, ease of use, implementation timelines, and more. By clearing up some of these misconceptions, we can better recognize PLM’s value in managing product data, processes, and decisions from inception to end-of-life.
Working as a PLM consultant, I frequently encounter various myths and misconceptions that organizations have about product lifecycle management solutions. These misconceptions often obstruct adoption and implementation of PLM that could greatly benefit product teams. Let’s explore some of the top PLM myths that need debunking:
PLM is the Same as PDM
A common confusion is assuming PLM (product lifecycle management) is analogous to PDM (product data management). While PDM focuses narrowly on managing CAD files, documents, and other product data, PLM takes a much wider, end-to-end view of a product across its full lifecycle. This stretches from initial ideation, to development, production, sales and marketing, service, and ultimately end-of-life. PDM is an important subset capability and data repository within the larger PLM framework.
PLM is Only for Engineering Teams
Because engineering teams often use PLM tools to collaboratively develop CAD models and manage other technical product data, there’s a misperception that PLM is solely an engineering solution. But modern PLM platforms deliver value across the entire product organization – helping integrate cross-functional teams in areas like project management, supply chain, manufacturing, sales, and service. By breaking down silos, PLM gives the entire business a unified view of product information.
PLM Systems are Difficult to Use
Early generations of complex, rigid PLM platforms reinforced notions that these solutions lacked user-friendliness. However, next-gen PLM tools focus extensively on delivering intuitive, role-based interfaces for all types of users across desktop and mobile experiences. Adaptive solutions allow non-expert users to easily find and share product data while interacting through familiar tools like Excel. Modern PLMs emphasize the user experience.
PLM Requires Lengthy, Expensive Implementation
Overly complex platforms of the past did require lengthy deployments, but lightweight, cloud-native PLMs can now be implemented much quicker. Taking an iterative approach allows companies to start seeing value in months versus years for legacy systems. And subscription pricing structures decrease upfront cost barriers while providing more flexibility. The latest web-based PLMs emphasize faster time-to-value and lower total-cost-of-ownership.
Our Current Systems are Sufficient
Product teams often believe the combination of their existing tools provides enough capabilities that investing in a dedicated PLM platform is unnecessary. But even teams leveraging sets of best-of-breed solutions will inevitably face challenges of fragmented data, manual workflows, cross-functional misalignment, and limited visibility into the full product record generated across disparate systems over time. PLM consolidates this.
PLM is Only Useful for Complex Products
Because aerospace, automotive, and medical device teams were early PLM adopters given their complex, highly regulated products, there’s a lingering perception that PLM is only useful for complicated product manufacturers. However, modern PLMs provide value across a diverse range of verticals including CPG/retail consumer packaged goods, industrial equipment, high tech electronics, and beyond. PLM is highly scalable, catering from the simplest to the most advanced products.
We Already Have Product Processes in Place
Mature companies often have deeply rooted product development processes, reinforced by years of culture and inertia. Leadership can therefore assume that adopting PLM will be too disruptive. However, effective PLM deployments focus on enabling existing processes digitally – not mandating rigid out-of-the-box workflows. PLMs provide the flexibility to map software capabilities to how product teams need to best function. And they add visibility with real-time tracking of how well processes are actually being followed.