Although many publications compare product data management and product life cycle management — commonly framing the debate as “PDM versus PLM” — that can create confusion. The functionality referred to as a product data management framework is more accurately a subset of a product life cycle management framework. This distinction can sometimes be blurred by the various marketing efforts of software apps.
The matter may even be further complicated depending on whether the purchasing agent is an engineer or a manager, as there are varying degrees of overlap in PDM and PLM app functionality. An engineer may focus on PDM functionality, while business managers and executives in BI are more likely focused on an enterprise-level solution. An enterprise software selection team should represent all aspects of business to ensure the company makes a decision that will fit the needs of all stakeholders. In particular, buying a standalone PDM framework may leave the enterprise without the broader functionality of a PLM. In this article, we’ll explore the nuances of offerings along the “PDM versus PLM” spectrum.
As we clarify the functional distinction between the two types of frameworks and summarize the rationale for choosing one or the other, an exciting new event in the evolution of product life cycle management emerges: The application of machine-learning-based (ML) analytics is sharpening PLM frameworks. We’ll begin by defining PDM as an integral part of PLM. We will then see how business intelligence within PLM is leveraging AI analytics toward new levels of insight and productivity.
What is product data management, exactly?
Briefly, PDM refers to domain knowledge expert engineering theory and its associated software tools. PDM manages engineering data about a product, particularly computer-aided design (CAD) data, and handles product revisions. Although PDM can be used to handle the design release process, most design release processes are external to PDM framework capabilities. And this defines the most important difference between PDM and PLM: PDM provides one of many inputs to the more comprehensive enterprise PLM process.
An engineering team commonly uses PDM software to collaborate in planning and organizing product data. Engineers can use a PDM framework to handle revisions and rollbacks, manage orders to change design specifications, and create and send a bill of materials. Engineering teams can save substantial time and remove trial and error by collaborating in a central CAD-based PDM. The CAD component includes product prototypes and models based on actual parts’ manufacturing instructions. Refinement and debugging are a natural component of PDM. A modern PDM system interfaces and shares product data with a variety of other software applications, especially enterprise PLM. Here are some of the many important methods and functions integral to PDM frameworks:
- Augmented reality and virtual reality for real-time syncing of design data
- Product CAD file data management
- Product revision control as well as revision and rollback history
- Search functions within CAD files — recycling product specs
- Scalability tools for spec replication
- Output data to integrate with PLM, enterprise resource planning (ERP), and materials requirement planning (MRP)
- Security management to authenticate permission levels for engineers
- Automated workflows such as engineering change orders
A favorite function of PDM frameworks among engineers is the ability to collaborate on designs and share comments and feedback. An equipment manufacturer can improve product development and productivity by sharing product data with suppliers and marketing teams. An engineering team typically leverages a PDM through the following aspects of product development:
- Initial product design
- Product prototyping
- Release to manufacturing
- Engineering change notice (ECN) process
ECNs are also called ECOs (engineering change orders) and are a practical procedure greatly enhanced and optimized by PDM frameworks. The release to manufacturing, mentioned above, streams naturally to many of the functions of the PLM that we will explore shortly. PDM often releases product CAD files to manufacturing. Bill of materials (BOM) data is streamed to the PLM and ERP. This final step unifies the product development in the global enterprise BI model.
Important benefits of PDM
Direct CAD integration is now standard in competitive product development. Computer vision is an important area of AI now enhancing many aspects of CAD in product development. Fast product data searches, product spec replication and recycling, as well as secure data access, can make the difference between a successful product release and a problematic one. Augmented reality 2D or 3D views have now elevated product design to a new level. Automated engineering change orders are likewise an industry standard that PDM frameworks have brought to automated fruition. Many previously disparate functions are now central to PDM, including:
- Revision control
- BOM management
- Spec recycling and reuse
Now that we have defined the engineering assistance provided by PDM frameworks, it will be easy to transition toward understanding the enterprise solution of PLM frameworks.
Full-spectrum product life cycle management
Now that PDM is defined as a subset functionality of PLM, what is a product life cycle management framework exactly? Briefly, PLM manages all aspects of a product from the initial design phase to product termination. Equivalent enterprise-level apps that tend to be unified within PLMs include ERP, customer relationship management, and manufacturing execution systems frameworks.
In the product development scope, PLM ideally integrates the other solutions centrally as a product data sharing hub. PLM is therefore envisioned as a business intelligence strategy with the ultimate objective of maximizing profitability. The PLM framework thus seeks to promote innovation in the context of product introduction, product maintenance, and all the way through to a product’s end-of-life planning.
An ideal PLM framework will track a product through its history, charting designated aspects of production and sales (by way of PDM inputs, service requirements, and spec revisions), all the way to product retirement. Generating vast data stores along the way, the PLM framework is now a primary beneficiary of ML-based data analytics. As such, the PLM framework unifies all business processes and enterprise applications, with the ultimate outcome of uniting people in the achievement of their best possible outcomes. When the PLM is integrated fully, the entire manufacturing supply chain benefits. Run correctly with integrated analytics, PLM optimizes product development, informs manufacturing and production rates for order attainment, and drives accurate marketing campaigns.
Some ways this manifests include:
- Tracking for all project development progress in designed phases with milestones, assessments, and projected outcome assurance
- Assessment of product-related business process performance at a glance
- Interfacing for all enterprise solutions with streaming product data in real time on development issues, hardware issues, and software QA events
- Strategic materials sourcing through supplier relationship management
- Context-driven and role-specific authentication and collaboration to improve data sharing, automated workflows, and BI tools and services toward actionable insights
- Actionable insights leading to concise decisions through ML-based data analytics on product development projects
- Flexible reporting based on portfolio management analytics
- Occupational health awareness and product safety
- Product and process quality assurance
- Improved operational control through monitoring of product revisions, resource constraints, and cost factors
- Interface PDM and engineering processes in product data with other BI processes and the business model on the whole
Comprehensive benefits of PLM
Having defined PLM as an enterprise solution for product development, let’s talk about the broad benefits of such systems: First, PLM creates a foundation for streamlined initial product designs and ECN processes. It also helps reduce development time and costs, speeding up time to market.
Ideally, PLM frameworks tend toward:
- Product designs that are universally visible throughout the organization, promoting collaboration and BI outcomes
- Reduced redundancy; increased design reuse and recycling
- Efficient sourcing and inventory investment, leading to further improvements in manufacturing productivity outcomes
- Documentation updates leading to higher standards of QA
- Inception of BI models that embrace innovative ML models and analytics
The analytics edge in PLM functionality
All processes that generate data can be analyzed to produce valuable insights about how to improve the efficiency of those processes. Both human and machine productivity can be dramatically improved with insights gained from models trained with live data from CAD and production facilities. Translating DevOps processes into the product design context is the natural evolutionary next step in PLM. API integrations for third-party analytics platforms like Sisense now make it easy to harness the forecasting power of AI.
Whatever you’re working on, the right analytics for your PDM or PLM data can help you build better products, services, and experiences that will delight your users and stand the test of time. Backed with insights and the right framework, you’re ready to build boldly and change the world.
Vandita Manyam is product manager for Cloud Data Team at Sisense. She has 5+ years of experience in product management and business analytics, improving the product experience for business intelligence companies like Interana and Tellius. Vandita has spoken for events in Gartner Data Conference and company webinars.