Structuring Your Product Data is the Key to PLM Success

Having a well-structured product data system is fundamental to a successful product lifecycle management (PLM) implementation. Without it, companies struggle with information silos and inconsistencies that slow down operations through errors and hindrances to collaboration. Fortunately, there are three key methods that companies can implement to achieve a successful product data structure; these include the establishment of standardized data definitions and a single source of truth, the development of a strong foundation with a core structure, and the adoption of techniques that enhance data accessibility and process optimization.

Through the following methods, companies can make sure their PLM systems function optimally and improve the day-to-day decision-making experience of relevant stakeholders as they work their way through the entire product life cycle. 

Standardization and Centralization: Pillars of Consistency

Single Source of Truth

According to Think with Google’s research, 86% of senior executives see the elimination of organizational silos as “critical to expanding the use of data and analytics in decision-making.” Data silos are common yet detrimental to smooth operations within multinational corporations, and with that in mind, a central PLM system acts as a single source of truth for all stakeholders, irrespective of department, that provides up-to-date information. With accurate data at their fingertips, teams can collaborate and reduce the risk of errors caused by outdated or conflicting data. 

Standardized Data Definitions

Through the establishment of clear and consistent definitions for all data points, from material properties to engineering specifications, within a company's PLM system, stakeholders gain a common understanding of what each represents, which guarantees data cannot be misinterpreted, reducing confusion and improving communication and consistency across silos. 

Building a Strong Foundation: Core Structures for Success

Engineering Specifications as the Backbone

All products require an engineering specification which forms the foundation of a product’s design. In each design iteration, specifications can relax or change, and the specification should be carried along in each product. As the specification is carried along, the corresponding BOM, product design data, and manufacturing package will be carried along with the specification. A well-defined specification shows stakeholders the core design and manufacturing intent for a product. 

Stage-specific Data for Targeted Use

Companies should structure their product data to reflect the different stages of a product’s lifecycle, from design to manufacturing and beyond. Design data, for example, might include 3D CAD models and associated engineering specifications, while manufacturing data might encompass production instructions, work order details, and quality control procedures. The adoption of this targeted approach will help stakeholders access the specific data they need at each stage, reducing wasted time searching for irrelevant information. 

Version Control for Clear Tracking

Tracking data changes throughout the product life cycle through version control allows stakeholders to see the evolution of a design or manufacturing process, identify who made changes, and, in case of revisions, revert to previous iterations. Having the ability to do so is essential in facilitating collaboration across teams and maintaining traceability and regulatory compliance. In the scenario where an engineer comes across an unexpected issue during manufacturing, version control would allow them to trace the issue back to a specific design change, identify the cause, and potentially return to a version of the product that functioned as intended.  

Enhancing Accessibility and Usability: Find What You Need, When You Need It

Metadata: Enriching Data for Discovery

Within parts libraries, component data is enriched with metadata tags that act like keywords for search and categorization features. Through these tags, an intelligent PLM system can easily search and filter through vast inventories of parts, for example using component types, values, and package sizes. Within modern PLM systems, an integration with MRP, supply chain data sources, and IMS applications gives stakeholders complete visibility into component data and where those components are used throughout a product portfolio.

Intuitive Interface for User-friendly Navigation

A user-friendly PLM system interference can enhance teams’ navigation experience. A well-designed interface that allows users to find, in an efficient and simple fashion, the data they need. The implementation of features like intuitive search functionalities, clear data visualization tools, and user-specific dashboards can streamline the data access process and improve overall user experience. When they can search for needed data efficiently, stakeholders are more likely to warm to and adopt new PLM systems, which results in a better collaborative culture and data-driven product development process. 

Advanced Techniques for PLM Optimization

Having covered the fundamentals of structuring product data for success, companies can explore additional methods to optimize their PLM systems and unlock even greater efficiency. 

Digital Twins: A Virtual Representation for Enhanced Insights

Companies could consider the incorporation of digital twins. PLM systems with a digital twin representation of a product allow manufacturing, QC, and teams in the field to close the loop on defect identification, product updates, and change requests. Continuous improvement is now possible throughout the product life cycle as real conditions in the field change. 

Integration with External Systems

The capabilities of a PLM system can be expanded through integration with additional enterprise systems like ERP, MRP, or CRM. Such integrations open the door to a more holistic view of product data that could streamline processes and improve companies’ overall operational efficiency. 

Internally, companies that follow these data structuring methods can unlock improved collaboration across disparate silos and teams, a reduction in errors caused by inconsistencies, and ultimately, faster time-to-market and higher quality products. Through these techniques, companies position themselves to adapt to the changing landscape of PLM, which ensures they remain at the forefront of innovation and competitiveness.  

Moving forward, as PLM continues to influence processes, so too will the available data structuring techniques; companies that embrace these changes will be well-positioned to optimize their product development lifecycles and gain a competitive advantage. As technology continues to advance and market demands evolve, the ability to efficiently manage and leverage product data will be a cornerstone of success. By prioritizing structured product data and embracing emerging PLM optimization techniques, companies can not only thrive today but also tomorrow.