How can you manage product data more effectively in PLM?

Product data is the lifeblood of any product engineering project. It includes all the information about the design, specifications, requirements, testing, validation, and documentation of a product. Managing product data effectively is crucial for ensuring quality, compliance, and efficiency throughout the product lifecycle. However, product data can also be complex, fragmented, and dynamic, making it challenging to keep track of and share among different stakeholders. That's why product lifecycle management (PLM) systems are essential tools for product engineers who want to streamline and optimize their product data management processes. In this article, we'll explore some of the best practices and tips for managing product data more effectively in PLM.

1 What is PLM and why is it important?

PLM is a software platform that helps product engineers manage the entire lifecycle of a product, from conception to retirement. PLM enables product engineers to store, organize, access, and update product data in a centralized and secure location. PLM also facilitates collaboration and communication among different teams and departments involved in the product development process, such as design, engineering, manufacturing, quality, and marketing. PLM can help product engineers improve product quality, reduce costs, accelerate time-to-market, comply with regulations, and enhance customer satisfaction.

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Prakash Chandar

Founder and CEO at Hontrel Technologies | Leading the AI-Driven Future of Engineering | Engineering Services
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Effectively managing product data is at the core of Product Lifecycle Management and from my experience, it's a pivotal aspect of ensuring seamless collaboration and optimized product development. I've witnessed instances where having a centralized repository for product data eliminated version control issues, reducing errors and rework significantly.

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2 How to structure your product data in PLM?

One of the key aspects of managing product data effectively in PLM is to structure it in a logical and consistent way. This means defining and following a standard naming convention, classification scheme, and hierarchy for your product data. This will help you avoid confusion, duplication, and errors when creating, modifying, and searching for product data in PLM. You should also use metadata, attributes, and tags to enrich your product data with relevant and useful information that can help you filter, sort, and analyze it. Additionally, you should establish clear and coherent relationships and dependencies among your product data elements, such as parts, assemblies, drawings, documents, and bills of materials (BOMs).

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3 How to control your product data in PLM?

Another important aspect of managing product data effectively in PLM is to control it in a secure and traceable way. This means implementing and enforcing a robust access control policy, version control system, and change management process for your product data in PLM. Access control policy defines who can view, edit, approve, and delete product data in PLM, based on their roles and responsibilities. Version control system tracks and records the history and revisions of product data in PLM, allowing you to compare, restore, and audit different versions. Change management process regulates and documents the changes made to product data in PLM, ensuring that they are justified, authorized, verified, and communicated.

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4 How to integrate your product data in PLM?

A third important aspect of managing product data effectively in PLM is to integrate it with other systems and sources of information that are relevant for your product engineering project. This means connecting and synchronizing your product data in PLM with your other software tools and platforms, such as computer-aided design (CAD), enterprise resource planning (ERP), quality management system (QMS), and customer relationship management (CRM). This will help you eliminate data silos, reduce data entry errors, and improve data quality and consistency across your product engineering ecosystem. You should also leverage the capabilities of PLM to import and export product data in different formats and standards, such as STEP, XML, and PDF, to facilitate data exchange and interoperability with your external partners and customers.

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5 How to visualize your product data in PLM?

A fourth important aspect of managing product data effectively in PLM is to visualize it in a clear and comprehensive way. This means using the features and functions of PLM to display and explore your product data in different views and perspectives, such as 2D, 3D, graphical, tabular, and dashboard. This will help you gain more insight and understanding of your product data, as well as identify and resolve any issues or gaps. You should also use the reporting and analytics capabilities of PLM to generate and share reports and charts that summarize and highlight the key aspects and metrics of your product data, such as status, progress, performance, and quality.

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Charles Arceneaux, PMP

CAD, PLM, PDM, & Roadmapping Expert | Transforming Business Needs into Technology Solutions
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Visualizing PLM data effectively requires an approach that emphasizes both clarity and detail. You can start by identifying key metrics and stages of the product lifecycle that are most relevant to your audience, such as development timelines, cost analysis, quality metrics, or even supply chain logistics. Using a combination of interactive dashboards and detailed reports with clear, easy-to-understand charts like bar graphs for cost comparisons, line charts for trend analysis, and Gantt charts for project timelines can be effective Since PLM encompasses a broad range of data types, complex visualizations like scatter plots for quality monitoring or heat maps for identifying process bottlenecks can offer more comprehensive analysis.

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6 How to optimize your product data in PLM?

A fifth and final important aspect of managing product data effectively in PLM is to optimize it in a continuous and proactive way. This means using the feedback and data collected from your product engineering project to evaluate and improve your product data management processes and practices in PLM. You should also monitor and measure the impact and value of your product data management activities on your product engineering outcomes, such as customer satisfaction, profitability, and innovation. Furthermore, you should keep abreast of the latest trends and technologies that can enhance your product data management capabilities in PLM, such as artificial intelligence, cloud computing, and internet of things.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can you manage product data more effectively in PLM?

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1 What is PLM and why is it important?

2 How to structure your product data in PLM?

3 How to control your product data in PLM?

4 How to integrate your product data in PLM?

5 How to visualize your product data in PLM?

6 How to optimize your product data in PLM?

7 Here’s what else to consider

Product Engineering

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How can you manage product data more effectively in PLM?

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1 What is PLM and why is it important?

2 How to structure your product data in PLM?

3 How to control your product data in PLM?

4 How to integrate your product data in PLM?

5 How to visualize your product data in PLM?

6 How to optimize your product data in PLM?

7 Here’s what else to consider

Product Engineering

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