If you’ve been involved into PLM implementations, you probably heard that engineers and manufacturing companies are working differently. Also, data model by one manufacturing company is significantly different. It is true, but only for a certain degree. It emphasizes innovation spirit of engineering and sometimes engineers’ interest to reinvent the wheel. It is certainly true for many aspects of engineering and manufacturing processes. At the same time, it often leads to additional levels of complexity in data management.

To resolve a problem with diversity of data models, my recommendation is to separate use cases between three main data modeling groups – (1) typical data modeling; (2) names and terminology diversity and (3) specific requirements.

Typical data models.

Despite diversity, manufacturing companies have lot of things in common. These things drive the definition of common data models and elements in PLM implementations. Below, I bring typical groups of data data you can often see in almost every PLM implementation:

  • CAD and design data
  • Bill of Materials
  • Projects
  • Products, configurations and portfolios
  • Requirements data
  • Manufacturing data
  • Supply chain and sourcing data

Names and terminology diversity.

It is not unusual, people in two manufacturing companies call things differently. It is okay and, as PLM vendor or service provider, you should not be too much worried about that. Be ready with your comparison table to map “your PLM world” with customer terminology. It can give you some pain during implementation, but it will also help you to clean terminology to use for future releases.

Specific requirements

While standardization is considered as a very important activity, it is not unusually to see variety of data requirements coming from engineering departments and manufacturing companies implementing PLM systems. Therefore, flexibility is on of the most important aspects of PLM technological platforms.

(c) Can Stock Photo

One Comment on “2.5 : Typical elements of PLM data:

Leave a Reply

Your email address will not be published. Required fields are marked *