PLM Glue Technologies for organization – recipe #1.

Organizational expectation from PLM implementation is to organize entire lifecycle process of product in organization. With this expectation PLM positioned in organization as set of products and initiatives aimed to play interlock role between other organizational systems – ERP, SCM, CRM, SDLC. My proposal is to see what specific technologies need to be developed for PLM to successfully provide solution for this interlock role – I called it “PLM Glue Technologies”.

I’d like to start from PLM data modeling. Data Modeling for PLM need to provide way to manage complexity of product data in organization. On the surface this is technology need to represent all aspects of product data – design, engineering, manufacturing and supply chain models. One of the key capabilities of this model is to reflect product semantics of manufacturing organization that can be different for different industries, organizations of different sizes as well as ability to be integrated across organizational value chain. Second important characteristic of this PLM data model is ability to be changed within time. In modern organization cost of change is significant and PLM as mission critical system need to be flexible to serve this needs. Third capability is to be connected to models of other organizational systems.

What technology and methodology can help us to successfully implement PLM data model for organization? RDBMS is the most mature technology today to support storage and organization of data. As part of advanced data technologies, models for data warehousing and master data management models provide significant addition to build complete product data model. Together with these established models, new emerging semantic data modeling technologies comes to the space from semantic web, XML and other places. Methodology for data modeling starts from more traditional entity-relation modeling and advanced models for enterprise architecture in organization.

 So far PLM Glue Technologies recipe #1 is PLM data model with following capabilities:

1.     Model product semantics of manufacturing organization

2.     Adaptive for change to reflect organizational changes

3.     Integrated into organizational enterprise landscape.

In today’s world robust and scalable PLM data model is still need to be developed to reflect all needs of modern manufacturing organization.  This PLM data model can serve as consolidation factor for PLM industry, Software vendors and Service organizations.


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  • Henk Jan Pels

    Glad to read that data modelling is important for PLM. However I am surprised that you mention entity relationship modelling as a technique, while today the UML static structura diagrams are a more general accepted tool, which especially is much stronger in expressing abstractions and inheritance which are so important in product structures.

  • Hello Henk and thanks for your comments!
    Indeed data modeling is very important for PLM, since ability of PLM to manage lifecycle and processes is highly dependent on how product and process model can be support and how this model can reflect product and organization. You are right about UML, UML is more generally accepted. On the other side many people see UML is too complex in practical life. Our systems allows you to work with UML, but if you take a look on our administration tools that helps people to create model, you will find lots of similarity with ER diagrams. This is just to make life easy for customers.
    Regards, Oleg