PLM Interest Group

The PLM AI Working Group is a collaborative project that aims to demonstrate the best-practice methodology for applying Artificial Intelligence within the PLM environment.

PLM has very specific requirements in terms of how AI should interact and perform, and also offers areas where AI tools could be particularly useful.

The complexities of PLM are well known to its practitioners, but not to the creators of AI engines.  AI needs to be shaped, and not just within PLM itself - as PLM reaches across the enterprise, AI agents from different domains will need to interact with each other, which creates a whole additional layer of 'orchestration' that needs its own structure and planning.

			The AI Working Group will find the answers by practical testing on real AI environments, allowing participants to build their own systems aligned with the central 'sandbox'.

The AI Working Group brings together people who want real solutions and leverages the advanced knowledge that has already been gained, crystallising the facts so that everyone can work right first time.

Solid ideas and concepts will be in a framework that doesn't move.  The depth and complexity can be explored in detail and everyone can start to build.  This is a holistic, year-long collaboration that will transform the ease and efficacy of AI implementation within PLM.

The Project is entirely practical.  Over the course of 12 months we will test and build a working PLM AI environment, and define the roadmap of possible implementation routes.

The 12-month programme is split into three phases: Mobilisation, Generation and Confirmation.

As the Project starts, every participant has a different AI setup and outlook.  In Mobilisation, the first step is to define a core logic that everyone can work with; and then to agree the method by which testing can be carried out without disruption to operations or prejudice to IP.  The third step is to define the Sandbox itself so that it will be representative but also flexible for individual needs.

The Generation phase is the engine room of the Project.  Participants collectively test their versions of the Sandbox and the PLMIG processes the results.  In parallel with this the Group addresses the advanced possibilities of AI for PLM, again coordinated and documented by the PLMIG.

In the Confirmation phase everything is rationalised, structured, and laid out as a roadmap for PLM AI best practice.  This neutral layout is integrated with the PLM Body of Knowledge, while participants take away their own fully-advanced configurations to continue with into the future.

PLM practitioners who are applying AI need clarity about the benefits and pitfalls, and some neutral guidance about the best route to implementation.  The Industry White Paper provides the starting point.

The White Paper just clarifies what has become a very confused situation, and acts as a reference point for sound ideas.   It begins with a Position Statement that declares how PLM should relate to AI, which serves to align everyone's thinking; and then expands into a structured overview of the factors that PLM practitioners need to bear in mind. It acts as a focal point for discussion and planning that keeps attention on the important issues.

There is an explanation of the 'Golden Rule', and a Vision that enables everyone to look to the future. Positives and negatives (because there are negatives) are highlighted, leading to a section on Industry Best Practice and the possibilities of enhancing AI adoption through collaboration.

The White Paper is published at Version 1.0 for general discussion, and feedback from practitioners everywhere is invited.  A Feedback Form is provided.

Download the White Paper [Word] >>>                              Download the White Paper [PDF] >>>

The Proposal shows how the White Paper can be brought to life.  It sets out a 12-month framework of active collaboration that will not only establish what AI best practice is in the context of PLM, but will give each project member their own live, running, optimised AI system.

The main body of the document explains the methodology in detail - in particular, how the 'distributed sandbox' mechanism develops each participant's own systems and protects existing IP while generating the formalised new knowledge.   It covers the issues to be resolved, the Target scenario of the future, and the additional possibilities for knowledge management raised by non-LLM AI research.

The collective target is to produce the landscape and roadmap that will become part of the industry-wide PLM Body of Knowledge.

The target for each participant is to have developed their test 'sandbox' into the fully-functioning, comprehensive AI platform they will use into the future (for users); or to position and enhance their products and expertise within the best-practice paradigm (for vendors and providers).

Download the Proposal [Word] >>>                              Download the Proposal [PDF] >>>

The first thing to do is to download the White Paper and see what you think of it.  It cuts through the hype and collects the facts into one reference document, simplifying the picture and making it easier to think about what can be done.

For those who are spending serious time and effort on making AI work, the Proposal shows how collaboration and structured testing within the Working Group allows you to get faster and better results.

If you would like to know how this could work for you, you can request more information via
aiworkinggroup@plmig.com.

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