Another year passed, and as usual, I took the time to look back. I always feel that things are going so much slower than expected. But that’s reality – there is always friction, and in particular, in the PLM domain, there is so much legacy we cannot leave behind.

It is better to plan what we can do in 2024 to be prepared for the next steps or, if lucky, even implement the next steps in progress.

In this post, I will discuss four significant areas of attention (AI – DATA – PEOPLE – SUSTAINABILITY) in an alphabetic order, not prioritized.

Here are some initial thoughts. In the upcoming weeks I will elaborate further on them and look forward to your input.

 

AI (Artificial Intelligence)

Where would I be without talking about AI?

When you look at the image below, the Gartner Hype Cycle for AI in 2023, you see the potential coming on the left, with Generative AI at the peak.

Part of the hype comes from the availability of generative AI tools in the public domain, allowing everyone to play with them or use them. Some barriers are gone, but what does it mean? Many AI tools can make our lives easier, and there is for sure no threat if our job does not depend on standard practices.

 

AI and People

When I was teaching physics in high school, it was during the introduction of the pocket calculator, which replaced the slide rule.You need to be skilled to uyse the slide rule, now there was a device that gave immediate answers. Was this bad for the pupils?

If you do not know a slide rule, it was en example of new technology replacing old tools, providing more time for other details.  Click on the image or read more about the slide rule here on Wiki.

Or today you would ask the question about the slide rule to ChatGPT? Does generative AI mean the end of Wikipedia? Or does generative AI need the common knowledge of sites like Wikipedia?

AI can empower people in legacy environments, when working with disconnected systems. AI will be a threat for to people and companies that rely on people and processes to bring information together without adding value. These activities will disappear soon and you must consider using this innovative approach.

During the recent holiday period, there was an interesting discussion about why companies are reluctant to change and implement better solution concepts. Initially launched by Alex Bruskin here on LinkedIn , the debate spilled over into the topic of TECHNICAL DEBT , well addressed here by Lionel Grealou.

Both articles and the related discussion in the comments are recommended to follow and learn.

 

AI and Sustainability

Similar to the introduction of Bitcoin using blockchain technology, some people are warning about the vast energy consumption required for training and interaction with Large Language Models (LLM), as Sasha Luccioni explains in her interesting TED talk when addressing sustainability.

She proposes that tech companies should be more transparent on this topic, the size and the type of the LLM matters, as the indicative picture below illustrates.

Carbon Emissions of LLMs compared

In addition, I found an interesting article discussing the pros and cons of AI related to Sustainability. The image below from the article Risks and Benefits of Large Language Models for the Environment illustrates nicely that we must start discussing and balancing these topics.

To conclude, in discussing AI related to sustainability, I see the significant advantage of using generative AI for ESG reporting.

ESG reporting is currently a very fragmented activity for organizations, based on (marketing) people’s goodwill and currently these reports are not always be evidence-based.

 

Data

The transformation from a coordinated, document-driven enterprise towards a hybrid coordinated/connected enterprise using a data-driven approach became increasingly visible in 2023. I expect this transformation to grow faster in 2024 – the momentum is here.

We saw last year that the discussions related to Federated PLM nicely converged at the PLM Roadmap / PDT Europe conference in Paris. I shared most of the topics in this post: The week after PLM Roadmap / PDT Europe 2023. In addition, there is now the Heliple Federated PLM LinkedIn group with regular discussions planned.

In addition, if you read here Jan Bosch’s reflection on 2023, he mentions (quote):

… 2023 was the year where many of the companies in the center became serious about the use of data. Whether it is historical analysis, high-frequency data collection during R&D, A/B testing or data pipelines, I notice a remarkable shift from a focus on software to a focus on data. The notion of data as a product, for now predominantly for internal use, is increasingly strong in the companies we work with

I am a big fan of Jan’s posting; coming from the software world, he describes the same issues that we have in the PLM world, except he does not carry the hardware legacy that much and, therefore, acts faster than us in the PLM world.

An interesting illustration of the slow pace to a data-driven environment is the revival of the PLM and ERP integration discussion. Prof. Jörg Fischer and Martin Eigner contributed to the broader debate of a modern enterprise infrastructure, not based on systems (PLM, ERP, MES, ….) but more on the flow of data through the lifecycle and an organization.

It is a great restart of the debate, showing we should care more about data semantics and the flow of information.

The articles: The Future of PLM & ERP: Bridging the Gap. An Epic Battle of Opinions!  and Is part master in PLM and ERP equal or not) combined with the comments to these posts, are a must read to follow this change towards a more connected flow of information.

While writing this post, Andreas Lindenthal expanded the discussion with his post: PLM and Configuration Management Best Practices: Part Traceability and Revisions. Again thanks to data-driven approaches, there is an extending support for the entire product lifecycle. Product Lifecycle Management,  Configuration Management and AIM (Asset Information Management) have come together.

PLM and CM are more and more overlapping as I discussed some time ago with Martijn Dullaart, Maxime Gravel and Lisa Fenwick in the The future of Configuration Management. This topic will be “hot”in 2024.

 

People

From the people’s perspective towards AI, DATA and SUSTAINABILITY, there is a noticeable divide between generations.  Of course, for the sake of the article, I am generalizing, assuming most people do not like to change their habits or want to reprogram themselves.

Unfortunate, we have to adapt our skills as our environment is changing. Most of my generation was brought up with the single source of truth idea, documented and supported by science papers.

In my terminology, information processing takes place in our head by combining all the information we learned or collected through documents/books/newspapers – the coordinated approach.

For people living in this mindset, AI can become a significant threat, as their brain is no longer needed to make a judgment, and they are not used to differentiate between facts and fake news as they were never trained to do so

The same is valid for practices like the model-based approach, working data-centric, or considering sustainability. It is not in the DNA of the older generations and, therefore, hard to change.

The older generation is mostly part of an organization’s higher management, so we are returning to the technical debt discussion.

Later generations that grew up as digital natives are used to almost real-time interaction, and when applied consistently in a digital enterprise, people will benefit from the information available to them in a rich context – in my terminology – the connected approach.

AI is a blessing for people living in this mindset as they do not need to use old-fashioned methods to acquire information.

“Let ChatGPT write my essay.”

However, their challenge could be what I would call “processing time”. Because data is available, it does not necessarily mean it is the correct information. For that reason it remains important to spend time digesting the impact of information you are reading – don’t click “Like”based on the tittle, read the full article and then decide.

Experience is what you get, when you don’t get what you expect.

meaning you only become experienced if you learn from failures.

 

Sustainability

Unfortunately, sustainability is not only the last topic in alphabetic order, as when you look at the image below, you see that discussions related to sustainability are in a slight decline at C-level at the moment.

I share this observation in my engagements when discussing sustainability with the companies I interact with.

The PLM software and services providers are all on a trajectory of providing tools and an infrastructure to support a transition to a more circular economy and better traceability of materials and carbon emissions.

In the PLM Global Green Alliance, we talked with Aras, Autodesk, Dassault Systems, PTC, SAP, Sustaira, TTPSC(Green PLM) and more to come in 2024. The solution offerings in the PLM domain are available to start, now the people and processes.

For sure, AI tools will help companies to get a better understanding of their sustainability efforts. As mentioned before AI could help companies in understanding their environmental impact and build more accurate ESG reports.

Next, being DATA-driven will be crucial.  As discussed during the latest PLM Roadmap/PDT Europe conference: The Need for a Governance Digital Thread.

And regarding PEOPLE, the good news is that younger generations want to take care of their future. They are in a position to choose the company to work for or influence companies by their consumer behavior. Unfortunately, climate disasters will remind us continuously in the upcoming decades that we are in a critical phase.

With the PLM Global Green Alliance, we strive to bring people together with a PLM mindset, sharing news and information on how to move forward to a sustainable future.

Mark Reisig (CIMdata – moderator for Sustainability & Energy) and Patrice Quencez (CIMPA – moderator for the Circular Economy) joined the PGGA last year and you will experience their inputs this year.

 

Conclusion

As you can see from this long post, there is so much to learn. The topics described are all actual, and each topic requires education, experience (success & failures) combined with understanding  of the technology concepts. Make sure you consider all of them, as focusing on a single topic will not make move faster forward – they are all related. Please share your experiences this year—Happy New Year of Learning.