At the beginning of this week, I was attending the 9th edition of the PI conference in London. Where it started as a popular conference with 300 – 400 attendees at its best, we were now back to a smaller number of approximately 100 attendees.

It illustrates that PLM as a standalone topic is no longer attracts a broad audience as Marketkey (the organization of the conference) confirms. The intention is that future conferences will be focusing on the broader scope of PLM, where business transformation will be one of the main streams.

In this post, I will share my highlights of the conference, knowing that other sessions might have been valuable too, but I had to make a choice.

It is about people

Armin Prommersberger, CTO from DIRAC and the chairman of the conference, made a great point: “What we will discuss in the upcoming two days, it is all about people not about technology.”

I am not sure if this opening has influenced the mood of the conference, as when I look back to what was the central theme: It is all about how we deal with people when explaining, implementing and justifying PLM.

AI at the Forefront of a Digital Transformation

Muhannad Alomari from R2 Data Labs as a separate unit within Rolls Royce to explore and provide data innovation started with his keynote speech sharing the AI initiatives within his team.

He talked about several projects where AI will become crucial.

For example, the EHM program related to engine behavior. How to detect anomalies, how to establish predictive maintenance and maximize the time an airplane engine is in operation. Interesting to mention is that Muhannad explained that most simulation models are based on simplified simulation models, not accurate enough to discover anomalies.

Modeling in the PLM world with feedback from reality

Machine learning and feedback loops are crucial to optimize the models both for the discovery of irregularities and, of course, to improve understanding of the engine behavior and predict maintenance. Currently, maintenance is defined based on the worst-case scenario for the engine, which in reality, of course, will not be the case for most engines. There is a lot (millions) to gain here for a company.

Interesting to mention is that Muhannad gave a realistic view of the current status of Artificial Intelligence (AI). AI is currently still dumb – it is a set of algorithms that need to be adapted whenever new patterns are discovered. Deep learning is still not there – currently, we still need human beings for that.

This was in contrast with the session from Kalypso later with the title: Supercharge your PLM with advanced analytics. It was a typical example of where a realistic story (R2 Data Labs) shows such a big difference with what is sold by PLM vendors or implementers. Kalypso introduced Product Lifecycle Intelligence (PLI) – you can see the dream on the left (click on the image to enlarge).

Combine PLM with Analytics, and you have Intelligence.  My main comment is, knowing from the field the first three phases in most companies have a lack of data quality and consistency. Therefore any “Intelligence” probably will be based on unreliable sources. Not an issue if you are working in the domain of politics, however when it comes to direct cost and quality implications, it can be a significant risk. We still have a way to go before we have a reliable PLM data backbone for analytics.

 

Keeping PLM Momentum after a Successful Campaign

Susanna Mäentausta from Kemira in Finland gave an exciting update of their PLM project. Where in 2019, she shared with us their PLM roadmap (see my 2019 post: The weekend after PI PLMx London 2019); this time, Susanna shared with us how they are keeping the PLM momentum.

https://twitter.com/josvoskuil/status/1224276842640826370

Often PLM implementations are started based on a hypothetical business case (I talked about this in my post The PLM ROI Myth). But then, when you implement PLM, you need to take care you provide proof points to motivate the management. And this is exactly what the PLM team in Kemira has been doing. Often management believes that after the first investment, the project is done (“We bought the software – so we are done”) however the business and process change that will deliver the value is not reported.

Susanna shared with us how they defined measurable KPIs for two reasons.  First, to motivate the management that there are business progress and benefits, however, it is a journey. And secondary the facts are used to kill the legends that “Before PLM we were much faster or efficient.” These types of legends are often expressed loudly by persons who consider PLM as an overhead (killing their freedom) instead of a way to be more efficient in business. In the end, for a company, the business is more important than the person’s belief.

On the question for Susanna, what she would have done better with hindsight, she answered: “Communicate, communicate, communicate.” A response I fully support as often PLM teams are too busy completing their day-to-day work, that there is no spare time for communication. Crucial to achieving a business change.

My agreement: PLM needs facts based during implementation and support combined with the understanding we are dealing with people and their emotions too. Both need full attention.

Acceleration Digitalization at Stora Enso

Samuli Savo, Chief Digital Officer at Stora Enso, explained the principles of innovation, related to digitalization at his company. Stora Enso, a Swedish/Finish company, historically one of the largest forestry companies in the world as well as one of the most significant paper and packaging producers, is working on a transformation to become the renewable materials company. For me, he made two vital points on how Stora Enso’s digitalization’s journey is organized.

He pleads for experimentation funded by corporate as in the experimental stage, as it does not make sense to have a business case. First DO and then ANALYZE, where many companies have to policy first to ANALYZE and then DO, killing innovative thinking.

The second point was the active process to challenge startups to solve business challenges they foresee and, combined with a governance process for startups, allow these companies to be supported and become embedded within member companies of the Combient Foundry, like Stora Enso. By doing such in a structured way, the outcome must lead to innovation.

I was thinking about the hybrid enterprise model that I have been explaining in the past. Great story.

Cyber-security and Future Mobility

Out of interest, I followed the session from Madeline Cheah, Cybersecurity Innovation Lead at HORIBA MIRA. She gave an excellent and well-structured overview. Madeline leads the cybersecurity research program. Part of this job is investigating ways to prevent vehicles from being attacked.  In particular, when it comes to connected and autonomous vehicles. How to keep them secure.

She discussed the known gaps are and the cybersecurity implications of future mobility so extensive that I even doubted will there ever be an autonomous vehicle on the road. So much to define and explore. She looked at it from the perspective of the Internet of Everything, where Everything is divided into Things, Data, Processes, and People. Still, a lot of work to do, see image below

Good Times Ahead: Delay Mitigation Through a Plan for Every Part

Ian Quest, director at Quick Release, gave an overview of what their company aims to be. You could translate it as the plumbers of the automotive industry Where in the ideal world information should be flowing from design to release, there are many bottlenecks, leakages, hiccups that need to be resolved as the image shows.

Where their customers often do not have the time and expertise to fix these issues, Quick Release brings in various skillsets and common sense. For example, how to deal with the Bill of Materials, Configuration Management, and many other areas that you need to address with methodology first instead of (vendor-based) technology. I believe there is a significant need for this type of company in the PLM-domain.

The second part, presented by Nick Solly, with a focus on their QRonos tool, was perhaps a little too much a focus on the capabilities of the tool. Ian Quest, in his introduction,  already made the correct statement:

The QRonos tool, which is more or less a reporting tool, illustrates again that when people care about reliable data (planning, tasks, parts, deliverables, …..), you can improve your business significantly by creating visibility to delays or bottlenecks. The value lies in measurable activities and from there, learn to predict or enhance – see R2 Labs, Kemira and the PLI dream.

Conclusion

It is clear that a typical PLM conference is no longer a technology festival – it is about people. People are trying to change or improve their business. Trying to learn from each other, knowing that the technical concepts and technology are there.

I am looking forward to the upcoming PI events where this change will become more apparent.