Capturing Tribal Knowledge to Solve the Manufacturing Skills Gap - with Sebastian Dykas of Smith+Nephew

**Marilie Fouche** (0:14)
Welcome everyone to the Emerge AI in Business Podcast. Today's guest is Sebastian Dykas, Director of Manufacturing, Engineering and Maintenance at Smith and Nephew. Smith and Nephew is a leading portfolio medical technology company across orthopedics, sports medicine, ENT and advanced wound management. Sebastian joins us on today's episode to explore how manufacturers can strengthen operations as experienced workers retire and production demands rise. He shares our team should capture expert techniques, standardized training and use real time process control to stabilize output. He also unpacks our machine connectivity and automated feedback loops, reduce scrap, tighten control limits and give leaders clearer visibility. The focus is on practical steps any manufacturer can take to modernize workflows and build more resilient operations. Today's episode is sponsored by Poka. Just a quick note for our audience that the views expressed by Sebastian Dykas on today's program do not reflect that of Smith and Nephew or its leadership. For our solutions partners, position your brand alongside the Fortune 500 leaders defining the enterprise AI roadmap. For the opportunity to showcase your solution to the executives currently funding and scaling global initiatives, partner with Emerge. Secure your partnership at go.emerge.com. That's go.emerge.com.
Now the conversation with Sebastian.
Hi, Sebastian, welcome to the show.

**Sebastian Dykas** (1:39)
Thank you for having me. I'm excited about this conversation.

**Marilie Fouche** (1:42)
There's a lot of talk about digitizing the factory, and we've had some interesting conversations around digital twins and advanced automation. But we hear one thing across manufacturing, and that is a more fundamental problem, a basic problem still lingering, where there seems to be a gap in knowledge. So a lot of knowledge is still in the hands and in the minds and in the hearts of a very few experts on the manufacturing floor. And the question is, how do you get that into the workflow where in an industry where a lot of workers are either falling off, not interested in going into manufacturing anymore, we see a lot of job changes.
How do we capture that? And so before we get into AI, and where all of this is heading, I want to start with what you really seen. Across operations you've worked with, what are the biggest points of friction around training, knowledge capture, or those paper-based processes that still seem to shape frontline work?

**Sebastian Dykas** (2:45)
There are several issues that you brought up that is plaguing the medical device industry, in my opinion.
One of them is operator challenges.
As the older workforce retires out, we're losing the machinists, we're losing some of the experts who've been in the industry running certain processes for sometimes decades. And the challenge is, is how do you create processes that don't require that level of expertise? And how do you transfer that knowledge in a way that someone can easily absorb? The other thing is, in certain areas of medical device, you also have a lot of craftsmanship. So there are machinists who are running CNC machines and making offsets to processes. You have hand finishers that are doing processes to finish the final form of an implant, in some cases. And then you have other just tribal knowledge of, how do you run the cleaning system? Some of the older equipment, when it glitches, what do you do?
You know, that's from the maintenance perspective as well. So there is a challenge as the younger, newer workforce comes online. There's just not that depth of knowledge that we're seeing in the older workforce that's been seasoned in the spend decades in the industry.
And so, what we have to do is figure out how to capture that knowledge. And then how do we then take what is done by these people, best practices, and then standardize it? For example, in a company I worked for, we were finishing hand-finishing, polishing certain medical device implants. And some of the older, more senior workforce was able to basically provide double the quantity and shift as someone who was only doing it for a short period of time.
They had developed their own best practices. They knew just because of quantity and time and hours on the equipment, they could produce almost no scrap, they could produce higher throughput, but it's very difficult to put that and ingrain that into someone who's starting off. So those are challenges as well as the way we inspect equipment, a lot of it is attribute, focus, box gauges, go-no-go gauges, things like that. So we have a tremendous gap of gathering the data of our processes that can help us to adjust equipment or understand what is happening in the manufacturing core. Examples would be we may use box gauges as go-and-no-go to pass certain features on parts, but if they're just off or if they're just big or just too small, what is it that we need to adjust in the previous process to fix that issue? And that's just the data we don't have, and then we can't trend it over time to see what's going on and really understand what might be causing some of our issues. Yeah, we have a big challenge with the paper-based system, paper device history records, paper routers, paper copies of SOPs on the floor, SOPs being updated constantly because of some process optimization, retraining, some of these more cumbersome things. And then in certain parts of the industry, you have a large time to learn a craft. I can give an example. We have a very specific hand-finishing process in a product, and I have talked to operators who started on the journey of learning how to do that. They say it could take six months for them to feel comfortable doing it alone. And that's just not sustainable and it's not scalable. And so those are some of the challenges we have. We make very good products in the medical industry, but some of our processes are very old. And the way we understand what's actually happening on the floor and during the process, that data is not being gathered or might not even exist at the moment. So it doesn't give us something to look at for further insight.