Like many companies, our end-of-the-year insights were intended to go out at the end of 2021. Also, like many companies, we found our team taxed from the throws of the holidays, wrapping projects up, end of the year tasks, and of course dealing with uncertainty and fluctuations of life due to each new COVID variant. If you are reading this and nodding your head, you and your team have probably been working through most of these things as well.
We are happy to be sharing this conversation between Rhiannon Gallagher, Chief Social Scientist, and Jennifer Herron, Founder & CEO with some interesting insights that we hope will help you and your team solidify implementing MBD this year. It really is imperative to your long-term competitive advantage in our ever-growing digital world.
Insights About 3D Transition – The People and the Process:
How did coaching for Action Engineering evolve last year?
Jennifer: We went really deep across a breadth of topics beyond “what are the tools that we need to do this job?” and even went beyond “what is the training we need to do this job?” We went into, “what does the organization have to look like, feel like, breathe and live to do this job?” And we ultimately realized that tools are a tiny one-tenth of the conversation.
Rhiannon: We had a sense of that already, but it is a different story than you see across the industry as a whole. There’s still a perception that the transition to 3D data is a problem to solve with tools.
Jennifer: We’ve known for at least three or four years that was the issue, and people who have started these transitions before also know it, but they didn’t know how to talk about it and they have had a hard time putting language around it. Finally, we have been able to define some language around it to help those difficult conversations. Knowing it and doing something about it are two different things.
Rhiannon: Part of our learning in 2021 was getting clear on the psychology of transitions. We have more insights on what motivates and threatens people. That’s an eye-opener for implementation teams when we start talking through the primal brain and motivators and they suddenly see the messages they’ve been sending and how they are not working for their downstream consumers.
Jennifer: Another big insight was in your shop floor interviews about autonomy. When we start bringing this digital data to them, that’s been masterminded by engineering, then they lose their autonomy to do their jobs, which wasn’t certainly the intention of the engineers. The reality is that it reduced some folks’ autonomy to do their job, even though the engineers thought they were increasing it, a completely opposite effect.
Rhiannon: And a mixed effect. Machinists and inspectors, people who really must dive deep into the data, felt like their autonomy was lost because they had to talk to engineers more. People in processing or assembly had a much clearer sense of the data with a model and they needed to talk to engineers less, so their autonomy went up while the machinists’ autonomy went down. If we hadn’t understood autonomy as a motivator, I don’t think we would have recognized that disparity and I don’t think we knew it existed before this year.
If we hadn’t understood autonomy as a motivator, I don’t think we would have recognized that disparity and I don’t think we knew it existed before this year.
That’s a big insight I’m trying to figure out how to incorporate into implementation strategies. It may be that initial pilots need to be focused on these teams that are going to get their autonomy increased and you figure out the complexity of what you present to a machinist later. Present an assembly and a parts list to an assembly team now. And then you’ve created all this momentum and all this positive buy-in. Then, you say, “Okay, people doing more complicated things with the data, let’s try and figure out what this looks like for you.”
Jennifer: Right. Because for some reason the instinct has been to start with the hard stuff first instead of getting the easy stuff done to start with. Everybody’s always starting with the part but really, we should be starting with the assembly and going from leveraging that assembly benefit. You can run that parallel effort on the part, but it takes a lot longer. And there are organizations who’ve done that. We had one customer a few years ago that worked it that way. They went for the assembly first. They never went into full-blown model-based definition and annotating because the tools they needed didn’t even exist yet. They got buy-in early on by just doing 3D navigation, giving their tooling, and fixturing people the engineering data so they could build off it right away. Those were a lot of quick wins.
Lessons Learned from Software Vendor Collaborations:
What did you learn from collaborating on webinars with software vendors this year?
Jennifer: I liked building out a step-by-step process and evaluating where the current state of 3D data is and looking at the future state, proving it out with a certain set of tools and showing the metrics in a very discreet believable way, one that is not a marketing glossy brochure saying, “Save 90% of your time.” Literally, nobody will believe that, so why would you even put it on a glossy brochure? It was powerful to build out tangible, believable, authentic metrics that we could prove out and match with the tools we were using. Then the software vendors gave us feedback on the process we were creating as a future state and grounded us in the reality of what’s feasible today. We gave the vendors a vision to shoot for and we ferreted out some little tweaks that they can take back to their development teams and say, “Oh, we found this error in this real-world process.” It was a great win-win for both sides of those collaborations.
It was powerful to build out tangible, believable, and authentic metrics that we could prove out and match with the tools we were using.
Because we’re using our OSCAR models in that process, real parts that somebody would actually go make, not just theoretical holes in theoretical blocks, it’s layering in some complexity that isn’t getting tested at the development level of these software tools. We have put a lot of time and expertise into those OSCAR models to present the state of the art with standards-compliant MBD and GD&T.
Looking Ahead in 2022:
Rhiannon: Looking into 2022, I think we’re finding we don’t have to convince as many people of the social science piece, so that’s exciting for me. Some have failed and realized that the reason they failed was that they didn’t take the people into account. Many are realizing they need to take the people into account before they fail, which is much cheaper in the long run! We don’t have to convince people of that basic premise, so my conversations get to shift into the details of, “Hey, this is how people’s brains work and this is how they are going to respond to the process you’re trying to put it in place, so let’s plan how to communicate with them.”
Many are realizing they need to take the people into account before they fail, which is much cheaper in the long run!
What do you see for the next year?
Jennifer: I think that the digital twin/industry 4.0 conversations have worked themselves through the world enough that we don’t have to convince people that they should be doing model-based definition or use 3D data throughout the enterprise. We don’t have to do that as much, and we’ve got all that well-documented in OSCAR.
Rhiannon: With 2022 coaching clients, we’re starting a few steps up the ladder.
Jennifer: Yeah. We’re starting a few steps up the ladder, which is good because those conversations are exhausting. We’re starting the process to commoditize the implementation of 3D data. It can be a wash-rinse repeat process going forward and not “Let’s make it up” new, every time. That is going to be more efficient and successful for everyone who takes on this 3D data transition in 2022.
We’re starting the process to commoditize the implementation of 3D data. It can be a wash-rinse repeat process going forward , and not “Let’s make it up” new, every time.