Cyber-Physical Design Sprints within the Enterprise

Video and transcript from Lee Duncan & Dan Benedict’s talk at Austin’s 2nd Annual Facilitator Summit, Control the Room

Please join us for the Control the Room 2021, which will be held Feb. 2-4, 2020. You can find out more and buy tickets here.

This is part of the 2020 Control The Room speaker video series.

In February we hosted the second annual facilitator summit, Control The Room, at Austin’s Capital Factory. We launched the summit last year in partnership with MURAL to create a space for facilitators to gather, break down the silos, and learn from one another.

The three-day summit is a rare opportunity to bring together an otherwise unlikely group of highly experienced and skilled professionals across various industries and crafts—from strategy consultants and negotiators to Scrum Masters and design thinkers.

Anyone interested in deepening their knowledge on how to successfully facilitate meaningful meetings and connect with other practitioners is welcome. Together, we dive into diverse methodologies, expand upon perspectives, and learn new insights and strategies that enrich our expertise.

This year we had the pleasure of welcoming 24 speakers, all innovation professionals, who shared their insights and strategies of successful facilitation.

Two of those speakers were Lee Duncan & Dan Benedict.

Lee Duncan is the Enterprise Design Sprint Leader at IBM, and Dan Benedict is the Digital Product Designer at IBM. In their presentation, “Cyber-Physical Design Sprints within the Enterprise,” they detailed the six steps of the cyber-physical system (anything that can sense, infer, and act, such as self-driving cars and changing thermostats) and how to navigate the innovation process:

1. Configure
2. Compile
3. Compress
4. Model
5. Build
6. Test

Watch Lee Duncan and Dan Benedict’s talk “Cyber-Physical Design Sprints within the Enterprise”:

Read the Transcript

Lee Duncan:

Okay. You can hear me. All right. So Douglas, I forgot to tell you there are 27 other co-facilitators. I didn’t mention them. So if you come on up, have you come up here. I want you to move your body before we get started, move your body. So I want you to do a corporate burpee. What is that? Two up downs. I want some vassal dilation. So if you don’t mind from your seated position, this is the corporate enterprise burpee. Stand up, sit down, stand up, sit down. That’s it. You’re wearing your corporate active wear. Congratulations. Okay, so get started. Let’s do some introductions. My name is Lee Duncan. I’m a methods facilitator. This is…

Dan Benedict:

Dan Benedict and I am a prototype facilitator.

Lee Duncan:

Okay. So we work together and that’s some of the value of co-facilitation. All right. So we want to tell you about cyber-physical sprints. What they are.

Lee Duncan:

You probably asking yourself, seriously I’ve never heard of cyber-physical anything. So what exactly is a cyber physical system? A cyber physical system is something that consents, can infer, it means it computes and it can act. It’s basically those three things. And if you think a cyber physical system is from the future, it is, but the future is now. It is self-driving cars, it is also the thermostats that you have in your house that collect data, they’re acting on it. Those are cyber-physical systems. So now you have an understanding of what a cyber physical system is, but now I need to give you a reason to care. So we’re going to do some cognitive hot prompts to get some friction with why you need to care and why your methods may not be ready. All right. So get a sticky note out, get your pen out.

Lee Duncan:

And this is going to be an on the spot quiz, fill in the blank to these prompts. And what I’m going to ask you to do is to tell me and describe what you think the future will be like five years from now. How many IoT devices do you think will be in the world? Okay, write that down. Then you see 79.4. So 79.4 what? What is the measurement of the future for data? And 5G? What will it cover? What percentage of the global mobile data will cover. And IoT overall, what percentage of the real time data, which is what we’re moving to is real-time data. What percentage of that will cover, and then show me the money. How much is this total available market worth? So write that down.

Lee Duncan:

Okay. Now it’s time to reveal. There are going to be 42 billion IoT devices, minor there are seven to 8 billion humans in the world. That’s more, a lot more. Zettabytes. I don’t know if you’ve heard of zettabytes. Most people have not, but here’s a fun fact. There’s 2.7 to three zettabytes of information available right now. That is the digital universe that we live in. 79.4 as compared to three now that’s more, that’s a lot more. 5G. 5G will cover 50% of all global mobile data traffic. That’s happening now. It’s going to accelerate big time. That’s what’s going to turn the machines on. Machine to machine communications with zero latency is going to happen starting now. And IoT collecting what percentage of real-time data it’s a lot 95%. And now for the big one, how much does this market worth would you guess? [inaudible 00:00:04:11].

Lee Duncan:

Okay. That backfired big time. 3 trillion. It’s still a lot. It’s more than I have in my wallet. By a significant portion, because all I have is like a receipt from 7/11. All right. So here are the steps, now you want to know, all right, I’m hooked. I’m interested. How do you do this thing? Well, there are six main steps. First we configure, we compile, compress, model, build and test. It rhymes, you should remember that. All right. So let me zero in and use your machine vision powers, which by the way, machine visioning is a cyber-physical thing. And here, if you’re able to see it, and I’m not sure I’m able to either. Here are the components. At the beginning, we configure, we make sure we’re solving the right problem, problem framing, but we get really serious about it. And we do some opportunity indexing.

Lee Duncan:

So even if it’s a real problem, there may be other opportunities out there. And you want to spend your resources on the right thing. Then we do some team engineering. We have to have the right experts. You cannot leave it to chance. This is technical stuff, deep tech. You need to have the right people in the room. And if you find out you’re missing that deep tech or the expert, when you’re in there, you’re toast. Supporting. We have premeditated musing. What the heck is that? That’s where you tell people to start looking for things, observing things. Many times you have to give people time to process and observe. You have to allow for those productive accidents to occur. If you tell them ahead of time, as opposed to just doing musing or looking for inspiration with 30 minutes, you’re going to miss stuff. Okay. And I think it’s also important to remember that sleep is one of the most powerful ideation tools that exists.

Lee Duncan:

So we also take a look at their innovation wellbeing. We make sure they’re sleeping right. We also make sure they’re tapering down for our event. We don’t want them working on multiple things at the same time, working late hours. We want gap days between that and the event. It’s important. We’re spending a lot of money. We’re having a lot of resources. And because of cyber-physical, we have a lot of experts it’s front end loaded and information. That’s a big difference. And we also have a cyber-physical design kit. We use fast materials to think and search with your hands as far as solutions. We also have a maker space, which has all those materials. And as far as compile, we have extreme experts which I went over. We have intentional listening, affinity tagging so we don’t get mapped shock with all the information that’s there.

Lee Duncan:

We have deep mapping and then we pick a target. And then to move a little faster because I’m taking forever. We compress that information, we have a 10X demo. We want to see the most interesting and exciting uses of technology, opportunity rendering, a cyber-physical sketch, and then we model it. We do blind mashup voting. We want to do a bias busting. Only the most novel ideas should live. And then we do some design sparring. We need to allow for contrast conversations, people to provide opinion. Otherwise a bad idea is allowed to exist. All right? So moving on, we build it. And a new term, which you may not have heard of is we have prototype operations. We break down all of the components of prototyping into individual pieces, because if you’re prototyping data, or if you’re prototyping something physical, that’s different requires different expertise and you have to be aligned on what’s going to be done when there’s a lot, there are a lot of complexity.

Lee Duncan:

And when it comes to testing day, we tasked with extreme users. We want the strongest signals possible. All right. And then we have some additional testing we do, which we call in fera testing. That’s fancy Latin, which means in the wild. We want to see how people react in a natural environment. And those are some of the components of the process.

Lee Duncan:

Now this is where I’m really going to go fast. Experience… if that wasn’t fast enough, experience debt assessment. We ask ourselves what happens if we do nothing? That is a choice. Where are we going? Because sometimes the best sprint is the one you don’t start. Do we have a responsibility to do something? What does it take to succeed with the current map? What is being done in the absence of a solution? And we take a look at the physical and digital components. How do we take a look at the physical interactions? What are taking place and back to data? Data is hard. And what we’re looking at is thick data. That’s the mix of quantitative and qualitative data. And we’re figuring out how to model that and how to get the best insights possible.

Lee Duncan:

So we take all this information, we put it on an achieveability canvas. We love a good canvas. I’m sure you do too. And we ask ourselves what capital’s present? Do we have the humans to build this thing? Do they have the skills? Do we have the political capital? The social will to make this. Because one big problem that is addressing Douglas’s book about the prototype of what happens after, is the idea is not enough. You have to make something from it, right? If you run a marathon and you’ve collapsed before you hit the finish line, that is not a successful event. You probably want me to get the sticker for the back of your car. So we take a look of all that. And another thing which is new, and I think quite important for the enterprise is responsible framing. Do we have an ethical and moral responsibility with the adoption costs, et cetera. So that’s what we do. We also share meals. We know that sharing meals is important, the fastest way to get swift trust. All right, I’ve burned all your time.

Dan Benedict:

All my time, but I got three minutes. So you thought Lee was fast. I’m going to whip through this very quickly. But we’re also looking at bringing somebody new to the table. We talked a lot about engineering the ideal team, created communication and trust. So we want to make sure that all that effort doesn’t go to waste. We want to be able to hold people accountable in the work that they’re doing to the standards that we mapped out during the achievability canvas, making sure that that capital is put to its best use. So the innovation villain is someone who’s going to come in and measure the ideas and the actions of the team to these constraints to make sure that we’re moving forward in a manner that is realistic, because you don’t just want to get to the test you want to get to the end product. And that’s this person’s goal. They’re designing for tension to make sure that you really reach the end game there.

Dan Benedict:

Then we’re looking at experience mapping in a new way too. We’re not just trying to take account of the user and their actions. We have to map specifically what is going to be occurring during the event and during the cyber and physical components. What is going to be sensed? What is going to be inferred? And then how are we going to act upon that back in the physical world.

Dan Benedict:

So then we have some checks and balances to make sure that as we can move from the map into the solution sketches, everything is weighed against what it is that the organization or team has decided to value. Is it the political capital? Is it high in user value? Is it going to show a large return for the company, organization or team? Is it high-and safety? Being able to measure these things as we look at each individual’s solution sketch can help them measure exactly what they’re going to be capable of and what the user may be able to take on.

Dan Benedict:

Then we think that one of the major things moving out of the prototyping stage with this is aiding the understanding of the team in the technical expertise that is necessary to create this end product. It may not be enough to simply say that it would work with a magic ward. You want to have the right terminology. You want to have the right expertise. And if it’s not built into the team, you can augment that team with prototyping cards, which expand on ideas of your sensors that are available, whether it’s a microphone, a camera, ultraviolet, and then how does that information transmit into the cyber? Is it through a closed secure network or using Bluetooth infrared? And then what type of processes are going on? And you can augment processes with any sort of cognitive elements that are going on, blockchain, whatever your digital medium is.

Dan Benedict:

And then again, how do we get back to the act? What is going on in the physical world? And then what type of actuator is making that occur? Is it an LED? Is it a motor? Is it a fan? So we’re helping people bridge that gap by giving them the terminology that they need to correctly convey exactly what they hope to make in the end.

Dan Benedict:

And then of course setting expectations, because in order to get to the end, you still have to pass that test. So with this complex idea, how do we do that? We set the right target. We pick exactly what we’re going to be testing. We determine the right test. Who’s the right audience. How do we conduct this? How do we make this? And then what level of fidelity is that executed at? Which allows us to move on knowing that we have the right idea that we’ve generated the capital that we don’t have and successes in the near future. So again, we have our wonderful statistics here. It’s a booming industry. There is going to be an unprecedented level of data. And as such, there is an unprecedented call to action for reliability, trust, and responsibility when creating in this area. Thank you.