Creating Future-Ready Organizations

This article is about how Digital Twins is not what you think, how companies can utilize systems that can solve the skill shortage gap. It's about how nothing changes, but everything will change.

Do you want to solve problems for real? Do you want to collaborate with experts all over the world, and realize the true potential of buildings from a lifecycle perspective? Do you want to stop making decisions in a vacuum and share ideas with others all over the world? Reach out to me here on Linkedin if interested and stay in tune with Beyond Buildings where we will announce something shortly. We are about ten people already from the entire lifecycle that want to solve problems for real, together. It is energy people, FM, technical asset managers, HVAC people, owners, construction, consultants, IoT-people, smart city people, and others. This article will also be featured at Automatedbuildings.com where my mentor Ken Sinclair resides. If interested you can always read some of the other articles that I wrote and see how they have aged over time.

This article will discuss a way to increase knowledge transfer and a way of tackling the skill-shortage gap in a future proof way. It is the combination of automation, digitalization, faster knowledge transfer through agreed-upon definitions of truth, that will solve the skill shortage gap in the short-to-medium-term. If everyone understands that only a few understand today, and even fewer people are needed to do what many people are doing, we get a double-whammy. Eventually, labor will be automated for the most part and the skill shortage gap will be no more.

A short audio clip about this challenge here to start with.

The future that everyone wants, and desperately needs

They had become legends. Not only in their organizations but also throughout the automotive industry. They had been the ones who realized that all the silo work did not lead to anything. The impact they needed always took too long, and it had been too hard, if not impossible to track changes and create ways to continuously learn from their mistakes. They did not understand the underlying technology of these modern tools, but they could very well understand the benefits and the new world order. They understood that if nothing changes, nothing will change. And they wanted to be the change they saw that was necessary. And because of that, they were hailed as legends, with a legacy far beyond anything that they could imagine.

Just imagine if the CEO, CFO, head of HR, Marketing, CTO, and some product managers and the head of the AI team was working on the strategy together in real-time. And a couple of the board members wanted to be in on these sessions because they were so much fun. And everyone understood each other. They were all talking, laughing, and changing launch dates, and how this could affect the other teams, and what a change in one area might lead to advancements in another area. And also, what problems might be incurred. The system they were building their decisions on worked in a way so that it not only answered questions, but also provided insight, and recommendations based on the output that they were looking for and found optimal conditions in real-time. It took in data from existing systems, no matter the source. Internal, external, from a local setting, from the world. Suddenly the CFO could make an instantaneous decision if they would go for a faster time to market, sacrificing potential recalls. But the upside would be an extra 2,5-2,8 Billion over the next three years which would allow them entry to other strategic areas. The AI team had seen an exponential increase in the organizational impact they were having, and the word had spread to other automotive manufacturers that still worked in silos, with disparate data sets, that they had succeeded where others were still struggling. Where data scientists were still cleaning data, and never realizing true value because of a lack of incomplete data sets, and a challenge in comparing data from different sources. And if they went for the lighter alloy, that cost more, but it made them slide under a weight standard, so they could classify the car differently so they would save hundreds of millions from a fleet perspective. And the lifecycle testing for all the function owners, and everything in the car, that once had taken them 6-8 months, were now cut in half even though the increased the number of use cases exponentially, and that their answers were more accurate than ever before. And they could use the data in a much different way during the entire lifecycle, and communicate, collaborate between silos, leading to exponential value increase. The most interesting part was that nothing really changed in how they worked. But at the same time, everything changed for the better.

"Suddenly the Innovation platform ate culture for breakfast, lunch and dinner"

They had something they all could agree on that was fed real-time data from everywhere. Visualized in a way so that everyone could understand how it all came together. For them, it was a car. It was their factories. It was the organization. For others, it might be a truck, a building, a city, a ship, a turbine, a coffee-machine - anything that has a physical presence in space and time. They had the 3D models, but they were only used in silos. It was only when they managed to put all the pieces together that they understood it had always been that easy. The solution had been there all the time. After decades of political and hierarchical struggles, everyone could now base all their decisions on one true source. It was more than a Digital Twin. It was beyond buildings, and beyond everything, they had ever wanted. It was an innovation platform that revolutionized how they, and everyone else made decisions in the manufacturing space. No more biased decisions and agendas. Just fact-based decision making leveraging other people's expertise, that was constantly aligned with the corporate mission and vision.

The tides had turned and no longer did culture eat strategy for breakfast. Now, the Innovation platform ate culture for breakfast, lunch, and dinner.

Yes, the world would surely head this way eventually. But they were first to it. And they could stay there for a long while because they were making data-driven decisions and eliminating waste faster, and better than anyone else in the business. Finally, they could let their size, scale, talent within the walls, and modern technology come together to do all the things that they, and everyone wished they could do. All thanks to a new perspective of a century-old problem.

Setting the stage - skill shortage and knowledge transfer

Imagine a large something that looks like any other large something. It could be a building, a ship, a truck, a car, or even a city. We got systems that are not necessarily connected, there is the old with the new and there's always decisions being made from assumptions. If we think about a factory or a large commercial building we can see that there's maybe one person who knows how it really operates because they have been there from the beginning. They might know how the factory works but how could they know how their decisions would affect other people? How would they know that their decisions would be aligned with the goals of the company?

The decisions of one will no doubt have an effect on the mix of people from other disciplines,

And in the future, all the other unknowns that want to offer services to whatever it is the company needs to utilize to bring better value for their stakeholders.

We are not only looking at a skill shortage gap, but also the skill transfer/knowledge transfer gap.

How, if ever, can we make these people agree on a shared truth?

How can we enable collaboration and communication between experts in their respective field, and with people with zero understanding of HVAC, lighting, pneumatics, valves, BACnet, vendor lock-in, the cost to do something, where stuff is, etc, etc.

How do we transfer the knowledge from the one person who knows everything, into the minds of the 5 people with different backgrounds, working in separate parts of the organization/building?

How do we agree on a shared truth between engineers when they only have access to BI-dashboarding tools, that offer a glimpse into a part of the building that is also reserved only for experts?

"Trying to learn what Web Services Digital Twins are by dissecting the acronyms is like trying to learn how to drive by studying an automatic transmission" -

(Paraphrasing) by The Legend Steve Tom (from his seminal article about BACnet/WS in 2004)

Agreeing on something that is based in space and time

Everyone is trying to make simulations of how the real world will operate. People are doing it in the automotive space, industry 4.0 initiatives, building automation, anything manufacturing, and in all areas where there's business. And companies are failing. Miserably so. Everyone is starting to say that they have got Digital Twins operating for them and they already have the keys to the kingdom. Most of them are wrong.

It is simple. Everyone is just working on their sliver of reality from a real-world puzzle and their so-called digital twins also operate in silos. Everyone is using BI-dashboards that only make sense within their own discipline, and experts cannot explain what they do to someone else outside their frame of reference. Decisions are made in vacuums.

But what is the one thing that they all should agree on, but find it hard to do? Well in the automotive industry, it is the cars, the trucks, that they work with.

For buildings, it is... yeah, you guessed it. Buildings!

That factory consists of systems that are usually not interconnected. And they also consist of HVAC systems, the actual pipes and plumbing, and people with responsibilities in different areas.

- Suddenly! A pipe bursts on the far-left hand side of the factory!!!

- What to do? The one in charge of that has no clue where it leads to, and their BI dashboard does not show the correlations to the entire plant, only their area!

- The one that knew all this retired a month ago and the 4 people in charge do not know where to start and how everything is connected.

- And the one person that has an idea, cannot explain well enough, nor get the understanding from all the different dashboards.

Luckily - they invested in an innovation platform the other week that solves these challenges! What does it do?

- They have integrated the data from different applications across silos into an innovation platform that is built upon a Digital Twin database.

- It absorbs data from any kind of source, irrespective of standard, API, and it makes sense of the world as it is by mapping data sources to a non-industry specific ontology.

- Initially, data was mapped towards the visual aspects of the Digital Twin and attached to CAD/BIM/GIS data from a template building/the real building to enable people to understand and see the space that they are all in.

- Everyone can see why the pump burst, what it is related to in the entire factory, and they can all agree with what the problem is, and what the solution is. As well as run simulations about what will happen if they do x y and z, and get suggestions from the system itself.

- They can quickly come together and determine what needs to be done since the systems are talking to each other, and that people can communicate around a one true source that is the visual digital twin. Not just dashboards.

- Other data that hasn't been available help them in an invisible way of making decisions by eliminating possible outcomes that are too risky, too costly, or will lead to unintended consequences for any of the other disciplines in the factory/or organization.

This pipe bursting is of course a hypothetical case since this would not have happened in the first place. Everything would have been connected and this would have been prevented before it happened. Managing risk is after all one of the major benefits with a connected innovation platform.