Improving business outcomes with a digital twin

Over the past 12 months there has been much talk or even hype about Digital Twins. Typically, it is the technology which everyone gets excited about and they often overlook the problem we are trying to solve.

At Twinview we are clear in our purpose, to help our clients improve business outcomes. Twinview allows our clients to achieve this in many ways and provides them with flexibility to use the information and data the platform gives them access to.

In 2010, Building Information Modelling was in a similar position, where again, there was hype about the technology without a true understanding of the value a 3-D digital model could offer to the design and construction process. BIM for building developers provided improved predictability of cost and programme, reducing their risk.

In this article we will set out how we believe a digital twin can improve business outcomes as part of a property digital strategy. As a starting point and to get it out of the way, our definition of a digital twin is:

“A digital representation of the physical which improves business outcomes.”

In 1991 to 2010 global productivity was 4%, however between 2011 and 2015 global productivity declined to 1%.

In the past decade we have seen exponential growth in new technologies. Today there are 30 billion IoT (Internet of Things) devices generating more than $30 billion of revenue with an economic impact of $11 trillion by 2025.

The Industrial Internet holds the key to unlocking growth over the decades ahead with new business models and the use of data.

Companies such as Amazon developed their business model and refined how they use data to optimise their services. Initially they used simple demographic data as to where we live. This progressed to psychographic information such as who we know or our purchasing habits. With this information they could predict our purchasing habits.

Additional knowledge has allowed them to grow their business with new associated services such as Amazon Prime or Alexa.

In the property sector we have similar opportunities. The equivalent of demographic data would be the information we gather for a single building system with psychographic information being the number of users over time, or room usage.  This data can then grow as future buildings within a portfolio are added.

Additional knowledge as to how their buildings operate allow property owners to consider new services such as tenant experience apps or even new business models such as flexible office space.

The Aerospace and Power sectors have led the development of the digital twins, most notably GE, who have invested in research across all of their business. 50 years ago, aeroplanes were a mechanical and analogue form of transport collecting no data. Today, GE jet engines gather huge amounts of data and with Machine Learning and AI involved they are able to optimise the performance of every single one of their jet engines as they travel around the world.

Over a similar period, the property sector has made progress with sophisticated heating and lighting systems now embedded in buildings. In 1931, the Empire State building was designed with few systems being naturally ventilated and heated with radiators. 60 years later the Freedom Tower was opened with air conditioning, ventilation and digital control systems. The building was naturally ventilated and heated using radiators.

However, whilst these systems produce huge amounts of performance data, it is not used to learn and optimise system performance. There are hundreds of other buildings in Manhattan producing a similar level of data which is not analysed either individually or in a group.

In the 21st century we face the challenge of reducing the level of carbon we use. Buildings are the greatest consumer of energy and production of CO2 in their construction and operation.

With more data than we have ever had as to how our buildings function, we have an obligation to optimise and reduce the level of resources across the property sector.

The car industry faced similar challenges 30 years ago. Much like the aeroplane, the car was a mechanical mode of transport collecting little performance data. As cars became more sophisticated they gathered more and more performance data in real time. Most cars now feed information back to the manufacturer in real time and the driving mode is adjusted as you drive with features such as an Eco mode.

This data is continually analysed and is fed into ongoing product development. The sector has now committed to discontinue the combustion engine and move to cleaner fuels.

There is a similar opportunity in property to optimise buildings in design, construction and operation through analysis of the data collected. With the internet, 5G, IoT devices and cloud computing, the digital twin is now a reality.

For the past decade, the sector has been producing data rich, accurate building information models in response to the inefficiencies of design and construction. By overlaying the real time performance data onto the 3D model, we have the opportunity for a true digital twin.

With this data we can analyse and optimise our buildings performance and improve business outcomes.

The opportunity we now have in property is that we have been developing quality building information models for the past decade. Fast speed internet, low cost Internet of Things and cloud computing makes a digital twin very achievable.