Predicting productivity

September 24, 2017

By Claire Penny

undefinedWe spend a huge amount of our days and nights in a building of one form or another. Yet, when buildings are designed, constructed or retrofitted, the end users of the buildings - the people - become an oversight, with the greater focus going to aesthetics, capital costs and yield. 

There are a multitude of reasons for this. Limited capital budgets, project costs, poor communication along the supply chain and the speed of project completion are all contributing factors. Building information modelling (BIM) is going some way to course-correct a few of these issues, but research has clearly and repeatedly shown that the majority of workplaces do not meet the requirements of today’s workforce. A survey conducted by the Middle East Council for Offices and the RICS found that 67 per cent of respondents found at least one thing in their workspace negatively impacting their productivity. Released in May last year the survey also found that when people were asked what was the one thing they would change, environmental comfort was ranked in top spot.

As the workforce continues to change, and with a deeper focus on productivity within companies, we have to deliver buildings that are central to the users needs.  This does not just apply to the workplace: think about the last time that you went to a hospital – were you able to navigate your way around easily? Could you find where you needed to go without asking member of staff?

Now with the onset and proliferation of Internet of Things (IoT) devices, the amount of data that is generated on a daily basis by our buildings and surrounding environs has grown exponentially. This is our opportunity to provide buildings which start to work for, and provide value to, the end users, which in turn should increase the productivity that businesses covet. 

We can provide building users with cognitive concierge services, enabling them to request services using natural language, via an employee app. Over time the systems will start to understand your preferences and ensure that the desk you’re allocated in any particular office has the light levels that you like, or that your preferred coffee is delivered to your desk and other environmental factors are set the way you like them.

Using adaptive machine-learning models, which analyse an individual’s consumption by influences such as day of week, weather and occupancy, operators of the building can start to learn and predict behaviours in energy utilisation at the building level, at speed and at scale. If anomalies are detected, they can be tracked down to the sub-meters that caused the anomaly using the meter hierarchy. The models then can help to explain the cause of the anomaly at the asset level. 

Operators can use augmented reality to diagnose problems and even control the assets. Using the same approach with machine-learning, space managers will have the capability to learn how space is utilised, at the building, floor, room, desk level and be able to ensure that they have the right type of space for users, today and in the future. They can put a very accurate cost, per person per square metre, and show exactly how much space is costing at its current utilisation rate – often a very sobering figure.

Critically for this region, this technology may help to close the loop between the design, construction and operation of a building. What level of intelligent and sustainable buildings could we collectively deliver if the data and insights from the operational phase of a building was fed back to architects and developers? They would be able to verify that the designs and materials they chose are fit for purpose, years after construction, and that they are still delivering what they were designed to do. Or they could quickly identify areas that we were not so successful and address them in retrofits or future designs.

The use cases are limitless, but what we need are forward thinking companies who put their employees and their productivity at the centre of the workplace, ensuring that buildings are working for the people, not the other way around.  We have the sensors, the data, the analytics, and the platforms. We have the security. Now, we just need the will to deliver.

About the author

Claire Penny is Global Industry Leader - Cognitive IOT for Buildings at IBM. She will speak about how artificial intelligence and big data will change the way buildings operate as part of the Imdaad Work Series, taking place on September 26 at the FM Expo. Follow her @Claire_PennyTay.


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