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Operational advantage

CAFM continues to help FMs meet the challenge of COVID, while the data produced can be harnessed for the future monitoring and wellbeing of buildings, assets and people. The experts tell us how


In March 2020 the COVID-19 pandemic became a frightening reality as lockdowns became prevalent across the world. The CAFM industry went into overdrive, with vendors repackaging their products as safety essentials that would help bring organisations back to their workplace.

Many of these solutions addressed the situation – with temperature screening, cleaning schedules, and socially distanced space planning. While these all provided immediate solutions for a workplace, as time moves on businesses are beginning to look at using smart and sustainable technology to enhance their facilities services.

Driving this requirement is a desire from the industry to make use of data to make automated decisions. With such a raft of information available, we must ensure that we are using the data to add real value to the operation. Enormous volumes of data are collated in CAFM systems, but the usage has traditionally been retrospective reporting or perhaps some dashboard rationalizing various KPIs. While these can look very attractive the content is always looking back, never forwards.

Artificial intelligence has been a hot topic to discuss in recent years, however, there is still much scepticism in terms of the effort and investment required to achieve the desired results. This can be overcome by identifying realistic outcomes and focussing on simple achievable use cases that deliver value to either the customer experience or in terms of reducing costs. By identifying key pieces of data and applying some basic algorithms we can do some very simple things that can dramatically alter our operational delivery.

For example, when a CAFM system is initially set-up it is usual to set estimated time and cost values against planned maintenance activities, such as the servicing of a chiller. As time moves on, the maintenance activity will be regularly carried out on the chiller and from the mobile workforce team employed we can collate accurate time data on the time taken by the engineers who carry out the work. So, if we had estimated a planned maintenance task takes eight hours, it may transpire it has never taken longer than four hours. The CAFM system can be configured to automatically update these estimated times to better resemble the actual time taken.

Now resource planners can prepare for a job that takes four hours and not eight and have recovered significant time. When we consider the volume of tasks that are planned daily, we have a great opportunity to dramatically increase the efficiency of our workforce.

Next, we can think about the ways that our CAFM system informs and escalates around the contractual Service Level Agreements (SLAs). We may take a Priority 1 call, relating to a major fault on a particular piece of plant. The CAFM may be set up to provide a warning as we come close to the four-hour fix time limit, perhaps an escalation is sent to the contract manager with one hour to go so that they can jump on the situation.

In reality, we can be much smarter about this. The historical data can tell us that, of the last 10 times that this type of asset had this fault, the engineers have been unable to fix the problem in less than three hours. Being aware of this information significantly changes how we view the SLA as unless we start work on the problem within the first hour of the fault being reported we will most certainly fail to meet the target.

Similarly, a call is logged for a particular building on a Tuesday, and it’s raining which through a coincidental sequence of events has previously resulted in the attending engineer being unable to gain access to the premises. With this data to hand, the visit can be pre-empted, and the situation avoided. If a specific part or tool is often required when a particular task is reported the system can learn from this recurrent data and ensure that the attending engineer is provided with it before their visit, dramatically increasing our first fix performance.

For the future, my recommendation for facilities management is to proceed with caution. Across all industries, we have seen enormous artificial intelligence projects falter due to being unrealistic in their size and implementation as they were too broad in scope and definition. By being smart and focused with our data we can create great improvements In FM delivery and provide an enhanced level of service to our customers. Taking small measured steps, filtering out the unimportant data, and identifying value is fundamental in the delivery, and subsequent use, of successful data analytics.

About Sarah OBeirne

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