What can maintenance management learn from other sectors?

    Data forms the basis of scientific inquiry and has long been used to measure and benchmark. Maintenance management, however, has always been what could be described as ‘data poor’. A lack of historical asset and operational information, as well as the know-how to exploit the data it does possess, are just two of the reasons the industry struggles to quantify or articulate its value.

    Fortunately, this is where new and existing technologies can help, and an exploration of data analytics use in other industries provides us with some lessons on how data science can be used to our advantage.

    Using data to visualise needs in healthcare.

    Just as medicine and technology in healthcare have advanced, so too has the sector’s use of data. The introduction of diagnostic equipment such as MRI scanners, PET scanners, ECG sensors, and radiography systems has revolutionised prevention and care. Using the visualised data that these diagnostics generate, doctors can diagnose everything from fractures to rare diseases.

    Sometimes it is as simple as piece of string. In the late ’90s, the CEO of a Seattle hospital, Dr Gary Kaplan, was looking for a management system to save his failing facility. Kaplan couldn’t find the processes and procedures he was looking for in other hospitals, so he eventually turned to Japanese car manufacturer Toyota. The initial response from the hospital staff was frosty. But that changed when a ‘sensei’ from the Toyota factory used a ball of blue string on a hospital map to trace the typical journey of cancer patients from their ward to the chemotherapy unit. The sight of blue yarn snaking around the map proved enlightening, enabling practitioners to understand for the first time just how needlessly complex the patient journey had become – and how much time the hospital was wasting.

    “We couldn’t conceive of it intellectually until we saw it visually.”

    Senior nurse at Seattle hospital

    The changes the hospital made in response to their learnings resulted in a 37% decrease in the organisation’s insurance expenses, and the number of patients the hospital was able to treat increased significantly.

    Instead of blue yarn, the healthcare sector now uses nodes, signals and networks to create complex data sets that inform evidence-based decision-making. In 2015, the Carter Review, an independent study into the operational productivity and performance of acute NHS hospitals in England, determined that the NHS could save up to £5 billion by making better use of clinical staff, reducing agency spend and absenteeism, and adopting better management practices. The study recommended using cost and output metrics to create a ‘model’ hospital that would give trusts “a single version of the truth on what good looks like from board to ward”.

    Since then, the NHS has begun to use data to inform and make recommendations in areas that are much closer to facilities managers’ purview: its real estate strategy. And in 2017, the independent Naylor Review used NHS performance and property data to make a series of recommendations, including the disposal of assets worth £2 billion.

    UT blog CTA MOFU mobile

    UT data scientist Blog CTA MOFU@2x

    Succeeding through achieving marginal gains.

    In recent years, data has also transformed performance and competition in a huge range of sports, from track and field to Formula One. Popularised by British cycling coach Sir Dave Brailsford (and the consequential success of the British Olympic cycling team), the world is now obsessed with the concept of marginal gains: the process of breaking something down into its constituent parts and making incremental improvements in each of these areas. Together, these small changes add up to create significant improvements overall. Brailsford was inspired by the same Toyota production system that changed the fortunes of the Seattle hospital. Looking to ‘kaizen’, a Japanese word meaning continuous improvement, the British coach urged his team think small, not big, and focus on improving each element by 1% – from the aerodynamics of the bikes and maintenance efficiency to the athletes’ health, wellbeing and food preparation.

    In more hi-tech arenas, the margins between success and failure are even finer. Formula One races – and even championships – can be decided by a thousandth of a second. Looking for every opportunity to save even the smallest fraction of a second, F1 teams monitor data from the car, the driver, the track, weather conditions, competitors, tyres, fuel… whatever can possibly impact on the race result. Today’s F1 cars represent the pinnacle of modern automotive engineering, and are fitted with as many as 200 sensors producing around three terabytes of data in a single two-hour race (that’s broadly the equivalent of 600,000 music tracks or over 500 movies).

    Marginal gains are important elsewhere, too. The competitive world of high-frequency trading is driven by complex algorithms and phenomenally fast data transfer, and competition has moved from traditional finance centres, such as the City of London and Wall Street, to data centres in remote and secretive locations. Sometimes this means laying the shortest fibre optic cable so that rival companies can be beaten by milliseconds.

    Use what you have.

    “The biggest reason that investments in big data fail to pay off, though, is that most companies don’t do a good job with the information they already have.”
    – Harvard Business Review

    So, what can facilities management learn from the way other industries and sections of society use data?

    Data is often linked to the concept of innovation and the introduction of new technology. But the truth is that however much data you gather, it’s only as good as what you do with it. A facilities manager’s use of data, big or small, needs to be pragmatic as well as strategic, identifying those incremental gains that add up to making a huge difference.

    From string to sensors, whatever technology you use to gather data, it’s your reporting, analysis and response that will drive both continuous improvement and step changes in business performance.

    Like everything, it’s what you do with it that counts.