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Future Technology: How to eat an elephant

“Understanding and ‘doing’ asset management can seem like eating an elephant,” says EC Harris head of highways Brian Fitzpatrick.

“For many of our clients, seeing and understanding the linkages, dependencies and real efficiencies that exist all the way from the boardroom or council chamber through to actual maintenance activities is too big initially to appreciate as a whole, so maybe the only way to start to approach it is in bite-sized chunks.”

Clearly Fitzpatrick thinks big data will have a role to play in how we manage our infrastructure assets in the future – or rather, how we eat the elephant – but only if asset managers and service providers get their organisational frameworks in order first. He thinks the most common mistake client organisations can make is to “charge after the technology” before they are equipped or well organised enough to deal with what it tells them.

“If you’re not organised to respond to data, there’s not much point in capturing it at the moment,” he says. “There are a lot of disparate ‘asset management systems’ out there, and different organisations are at different stages in terms of their capability maturity and ability to understand and drive insight from the data they collect.

motorway at night

Motorways: Big data can increasingly be marshalled to optimise the efficiency of maintenance programme

“Some are a lot further down the route than others in aligning their organisational practices with standard capability methodologies and asset management-specific guidance such as PAS55 or ISO55000.”

“Ideally an effective asset management system needs to work like a machine”

Brian Fitzpatrick, EC Harris

He cites the Highways Agency and Transport for London (TfL) as examples of organisations that are working hardest to develop this organisational maturity. Both are conducting assessments to develop their ability to act on and respond to data and, in turn, better inform their delivery partners.

“Ideally an effective asset management system needs to work like a machine: there are a lot of moving parts but all of the rules and regulations, all of the performers, all of the roles and all of the processes are clearly understood, as is the context within which people are working” says Fitzpatrick.

Transport for London (TfL) and the Highways Agency are large organisations which have a big spend and can afford to invest in preparing for change and the challenges that big data can represent, but together they account for less than 5% of the roads network in England alone. What about all of the other highways authorities looking after the rest of the network?

Fitzpatrick has a dynamic vision of how big data will improve their approach to managing assets in the public sector if they too can make the critical changes that they need to make around how they are organised. At a primary “hard” level, it is about using data to make a more informed decision about when an asset might be repaired or replaced, and understanding life cycle costs and expenditure.

EC Harris has already developed what Fitzpatrick describes as a “cross asset optimisation tool” (see box out) that is designed to help asset managers make primary interventions, but the plan moving forward is for even more information to be included in the model.

“The reason why they haven’t been developed before is because it’s very difficult, there’s a huge set of complex interdependencies and arrangements in the model,” he says. “To understand not just the benefits and the value returns from those schemes is very difficult, but if you can do it, why can’t you then start to model some ‘soft’ elements using the same platform?”

Fitzpatrick explains that these “soft” elements include less tangible judgements about assets. “What’s equally interesting is to understand the true value of the socio-economic benefits of investment in transport and maintenance: the impacts of, say, street lighting on footfall in an urban environment, the impact of street lighting on crime.

“Within the next five years we should see some very visual, real time information coming back that will inform asset management decisions”

Brian Fitzpatrick, EC Harris

“Should we be keeping green spaces open or shutting them at night?” he says.

Admittedly these might be more difficult to model, but the task is not impossible. For instance, TfL’s roads taskforce recently made a matrix of most of the streets in London, assessing not just their functionality but also their liveability.

“They then ranked these streets so they can understand that they’re not just going to invest in a street because it’s a red route but because it has an important social component that they need to recognise.

“If you can start to use technology and good computers to model the soft and hard outcomes, then you start to get an approach to wellengineered public realm, as well as well-engineered infrastructure.”

For Fitzpatrick, it’s a vision of the future that closely mirrors science fiction. “It’s almost like ‘Minority Report’ [a film where criminals are arrested before a predicted crime takes place] coming true,” he says. “You’ve got a ‘graphic equaliser’ and a platform that can enable you to adjust your investments and interventions according to the areas of most need, highest risk and best return.”

Creating such a model will inevitably involve new skill sets and engineering could have greater need for those of a mathematical bent in the future.

A graphic equaliser for asset management

Our asset optimisation platform can take inputs from other ‘asset management’ systems, and not just prioritise or sequence schemes, but compare and optimise the outcome of as many investment/intervention scenarios as the client can think of or can afford,” explains Fitzpatrick.

“These outcomes then have implications for how the work will be delivered, which gets the client back to making sure they are organised properly”.

Fitzpatrick says he was partly inspired to develop the approach in former roles as the head of highways and transportation in a central London borough, and as an elected councillor in a Kent local authority.

“I was presented with, or had to draft, annualised programmes and specific schemes for the year which were not evidence-led in many cases, and the benefits of different schemes were arguably hugely subjective. I wanted to develop the concept of a “graphic equaliser” that allowed engineers to utilise their existing data, bring it into one place, and visibly test the trade-off between their investment and scheme choices. Such a tool should also incorporate best practice principles such as the Highways Maintenance Efficiency Programme (HMEP), and conform to Whole of Government Accounting Rules.”

More importantly the tool would evidence engineers’ choices of schemes to citizens and councillors, demonstrating the benefits able to be achieved. It can also help explain the reasons why hard choices must be made and some schemes weren’t able to proceed.

He continues: “There is potentially a lot of data to bring together, and a big range of complex interdependencies and arrangements to model. We realised though that it was less a roads engineering problem than a maths and computing problem”.

After realising this, EC Harris has been able to test and continues to develop its approach with a range of public and private sector clients. 

“We’ve got some very smart maths around it and different computational techniques,” he explains.

“It’s not new but it’s a slightly different lens and a slightly different perspective – we’re talking to a leading British university and some others to almost accelerate it into the market in the next six months.”

So how likely is it that the market will trust and adopt the technology?

“We have already advanced quite a bit and we are talking to people about the deployment of our platform but would stress it’s a decision support tool, it’s not a decision making tool,” says Fitzpatrick. “That’s all about confidence and the accuracy of the data. But I think within the next five years we should see some very visual, real time information coming back that will inform asset management decisions. In 10 years I think it will be a reality.” 

Produced in association with EC Harris.

 

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