Data-driven programme management offices can reduce workloads and ensure better data integrity and greater predictability of outcomes, according to Lisa Silander.
Humans can be creatures of habit. In the construction industry that can mean heading straight into projects without spending enough time thinking and planning how best to deliver these increasingly complex programmes. In doing this, we end up following the same processes and procedures and don’t take lessons learnt forward – usually due to time pressures on delivery – rather than looking for new and better ways of doing things. But what happens if we harness, capture and use the data we have?
It is highly publicised that construction industry productivity is low, and our resource pool is getting smaller. A step change is therefore needed to take our industry forward and improve deliverability. By carrying forward acquired knowledge, through data that provides us with insights and predictability, we can make positive changes for the future.
With the help of data analytics and using a digital, centralised programme management office (PMO), we can now generate quality data to enable timely decision-making, based on real project information and with far less resource required to prepare it. This allows teams to take a more proactive approach to data, acting on insight and driving the programme or project to successful completion. Bringing all relevant project data together in one single environment ensures complete transparency and promotes cross-team collaboration, as well as making sure that any experience gained is captured, and changes actioned, throughout the duration of programme.
‘With data analytics and using a digital, centralised programme management office, we can now generate quality data to enable timely decision-making’
In the long term, harnessing data from previous projects and programmes over time will allow us to give greater predictability to future ones, improving our outcomes.
How it works
Data is a by-product of all our projects and programmes of work, but it’s also a powerful tool that can aid us in working more efficiently – not just on one single programme but on all future programmes. However, in order for us to get the best value out of it, we need to start sharing data across industries and programmes of work – anonymised, of course, using key structures and words to capture and group data.
In this way, we can gain a better understanding of the issues that see too many programmes of work over-spending and over-running. The more data we have, the smarter our digital tools become and the greater the impact we can have on the delivery of future programmes of work.
SNC-Lavalin has developed a PMO analytics control centre that has already shown reduced workloads and better data integrity, as well as greater predictability of outcomes. With data stored in one environment, there is no double handling of information and, through connectors to industry software, integrated information can be seen in real-time by all, creating efficiencies in reporting and analytics. Thanks to machine learning, reliability also improves as pinch points and possible future issues are identified, so we can stop making the same mistakes repeatedly.
Greater transparency is another key benefit of data-driven PMO because all parties working on a project have access to the same, easily accessible information, reducing siloed working. This standardised approach also makes the data easier to digest and provides continuity of information. Because programme teams aren’t reliant on certain people for their information, there can be no conflicting data from different sources.
Encouraging adoption of better data practices
‘We need to foster more trust in our data and the best way to do that is through proof of concept and demonstrating value’
In order to encourage those in our industry to begin working in a more data-driven way, we need to foster more trust in our data and the best way to do that is through proof of concept and demonstrating value. To that end, we are currently working with historical data from past projects, inputting it into our digital PMO platform to see what we would have learned if we had used the tool while the project was underway. The results show that 85% of issues would have been identified and could have been addressed using machine learning outputs.
We are also employing, centralised PMO analytics control centres on live projects, including a new island resort build in the Middle East, nuclear and land regeneration projects in the UK, and look to use it in future in rail, highways and wind farm projects. These are all large, complex projects, but the tool is scalable and can be adopted on smaller, more straightforward projects that would benefit from it: for example, when there are multiple projects being delivered under a programme of works, such as estate management, with banks, large corporations, universities, and hospitals.
The fact is, if, as an industry we stand still and do not embrace more digital and data-driven ways of managing our workflow, we will not improve productivity. Construction and engineering projects and programmes are becoming increasingly complex, with growing pressure to be more efficient and improve delivery outcomes. Manual data handling requires an army of people to align and pull together – resources that we just do not have in the industry. The slow speed at which the data can be gathered and manually assured in this way also makes it of little real-time value and data patterns across programmes will rarely be picked up.
The technology is now freely available to help us better digest and understand our data and, most importantly, for us to learn and improve as we go. Let’s make sure we’re leveraging it to get the best results for ourselves and our clients.
Lisa Silander is UK technical leader project controls at Faithful + Gould.
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