Large infrastructure projects are becoming more and more complex as the years go on due to a combination of new regulations, ever-changing requirements, and increased customer expectations.
With so many elements to contend with, it’s no wonder that 90% of large infrastructure projects go over budget, with each overrunning by an average of 28%. This shows just how much traditional planning methods are no longer working, creating added costs along the way.
Why Are So Many Projects Going Over Budget?
Large infrastructure projects have a lot of moving parts, so it can often feel challenging sticking to a tight budget. The trick to effectively managing finances is making sure that sufficient planning has been done before the physical work kicks off. Understanding the common causes of overruns helps make sure that they are avoided, so let’s discuss some of the key ones.
- Poor cost estimates. One of the biggest causes of budget overruns is making rough estimates instead of data-driven decisions. The better the initial cost estimate, the smoother the entire project will run since there will be fewer surprises along the way.
- Changing project needs. When working on a big scale, hiccups that were not accounted for are almost a certainty. However, the more the project requirements change, the more this is going to cause havoc on the budget. This is why having solid project foundations in place is a necessity.
- Labor shortages. Whether a team is dealing with unexpected staff absences or not enough manpower has been scheduled into the rota, labor shortages create delays that will inevitably increase costs. The costs of hiring external contractors or extending the project both quickly add up.
- Supply chain issues. Third-party issues may seem out of the control of the project manager, but plans need to be in place to ensure there is enough leeway to prepare for holdups. Otherwise, a team may be sat around wasting time as they wait for a late delivery.
What is Machine Learning?
Machine learning is a form of artificial intelligence that is trained to make informed decisions. This is done through analyzing large quantities of data to identify any noticeable trends and predict future outcomes.
In the context of infrastructure projects, this helps project managers make more accurate plans that go beyond the capabilities that humans on tight schedules would have. Machine learning is an effective way to gain deeper insights than would otherwise be possible, making it easier to create accurate financial decisions.
Its ability to combine historical project data with real-time project data gives a thorough insight that helps give the team a good understanding of the planned outcome and any potential risks that could potentially impact it.
This knowledge allows risk assessments to be put in place before the project kicks off, reducing the impact any issues that do arise could have on timings and budgets.
How Machine Learning Can Be Used in Infrastructure Projects
There are various ways machine learning can be implemented when carrying out large infrastructure projects, not only reducing the likelihood of budgeting mistakes, but also saving hours of time during the planning process.
Here are some of the most powerful ways machine learning can be used to improve the efficiency of a project:
Improve budgeting accuracy
One of the most notable differences the implementation of machine learning can have is helping a business accurately plan out its costs. Since it has the ability to pull through extensive data from previous projects and use this information alongside current market conditions, it can create reliable pricing points that give both the business and the client more realistic expectations.
Allocate resources effectively
Trying to plan the right amount of materials, allocate the equipment needed at each stage, and schedule workforce rotas accordingly offers a lot of room for error, and these errors can result in expensive holdups. Using machine learning to map out the resources needed throughout the project will take a huge weight off the project manager’s shoulders and make sure the team have the resources they need at the ready.
Monitor projects in real time
Once work is underway, it’s important that it is closely monitored to make sure that the deadlines are being met. Machine learning continuously monitors the progress of the project against the planned budget and timeline, flagging any significant deviations so they can be addressed early on.
The Key to Carrying Out Projects Within Budget
As large infrastructure projects become harder to manage, the help of machine learning can completely transform the process for project teams.
Being able to get personalized insights based on a combination of historical data and real-time market conditions makes sure that any decisions made are done so with the knowledge needed to avoid complete guesswork.
Kicking off a project with informed choices and accurate budgets sets a much stronger foundation than many businesses have been using, creating a smoother process throughout and a stronger final result.
