Due to the nature of construction projects, it is common for them to generate huge amounts of data.
Historically, a lot of that information was kept, stored away, and then never used again. This meant that no matter what happened in the past, new projects were treated as completely new.
This lack of reflection often meant data that could have made forecasts more accurate or information that could help avoid common issues was missed, and more money and time were wasted.
Many organizations are trying to make use of predictive capabilities that are included with a construction management tool to improve their planning and decision-making throughout projects.
But what exactly is predictive analytics, and how is it changing construction project management?
The Facts of Predictive Analytics
For a long time, construction management relied on manual forecasting that relied on managers’ experience for its accuracy. Although many companies made a point of collecting data and using it to vaguely direct decisions, most of the useful information was ignored in favor of current project status only.
Predictive analytics takes historical data and, using statistical models and occasionally machine learning, tries to make it more useful. This style of Analytics tries to identify consistent patterns and tries to estimate what is likely to happen before the warning signs start.
Although many people assume predictive analytics is only used during the initial forecast, it is actually implemented throughout the entire lifecycle of a project, and so helps keep management thinking about what is coming next.
This helps teams move from backward-focused thinking to a forward-facing, proactive mode of management, and there are multiple ways this can help.
Scheduling forecasting
Traditionally, project timelines have relied heavily on managers’ assumptions and have limited flexibility in their planning. This has meant that when things don’t go to these plans, the budget might be exceeded and deadlines missed.
Schedules can fast become obsolete, as situations change and unforeseen factors begin to have effects. It can be difficult to correct schedules efficiently, and small mistakes can cause big problems.
Predictive analytics helps by examining previous project performances and helping highlight patterns that are frequently linked to delays. As well as this, schedule risks are identified early as the analytics happen on an ongoing basis, rather than just at the start.
This keeps project timelines more realistic when the initial forecasts happen, and helps limit delays as the project continues. A well-managed schedule keeps the team productive, and meeting the targets keeps the clients happy.
Identifying Budget Risks Earlier
One of the biggest burdens on the project management team is making sure things stay on target, from money to time to performance. Targets and goals must be met, or it puts future projects at risk.
Cost overruns can be tough to track and to catch. It is common for overruns to only become visible when it’s too late. At this stage, fixing it becomes harder and potentially more costly.
Companies can monitor spending patterns to help identify emerging cost pressures as they are happening. Predictive analytics keeps managers on the front foot, and assists in forecasting potential overruns while they might still be avoided.
Supporting Resource Planning
In the past, one of the hardest parts of forecasting, outside of budget and timeline, was the things that got the job done. Labor, equipment, and material all require forecasting, and due to the outside factors that can affect these, many construction companies run into trouble.
Problems in the field can slow the project down, and due to the reliance on outside resources, there’s no guarantee the fix will be quick.
Predictive analytics helps by shifting focus to trying to predict future resource requirements and helps teams identify shortages before they occur. By knowing what might be at risk beforehand, allocation decisions become more accurate, and this saves time and money.
Strengthening Risk Management
Project managers find that they must react to risks after they have appeared. Although this is a key strength of any management team, it can be a heavy burden on them, and a risk for the company.
When reacting to the issues, decisions need to be made faster, and this means that the fastest solution might be used instead of the best.
By analyzing the data that companies take in, indicators of the risk can be highlighted before they even happen. Patterns that emerged in previous project issues can be pointed out faster. This clarity and speed mean that managers can be proactive in their planning.
The Final Thought
Although the benefits of predictive analytics are clear, it’s important to understand the limitations behind it.
Predictions that the model provides are only as useful as the information that the analytics are done with. Meaning that if a company doesn’t keep data well, the predictions will also be affected.
Analytics are a good support for the management, but it is also important that project management has the trust to take control and make decisions when it’s needed.
Predictive analytics help give better forecasting and assist with resource planning, as well as boosting risk visibility, and can help give your teams the best chance at making informed decisions before the issues escalate.