Construction is a fast-paced industry that requires constant innovation to stay ahead. Predictive analytics can help construction businesses improve their operational efficiency, drive business growth, and improve their competitiveness.
Predictive analytics are designed to provide construction professionals with the ability to collect, organise and analyse project data in order to gain insights into how projects are progressing and identify areas for improvement. The software enables construction teams to access project information from anywhere, giving them visibility into current status and upcoming milestones. Handy features such as task lists, scheduling tools and document sharing make predictive construction management software a vital asset in any construction project.
In this article, we’ll explore where predictive analytics can be used in construction projects, how it works, and why it’s useful for businesses today.
Challenges facing construction managers today
The primary challenges that construction managers are facing today include:
- Finding a way to keep up with the pace of change in the industry. The pace at which technology and innovation are changing has never been faster, leading to more frequent changes in project schedules and budgets.
- Having limited access to data analysis tools that can help them make informed decisions about their projects’ progress and costs. Most companies lack the tools needed for advanced analytics—such as advanced predictive models—and must rely on manual processes instead. This limits their ability to make timely decisions around scheduling or cost reductions while also creating unnecessary administrative tasks and delays during the planning stages of any given project.
Predictive analytics in construction – the need of the hour
Predictive analytics is a tool that helps businesses improve operational efficiency by predicting how they will perform in the future. Predictive analytics can be used to identify patterns, predict future outcomes, and make better decisions.
It has become a popular tool for many industries worldwide because it provides actionable insights for improved decision-making, informed predictions about customer behaviour, better project planning, and cost reduction.
Some of the common benefits of predictive analytics for construction project leaders are in:
- Forecasting – By using predictive modelling techniques such as machine learning (ML), artificial intelligence (AI), and deep learning algorithms, you can accurately predict when projects will finish earlier or later than expected or have increased productivity due to reduced waiting time between phases of work on site. This allows you to schedule resources more efficiently which saves money on labour costs, therefore increasing profitability both now and later down line.
- Decision-making – Predictive analytics can be used to identify patterns, predict future outcomes, and make better decisions. For example, predicting the best time to start a new project based on historical information about similar projects. This helps with the planning and scheduling of resources which can save money.
- Project planning – Predictive analytics can be used to identify patterns and make better decisions about project tasks and resource scheduling.
Barriers to using predictive analytics in construction projects
While there are many benefits to using predictive analytics and data science in construction projects, there are also barriers that prevent managers from fully adopting the technology. Perhaps the biggest challenge facing construction managers is that they don’t have a clear understanding of how predictive analytics can help them. Another important barrier is the availability of good quality data for building predictive models.
- Data availability: If there is not enough data available, predictive models will not work as well. This can be a problem if you are trying to predict the likelihood of a project going over budget or being completed on time, which requires knowing more about the project than what’s already publicly available.
- Data quality: If your data is not accurate enough, then any predictions made from it will also be inaccurate. For example, if your model says that a project will cost $15 million but you discover that it costs a total of $9 million, then your model has failed and you need to go back and adjust it until it starts making accurate predictions again.
- Data scalability/scalability of models themselves (i.e., how easy it is for people to build them). This is often overlooked when people are starting with predictive analytics but then once they get started they realise how hard it is to make these things scale up (or how long building them takes).
Digital project management software platforms like PlanRadar allow you to collect real-time data and identify insights in your construction projects. Using this platform, you can streamline project management, collaborate with your team, and collect quality data that provide insights about your construction projects in real-time. To learn more about how PlanRadar helps you enable predictive analytics in your construction projects, you can try the app for free or contact us here.
Predictive models for process optimisation and streamlining
Predictive analytics can be used to improve the operational efficiency of your construction business. Here are some examples of processes that can be improved using predictive analytics:
- The planning, scheduling, and execution of a construction project. For example, it is possible to predict when a project will finish on time and within budget by analysing historical data from similar projects in your area. This information allows you to adjust staffing levels accordingly so that resources are always available when needed (for example, if there’s an unexpected delay in one phase).
- The tracking and reporting of daily operations throughout the life cycle of a job so that everyone involved knows where they stand at any given moment – this helps avoid costly mistakes down the line!
- The analysis of warranty claims to identify patterns and trends that may be responsible for defects or unexpected failures (for example, using your database you can determine which contractors tend to have more problems than others). This information can then be shared with your colleagues so they know who to avoid in the future.
Conclusion
Predictive analytics are critical for the success of construction projects. They allow construction project leaders to make informed decisions about where to focus efforts, who to hire, and how to utilise their resources. With predictive analytics, construction project managers can develop a plan that will help them optimise their project’s efficiency, minimise cost overruns, and increase profitability.
Overall, construction management software provides construction teams with the tools and insights needed for successful project completion. By giving construction professionals an easy way to access and analyse data, construction management software helps streamline operations, improve project outcomes and reduce costs. With construction management software, construction projects can be completed on time, within budget and with greater efficiency.
Interested in using digital construction management software to enable predictive analytics in your next construction project? Start your 30-day free PlanRadar trial here.
