Statistics from the 2015 KPMG Global Construction Survey says that:
“60% of organizations that spent more than $10 million on capital construction projects reported that at least one project failed or underperformed.”
If you read more into statistics related to big construction projects and what causes them to be unsuccessful, you will find that there are some common factors in most of these failing projects. What are these common factors apart from the fact that they all failed?
Lack of proper planning
Poor risk management
Poor equipment management
The answer to all these recurring issues can be Big Data and Analytics. How? Let us elaborate.
1. On Site Process Efficiency
Big data can improve the efficiency of a jobsite manyfold, if contractors used it to better the workflow, to cut costs and provide automation in the tasks by collecting the data from their sites. It's not all about the commercial cost, big data analytics can help you track the whereabouts of your workers. You can track how much work is accomplished through smart gadgets, wearables or smartphones they carry.
In one of such cases a contractor used this information to replace the material to a location, which was closer and most accessible by the workers and saved time by reducing all the extra movement.
The contribution of big data doesn't end with a project. It extends to the post completion phase as well. By providing the data insight for future maintenance and structural details to the crew for easy renovation. There are cases where construction crews have installed sensors to monitor structural developments in finished buildings. The data provided by these sensors actually helps in understanding the future outcomes of the measures taken and to ensure a secure culmination of construction projects.
2. Integration of data from multiple sources
If you are a big business, you would be well accustomed to the amount of unstructured data collected on a daily basis. What is unstructured data? It is the daily amount of emails, paperwork, electronic work, images, audio files and video files. Things you need to store safely to analyze your business procedures. It is not only tough but also expensive to find the right people who can help you get value from this unstructured data. Data analysts and scientists who can help you not only capture the data but also clean and integrate it.
Established organizations like Tesla are not only one of the popular companies promoting the use of big data but they also provide big data solutions. Tesla started as a company that recorded all the data related to cars and its buyers, then analyzed the use of these products. Setting the precedent for other streams like the construction industry and its leaders such as Aecom and Bechtel to follow suit.
Big data and real time data was nonexistent for the construction industry a few years ago. But now, it is one of the biggest investments for all industries alike. It is also the next step towards sustainability efforts for the earth, as most of the environmental issues caused by the construction industry can be tackled by data.
3. Making predictions with data
Big data has shown to reduce risks associated with construction projects (if applied from the planning phase of the project). For instance, it can detect the productivity level of the labor and equipment being used and by doing so it can inform the contractors of any delays, energy levels (such as point of fatigue) or simply having too much equipment on the site.
Having too many people working on one project not only makes the environment overcrowded but also poses safety issues. Having the leg space to move around for the workers and machinery can actually accelerate the productivity levels.
Construction waste is predicted to reach in billion tons in the coming years and most of it comes from wrong calculations. By reducing this waste not only big data is helping the economy but it is also saving the earth in the process. According to Forbes, 35% of material cost is spent on wasted material and correction errors. Big data helps you save cost by predicting accurate figures for your investments in material. Hence increasing your profits in the process.
Big data can correspond engineering plans with architectural plans and predict any mishaps before they can happen. Prevention becomes easier than the cure, as site managers and industry leaders can solve problems before they arise.
4. Improved infrastructure development
Building Information Modeling (BIM) data is a revolutionary new invention for the construction industry. Unlike Computer Aided design (CAD) where most drawings are not only done in 2D but are also flat and volumeless, BIM provides the reality of the structure. This is the latest way to digitize the real world which is far more superior in quality and predictions. It is a breakthrough for analytical and application purposes.
Here is a quick look at how BIM is better than CAD:
CAD vs BIM
Status quo Change of thinking
Necessity of printing Electronic communication
Manual work Automated
Analogous processing Digital processing
Slow work Fast work
Disconnected part Fully integrated
If this wasn't an enough upgrade in itself, the BMI companies are already integrating with AI, in trying to improve the potential of their programs and designs. BIM collects tons of data and AI uses it to explore possibilities on how to predict the best solutions. Completely diminishing the possibility of human error and in the process saving substantial amounts of time.
5. Reducing waste.
Construction and engineering industry was accountable to 39% of the total carbon dioxide emissions in the year 2018. With such mind numbing insights the recent shift to lean construction was made possible mainly due to big data analytics and their ability to provide the construction teams real time information, which helps them use the material, plant and their equipment consciously and efficiently.
Human existence and their efforts to strive on this planet has affected the earth greatly. Eliminating the use of paper, improving human resources and reducing the amount of waste wrong predictions can make in the construction industry, eventually it has come down to data being a far safer choice for the environment as well.