How analytics can drive smarter Engineering and Construction decisions
Three applications illustrate how companies are beginning to embrace data solutions while establishing a foundation for more ambitious initiatives in the future.
The construction business faces a major productivity challenge. While labour productivity in the global economy has increased by an average of 2.8 per cent a year over the past two decades, and in manufacturing by an impressive 3.6 per cent, the construction sector has registered a mere 1 per cent annual improvement. As the capital-project partners responsible for execution, engineering and construction (E&C) firms are well positioned to drive changes that can help close this troubling gap.
To do so, some are turning to data-driven solutions that have already revolutionized many other corners of the economy. These techniques are emerging as vital tools for improving capital project outcomes and reducing risk. By enabling E&C companies to leverage the vast amounts of data they already collect, analytics can uncover critical insights that both speed up and improve the quality of management decisions. In particular, they can help project teams assess market conditions, portfolio composition, and individual project performance.
1. Should we bid on this project, and if so, how much?
Usually, E&C firms must decide whether to bid on a project based on incomplete information. Major construction projects often have a five- or 10-year timeline, if not longer, which makes it difficult to accurately define the scope and predict likely complexities or complications up front. Companies rely on staff experience to weigh potential risks and profitability, but those judgments are subject to inherent biases and may be affected by ambitious growth targets or individual incentives.
Misjudging risks and underestimating costs can prove disastrous. In a business with typical margins of 5 to 7 per cent, underestimating a bid by 10 per cent without the ability to recover the extra costs can make the project an expensive money-loser for the E&C firm. Conversely, overpricing a project by building in too big a contingency cushion will likely mean the loss of the contract—something a firm can ill afford in an industry with win rates of merely 15 to 25 per cent.
Data modelling can replace cognitive bias and flawed assumptions with fact-based insights about a project’s statistical chances of success. By analysing historical information such as types of labour and contract arrangements, regional spending trends, and project size, analytics can assess the probabilities of project outcomes. Those, in turn, enable teams to better evaluate the attractiveness of a given project, re-balance the portfolio away from jobs that tend to underperform and calculate the right level of contingency to include in a bid.
2. Are the subcontractor bids reasonable?
When E&C firms receive bids from subcontractors, they turn to procurement specialists to assess the quotes. These individuals often rely on parametric estimates to evaluate the quoted costs and tap the expertise of project managers, slowing down the process. Complex estimates pass through multiple reviewers, with each one adjusting the estimate based on his or her own experience and judgement (as well as potential bias).
Despite these extensive consultations, the lack of an empirical foundation makes it hard for engineering companies to credibly challenge a subcontractor’s estimates beyond relying on generalized rules of thumb. In addition, while many companies maintain (and subscribe to) databases of parametric cost factors for bidding, they rarely follow up with the actual costs at the end of their projects to gauge the accuracy of those estimates.
Analytics can provide a solution to these problems. By analysing individual drivers of past project costs, such tools can enable E&C companies to rapidly assess a realistic level of effort and cost for a project and compare those figures to subcontractor quotes.
3. Is the project about to run into trouble?
Traditional project controls often lag the incurrence of costs by days or weeks, which makes them an effective tool for retrospective reporting but not for managing ongoing projects. The controls also don’t account for the interconnectivity of different metrics and the unique combinations that may have outsized effects on performance.
Unable to continually track and grapple with all the data a project generates, managers tend to follow a few key performance indicators. The resulting incomplete picture of the project’s daily progress can lead to flawed decisions on the ground.
Analytical tools can deliver a significant improvement on this front by allowing companies to quickly and continuously analyse project data and assess progress, enabling managers to react faster to potential problems. With real-time or near-real-time project controls in place, an E&C firm can track events or problems known to correlate with the erosion of bid margins, such as a one-day weather delay or three consecutive days of a subcontractor’s failure to complete designated tasks.
Engineering and construction firms wishing to prepare for the digital age will need to establish a new operating model. Such a shift requires treating digital initiatives as part of the core strategy, adapting processes and organizational structures, and ensuring staff have the necessary training to deploy, troubleshoot, and lead digital initiatives. But the first step in such transformations is applying analytics to assess current operations and performance.
Companies also need to establish standards for the data they collect in the future. Whether it’s a full-fledged data management system or simply a standard way of tagging and collecting information, standards for what you want to collect and how you collect it are critical to a long-term analytics strategy.
As digitization penetrates all parts of the economy, including engineering and construction, capitalizing on the insights hidden in data will become essential. E&C companies reluctant to invest in the systems and skills needed to harness what they have collected should remember that competitors who have successfully made the move are already reaping significant benefits. Firms that embrace analytics can make sharper bids, thus avoiding unprofitable projects and increasing their win rates on those with strong margin potential. They conduct savvier negotiations with subcontractors, reducing costs and increasing decision speed. And they anticipate problems with ongoing projects, allowing managers to intervene before potential delays and cost overruns turn into real ones. As the industry increasingly deploys these tools, the companies that get in early will likely emerge as leaders.