How analytics can drive smarter engineering and construction decisions
Three applications illustrate how companies
are beginning to embrace data-driven solutions while establishing a foundation
for future initiatives.
The construction business faces a major productivity challenge. While labor productivity in the
global economy has increased by an average of 2.8 percent a year over the past
two decades, and in manufacturing by an impressive 3.6 percent, the
construction sector has registered a mere 1 percent 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.
Admittedly, adopting
analytics tools may pose challenges for project-driven businesses in the
construction sector. Unlike manufacturers, for example, which tend to follow
predictable and repeatable processes, E&C firms face high variability.
Progress-tracking systems sometimes change mid-project, causing
incompatibilities and inconsistencies in the collected data. Parameters such as
scale, materials, and subcontractors involved also vary significantly from
project to project, making it difficult to establish benchmarks.
The cultures and
processes within E&C organizations can pose additional barriers. The industry
tends to put trust in individual experience and expertise over empirics, and
few companies have data analysts on staff who can take ownership of advanced
analytics initiatives.
In time, predictive
analytics, machine learning and artificial intelligence solutions will likely usher in bigger
changes to the ways E&C firms bid on and deliver projects. For now, three
applications illustrate how companies are beginning to embrace data solutions
while establishing a foundation for more ambitious initiatives in the future.
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. What’s more, bidders don’t know how
market shifts may affect their costs between the time of the bid and the
project’s start. 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 percent, underestimating a bid by 10 percent 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 percent.
Data modelling can
replace cognitive bias and flawed assumptions with fact-based insights about a
project’s statistical chances of success. By analyzing historical information
such as types of labor and contract arrangements, regional spending trends, and
project size, analytics can assess the probabilities of project outcomes.
Those, in turn, will 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.
One company, for
example, leveraged data from more than 100 of its past projects. It combined
internal data on project locations, asset classes, contract structures, and
profit margins with external information such as total spending in a given
sector or geography and statistics on local workforce size and unionization.
Analyzing these factors in aggregate, the company uncovered project
characteristics that influenced profit margins in ways that conventional
analysis could not illuminate. For example, while companies often look to
factors like region or project type to predict profitability, those variables
may be merely correlated with more influential factors such as contracting
strategies, craft unionization, or regional public sector budgets.
Using the insights from
this analysis, the organization developed a dashboard of risk variables that
could affect project margins. The system creates a scorecard that identifies
potential risks based on past patterns—for example, if the venture is in a
region with a history of low-margin projects, or if it entails working with a
public-sector owner with different requirements than typical private-sector
partners.
During pre-bid
meetings, teams rely on this information to help them decide whether the
project is sufficiently attractive to make a bid, estimate the costs, and
calibrate the size of the contingency to assign to the 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 analyzing 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.
One large US
infrastructure owner took the initial contracts from 17,000 past projects,
incorporated amendments and adjustments, and created a comprehensive database
of all final costs by work breakdown structure, both in time and materials. It
then built a multi-variate statistical model to determine the factors that
would most accurately predict final project costs, such as the likely number of
structural engineering hours required for a bridge replacement, or projected
materials cost for an additional lane along a four-mile strip of rural arterial
highway. The result is a procurement tool that benchmarks a project’s final
cost. When bids come in, managers immediately know if these are within the
expected range for that type of work. Today, leaders can gauge an accurate
price for procured contracts within an average of two days, down from an
average of 60 days often spent in labor-intensive negotiations.
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. For example, lagging crew productivity can often be recovered
through special planning activities; but late material delivery or multiple
days of adverse weather might exacerbate crew productivity losses and require a
different intervention from management.
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 analyze 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.
Industry leaders have
created an approach, statistically correlated with erosion of margins, to
monitor their project performance. On a daily basis, the analytics model
crunches the day’s project data and looks for these red flags; if enough of
them appear, management is alerted immediately to intervene before the problem
even materializes.
As we have written
elsewhere, 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.
Often, the greatest
hurdle to implementing such solutions is the one-time backward reconciliation
of data. Most firms have collected lots of information over the years, but it’s
stored in disparate systems and inconsistent formats. As such, the first step
should be to take stock of what they have—many companies will find they have a
lot more data than they realize, such as accounting records and purchase order
history—and put it into a form they can digitally analyze. This may be a
tedious and resource-intensive process, but it will set the foundation for more
sophisticated data collection and analytical techniques down the line. What’s
more, this one-time work will create a foundation for structuring data—into
data lakes, for example—that will make future analytics initiatives easier.
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.
By Garo Hovnanian, Kevin Kroll, and Erik
Sjödin
https://www.mckinsey.com/industries/capital-projects-and-infrastructure/our-insights/how-analytics-can-drive-smarter-engineering-and-construction-decisions?cid=other-eml-alt-mip-mck&hlkid=341ace5bba9249ceb060f71631aa1093&hctky=1627601&hdpid=bdd94ef2-43be-49a1-84b8-5091f0b8cf58
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