Construction Analytics Speeding Up Timelines
E&C firms are frequently forced to decide whether or not to bid on a project based on insufficient information. Major construction projects typically take five to ten years to complete, if not more, making it difficult to precisely define the scope and anticipate potential complexity or complications upfront. Furthermore, bidders have no idea how market changes will affect their costs between the time of the offer and the start of the project. Companies rely on employee experience to assess possible risks and profits, but these assessments are vulnerable to inherent biases and can be influenced by aggressive growth plans or individual incentives.
Are the bids from subcontractors reasonable?
When E&C firms get bids from subcontractors, they enlist the help of procurement experts to evaluate the proposals. These people frequently use parametric estimates to evaluate stated prices and rely on project managers’ experience, slowing down the process. Complex estimates are reviewed by several people, each of whom makes adjustments based on their expertise and judgment (as well as potential bias).
Despite these thorough consultations, engineering firms find it difficult to contest a subcontractor’s estimates without resorting to general rules of thumb due to a lack of empirical support. Furthermore, while many organizations retain (and subscribe to) databases containing parametric cost components forbidding, they rarely compare actual prices at the end of projects to ensure accurate estimates.
Analytics may be able to help with these issues. Such technologies can help E&C organizations quickly determine a realistic amount of work and expense for a project and compare those figures to subcontractor quotes by evaluating individual drivers of historical project costs.
One significant US infrastructure owner compiled a comprehensive database of all final expenses per work breakdown structure, both in time and materials, incorporating modifications and adjustments using the initial contracts from 17,000 previous projects. It then developed a multi-variate statistical model to identify the factors that would most accurately predict final project costs, such as the number of structural engineering hours required for a bridge replacement or the estimated materials cost for an additional lane along a four-mile stretch of rural arterial highway. As a result, a procurement tool that benchmarks the eventual cost has been developed. Managers can tell right away if bids are within the expected range for that type of work when they receive them. Leaders may now determine appropriate pricing for procurement contracts in as little as two days, compared to the 60 days it used to take in labor-intensive negotiations.
‘Does the project appear to be in jeopardy?’
Traditional project controls are sometimes days or weeks behind the occurrence of expenses, making them a useful tool for retrospective reporting but not for managing ongoing projects. The rules also don’t consider the interconnectedness of many measures, as well as the unique combinations that might have disproportionately large effects on performance. For example, lagging crew productivity can typically be improved through special planning activities; however, late material deliveries or numerous days of inclement weather may worsen crew productivity losses, necessitating a different management intervention.
As stated before, engineering and construction organizations that want to prepare for the digital age will need to build a new operating model. This transition necessitates considering digital projects as a core strategy, adjusting procedures and organizational structures, and ensuring that employees are properly trained to deploy, troubleshoot, and lead digital initiatives. However, utilizing analytics to examine current operations and performance is the first step in such reforms.
The most common stumbling block to adopting such solutions is the one-time data backward reconciliation. Most businesses have amassed a large amount of data over time, but it is stored in various systems and forms. As a result, the first step should be to assess what they have—many businesses will discover they have far more data than they know, such as financial records and purchase order history—and convert it into a format that can be analyzed digitally. This may be time-consuming and resource-intensive, but it will lay the groundwork for future data collection and analysis techniques. Furthermore, this one-time effort will provide the groundwork for organizing data—for example, into data lakes—which will make future analytics endeavors easier.
Companies must also set guidelines for the data they will gather. Standards for what you want to pick and how you collect it are crucial to a long-term analytics plan, whether a full-fledged data management system or merely a common means of categorizing and ordering information.
As digitization spreads throughout the economy, including engineering and construction, capitalizing on data insights will become increasingly important. Companies in the E&C industry who are hesitant to invest in the systems and skills required to harness the data gathered should keep in mind that competitors who have made the transition successfully are already reaping major rewards. Analytics-driven companies can make more precise bids, eliminating unproductive projects and raising their win rates on initiatives with high profitability potential. They negotiate with subcontractors more effectively, lowering costs and speeding up decision-making. They also foresee issues with active projects, allowing management to intervene before predicted delays or cost overruns become actual. Companies that get in early on these technologies will likely emerge as leaders as the industry adopts them more widely. In the present time, innovative construction software is saving money.