I have seen firsthand the risks associated with underbidding a public construction project. While it may be tempting to submit a low bid to win a contract, doing so can ultimately be detrimental to the success of your firm.
Underbidding a public construction project can have significant financial consequences for you firm. If the actual cost of the project ends up being higher than your bid amount, your firm may have to absorb those additional costs. This can lead to financial losses, which may impact the profitability of your firm and harm your reputation with clients and make it difficult to secure future contracts.
This can also result in a lack of resources to properly ensure safety on the job site. This can put your workers and the public at risk, potentially leading to legal action taken against your firm.
Fortunately, with the use of machine learning and predictive analytics, firms can accurately estimate the cost of a construction project and avoid the risks of underbidding. By analyzing historical data on similar projects, a machine learning platform can provide insights into the cost of labor, materials, and other expenses associated with a project. This allows firms to submit a more accurate bid, reducing the risk of financial losses and quality issues. Leveraging these systems can also drastically cut down the time to prepare a bid by as much as 95%, and eliminate any potential calculation errors.
Underbidding public construction projects can carry significant risks for general contracting firms, as well as municipalities and agencies putting these projects out to bid. However, with the use of machine learning and predictive analytics, firms can accurately estimate the cost of a project and avoid the risks associated with underbidding. By doing so, firms can improve their profitability, reputation, and overall success in the industry.