Cost estimation plays a pivotal role in determining the success of any industrial project. In each step of the process, accurately forecasting necessary budgets ensures that projects remain on track and on schedule. However, many organizations still rely on historical benchmarks, overlooking the valuable data trapped in technical drawings and schematics. This leads to budget overruns, scope creep, and delayed project timelines.
Modern industrial projects often involve brownfield data, which seldom reflects the technical drawings fully, making manual cost estimation even more error prone. Without accurate data, estimators are forced to pad their costs with contingencies, leading to either inflated bids or underestimates that cause project budgets to balloon later on.
Why Traditional Cost Estimation Methods Fail
For decades, industrial sectors have relied on manual processes to estimate costs. These methods depend on human estimators who must painstakingly scour through thousands of pages of technical drawings, piping and instrumentation diagrams (P&IDs), or scanned PDFs. While experienced, these estimators are prone to fatigue, leading to errors that can significantly affect a project’s bottom line. The inability to validate data in real time or extract crucial information from technical drawings further exacerbates these challenges.
The Future of Cost Estimation
AI-powered solutions can transform the traditional methods of cost estimation by leveraging data directly from technical diagrams, improving accuracy, reducing time, and increasing project margins.
How Can We Help You?
HubHead and DataSeer’s AI Service combines human-level understanding with machine speed to build a scalable knowledge data store of engineering designs. By integrating these solutions with your existing EAM/CMMS systems and creating a digital twin, you can enhance decision-making and streamline your maintenance processes. Contact us for a free demo or book a call.
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