Challenges in Estimating
Accurate estimating is mission critical but often hindered by:
- Inaccuracies: Human errors lead to costly overruns and lost profits.
- Time-Consuming Methods: Manual processes delay bid submissions.
- Underutilized Data: Past project insights are not effectively leveraged.
- Competitive Pressure: Slow estimates can result in missed opportunities.
How AI/ML Solutions Provide Value
1. Automate repetitive tasks to speed up estimating.
- Create a database of historical projects with productivity data.
- Identify the key drivers of productivity and cost for each type of project.
- Use this data to create a predictive model for future projects.
- Use this model to generate estimates for new projects.
Benefits:
- Increased Speed: Generate estimates faster for timely bid submissions.
- Enhanced Efficiency: Reduce manual data entry.
- Higher Bid Volume: Handle more projects without additional staff.
2. Use historical data to improve accuracy.
- Aggregate and clean historical project data.
- Identify key cost drivers and performance indicators.
- Develop machine learning models to predict costs and timelines.
- Implement a feedback loop to continuously improve predictions.
- Integrate risk assessment based on past project challenges.
Benefits:
- Data-Driven Estimates: Inform current projects with past data.
- Reduced Errors: Minimize miscalculations.
- Risk Mitigation: Anticipate potential overruns.
3. Produce precise estimates to win more bids.
- Train an AI model on historical project data.
- Use this model to generate estimates for new projects.
- Use this model to optimize bids for new projects.
Benefits:
- Better Bid Quality: Submit more compelling proposals.
- Increased Win Rates: Improve success with accurate estimates.
- Protected Margins: Avoid underbidding or overbidding.