Anomaly Detection
Catching issues before they become problems can save time, money, and reputation. Anomaly detection is your AI-powered watchdog, constantly monitoring for the unexpected.
What is Anomaly Detection?
Anomaly detection continuously analyzes your project data in real-time and compares it against historical "normal" data to establish a baseline of expected behavior and identify deviations from this baseline.
This process involves:
- Data Ingestion: Collecting and preprocessing diverse data sources from your projects.
- Feature Extraction: Identifying key characteristics or "features" in the data that are most relevant for detecting anomalies.
- Model Application: Applying the trained AI model to new data to detect anomalies.
- Threshold Setting: Determining the level of deviation that constitutes an anomaly, often using statistical measures or machine learning-based decision boundaries.
- Alert Generation: Flagging detected anomalies and generating alerts for human review.
The AI system can detect both point anomalies (individual data points that are unusual, such as a sudden spike in material costs) and contextual anomalies (data points that are unusual in a specific context, such as a subcontractor's productivity rate that seems normal but is significantly lower compared to similar projects in the past).
These capabilities allow the system to identify a wide range of potential issues, from obvious discrepancies to subtle, context-dependent irregularities.
I mostly implement Anomaly Detection for these purposes:
1. Financial Irregularity Detection
Why?
- Spot unusual patterns in project financials to detect errors or fraud early
- Identify cost overruns or underruns early in the project lifecycle
- Detect unexpected changes in supplier pricing or subcontractor billing
2. Safety Data Analysis
Why?
- Identify unusual patterns in safety data
- Detect deviations from safety protocols in real-time
- Recognize equipment usage patterns that could lead to failures or accidents
3. Quality Control Enhancement
Why?
- Detect deviations from quality standards in construction processes
- Identify unusual patterns in material usage or performance
- Spot inconsistencies in inspection reports or quality assurance data
4. Schedule Optimization
Why?
- Identify unexpected delays or accelerations in project timelines
- Detect unusual patterns in task completion rates or resource utilization
- Spot potential bottlenecks or inefficiencies in project scheduling