Executive Summary
The construction industry suffers from persistent unpredictability in safety, environmental compliance, and quality assurance. While equipment failures are commonly addressed through reliability metrics like Mean Time Between Failures (MTBF), there is no similarly integrated framework for predicting human- and process-driven incidents. This white paper introduces Mean Time Between Incidents (MTBI) as a unifying metric for anticipating and preventing incidents across safety, environmental, and quality domains. MTBI empowers construction companies to transition from reactive compliance to predictive control, ultimately reducing rework, injury rates, and regulatory violations.
Background
Construction professionals are familiar with MTBF as a measure of reliability for machines and systems. MTBF allows planners to anticipate maintenance schedules and proactively reduce downtime. However, incidents caused by human error, field conditions, or procedural failure—such as a safety accident, environmental spill, or failed quality inspection—lack an equivalent predictive structure.
Safety metrics (e.g., TRIR, LTIR), environmental records (e.g., spill counts, non-compliance days), and quality defect logs are reported reactively. These metrics are often siloed, domain-specific, and detached from predictive planning workflows. The industry lacks a unified, reliability-centered metric to manage field incidents holistically.
The consequences of these incidents go far beyond documentation. Safety events can result in injury, lost workdays, legal exposure, and insurance rate increases. Environmental violations may lead to fines, remediation costs, and reputational damage. Quality failures routinely drive rework, material waste, schedule delays, and productivity loss. Each type of incident imposes measurable financial and operational risk—and yet, many organizations struggle to quantify or predict them proactively.
MTBI fills this gap by offering a structured way to assess how often projects are disrupted by failure events. It provides visibility into which crews, locations, or scopes are driving risk, enabling earlier intervention. In doing so, MTBI supports a shift from reacting to incident reports toward minimizing their occurrence and cost in the first place.
Defining MTBI
Mean Time Between Incidents (MTBI) is defined as:
\[ \text{MTBI} = \frac{\text{Total Operational Time}}{\text{Number of Incidents}} \]Where:
Operational Time is measured in workdays, man-hours, or shifts.
Incidents may include any field failure event such as:
- Safety: injury, near miss, stop-work
- Environmental: permit violation, uncontrolled release, regulatory notice
- Quality: inspection failure, rework order, missed specification
MTBI is inherently based on historical incident data and therefore starts as a lagging indicator. However, this historical foundation is precisely what gives it power—it reflects the real-world cadence of disruptions, allowing superintendents and project managers to identify reliability patterns over time.
Much like MTBF helps maintenance teams plan for equipment failure, MTBI helps construction leaders understand how frequently incidents interrupt operations. This enables teams to spot chronic issues, compare reliability between work crews or subcontractors, and quantify the cost of operational disruptions across safety, quality, and environmental dimensions.
Once established, MTBI acts as a temporal reliability signal—revealing how consistently a project or crew operates without incident. When combined with leading indicators such as weather risk, shift loading, or behavioral patterns, MTBI becomes a powerful predictive tool. It empowers decision-makers to prioritize interventions, allocate training, or adjust workflows before problems recur. In this way, MTBI evolves from a descriptive metric into a strategic planning instrument.
For example, a project superintendent may notice that the MTBI for one crew has dropped from 25 days to 9 days over a two-week period. Even if no major incident has occurred, this signals a pattern of minor issues—safety near missis, procedural deviations, or informal workarounds—that require attention. By addressing the underlying stressors or retraining team members, the superintendent can correct course before a recordable event or shutdown occurs.
Similarly, a project manager reviewing work package-level MTBI data might identify that quality incidents have started to cluster around a particular subcontractor. Instead of waiting for failed inspections to mount, the PM could increase QA walkdowns, verify calibration of tools, or realign responsibilities across foremen or reach out to the subcontractor to mitigate increased risks. MTBI transforms hindsight into foresight.
Predictive Modeling with MTBI
By modeling MTBI across domains, projects gain foresight into deteriorating trends before they manifest in incident reports. Forecasting MTBI is performed using a combination of structured and unstructured signals, including:
- Operational and environmental stressors such as weather, shift patterns, workload intensity, and crew composition
- Organizational behavior cues, including procedural stagnation or skill gaps that often go undetected by conventional audits
- Historical MTBI trends across similar crews, scopes, or subcontractors
- Narrative pattern recognition in job logs and field communications, which provide early indicators of emerging risk
MTBI becomes the dependent variable in predictive models that learn from JobSight360’s structured and unstructured data streams.
Use Cases in Practice
1. Crew Risk Forecasting
- Identify crews whose forecasted MTBI is declining due to excessive overtime, tool issues, or past safety indicators.
- Monitor the impact of crew configuration, onboarding, or supervision gaps that may affect incident frequency.
2. Environmental Risk Zones
- Model the effect of slope, soil type, or precipitation on MTBI for environmental incidents like runoff or erosion.
- Highlight areas with elevated risk based on historical spill, dust, or violation rates tied to location-specific data.
3. Quality Reliability by Work Package
- Track MTBI for quality-related failures by subcontractor or scope, surfacing problematic teams or materials.
- Analyze task-level trends where poor workmanship or procedural shortcuts have historically resulted in rework.
4. Field Control Dashboards
- Supervisors view real-time MTBI by work area to spot hotspots and intervene proactively.
- Integrate MTBI alerts into daily briefings, enabling rapid coordination between safety, quality, and operations.
Strategic Implications
MTBI reframes field reliability as a cross-domain problem—not just mechanical, but human and procedural. This unlocks several advantages:
- Unified reporting across EHSQ (Environment, Health, Safety, and Quality)
- Benchmarking: compare crews, companies, and scopes with a consistent reliability metric
- Proactive mitigation: use leading indicators to forecast and avoid incidents
- AI-readiness: provides a clean, quantitative target variable for machine learning models
MTBI can become a cornerstone metric for predictive risk management in construction.
Conclusion
The construction sector stands to gain significantly by treating safety, environmental, and quality failures as predictable—and preventable—events. MTBI offers a clear, unified, and forward-looking metric for managing these risks with the same rigor applied to equipment reliability.
MTBI provides a single lens through which project managers, safety officers, quality teams, and environmental leads can align their efforts. By surfacing temporal reliability trends and embedding them in daily workflows, MTBI transforms raw incident data into a proactive management tool.
JobSight360 aims to make MTBI a standard feature of every field-facing dashboard, predictive risk model, and performance review. This metric is not just a number—it is a reflection of operational resilience, culture, and execution quality. By adopting MTBI, construction companies can move beyond compliance toward true operational reliability, where fewer surprises and greater accountability drive safer, profitable, and more consistent project delivery.
To learn more or pilot MTBI tracking in your project portfolio, contact the JobSight360 team.