Used intelligently, information can become the key to unlocking enhanced monitoring, targeted training, and overall better decision-making with regard to creating a culture focused on safety.
Currently, trucking companies can use data for more than improving efficiency. They can use it to avoid accidents. And to also avoid headaches that come with them – like healthcare costs, lawsuits and higher insurance premiums.
Data Sources for Safety Analysis
In today’s digitally connected world, many sources of data can provide trucking companies visibility into safety performance. A regular analysis of these data streams can allow for continuous improvement through identifying problems, predicting risks, and tracking the impact of implemented changes.
Telematics and Vehicle Data
Sensors and internet-connected technologies integrated with trucks provide a wealth of data directly from the vehicles and drivers:
● GPS tracking data reveals speeding, unnecessary acceleration/braking, and whether drivers follow routes and schedules.
● Engine fault codes can indicate maintenance needs before catastrophic failure.
● Fuel efficiency metrics help identify problems like dirty air filters decreasing MPG.
● Driver performance statistics (revving, idling times, hard braking incidents) can facilitate coaching.
● Built-in cameras can be reviewed to determine accident causes.
● ELD mandate and other mobility platform integrations centralize compliance-related data.
● APIs make other transport management software data accessible for decision-making.
This vehicle and driver data offers tremendous visibility into ongoing safety issues and risk factors on a per-vehicle or driver basis.
Over time, data patterns can clearly highlight candidates for preventative maintenance, retraining, or intervention conversations to correct unsafe behaviors before accidents occur.
Driver Records
Driver histories tell a data story as well:
● Motor vehicle records (MVRs) provide license status, past violations, and accident reports from government databases.
● Pre-employment screening reveals background flags like reckless driving charges.
● Internal company records indicate training progress, safety incident details, and performance trends over time for existing drivers.
Comparing individual driver records against fleet-wide benchmarks allows safety managers to pinpoint and address outliers with additional training or progressive disciplinary steps per company policy. And implementing an ongoing MVR monitoring system also provides alerts to violations that occur after hiring so problematic behaviors can be addressed quickly.
Inspection & Compliance Data
Other data streams can also help ensure trucks and drivers meet essential safety and compliance requirements:
● Pre-trip/post-trip inspection checklists completed by drivers
● Repair work orders and maintenance records
● Government inspection reports tracking violations and out-of-service orders
● Drug and alcohol testing results
● Medical exam certificate expiration dates
● Updated carrier safety ratings and crash data from FMCSA
● Changes in regulations, taxes, permits, and licensing around the country
Regular analysis of this compliance data permits proactive scheduling of necessary repairs, preventative maintenance, license renewals, and testing to avoid issues that could impact safety.
For areas where patterns of infractions occur, trend analysis over time can identify parts of company safety programs needing improvement.
Crash Reports
Crash reports provide early notification and documentation about accident occurrences, including:
● Details like date, time, location, parties involved, weather and road conditions, injuries, vehicles, cargo, and damage
● Police reports and insurance claim filings
● Photos taken at the accident scene from multiple angles
Compiling information into a standardized accident report form facilitates organized incident tracking so crash analytics can inform better decision-making.
Areas that can be examined by type of crash include frequency, severity, preventability factors, costs, locations, drivers, vehicle types, behaviors like speeding/distraction, and more. This crash data analysis over the long term can help identify risk patterns to address through safety policy or program changes.
Operational Data
Other business operations data can also offer supplementary context for safety evaluation, including:
● Operational nature like long haul vs short haul or type of freight hauled
● Size metrics such as power units, drivers, trailers, and annual miles
● Safety staff-to-driver ratio
● Fleet composition vehicle makes, models, ages
● Account base and financials
● Safety-related expenses and investments
Benchmarking safety metrics against operational context allows for appropriate scoping of target settings and progress measurement. Companies can then identify peer groups with similar characteristics to compare safety records as a frame of reference for internal performance. These operational statistics also help demonstrate that “reasonable care” is taken to govern authorities.
Best Practices for Leveraging Data
With so many data sources available, trucking companies must follow fundamental best practices to gain meaningful safety insights, including:
Centralize Data Collection
Establish centralized data intake pipelines from telematics platforms, hardware detectors, driver-reported incidents, inspection systems, and other formats into singular dashboards or data lakes.
Manual uploads from spreadsheets have higher risks of human error. And so prioritizing automation with consistent schemas enables unified data analysis across disparate sources.
Clean & Standardize Data
With data amalgamated from various interfaces, standardizing terms like “accident” vs “crash” or “truck” vs “power unit” or date formats must be aligned to avoid misleading metrics or double counting events.
Protect Sensitive Information
Certain data like drug tests, injuries, and disabilities require strict access controls to safeguard personal privacy. And so encryption, multi-factor user authentication, and security protocols can be used to defend sensitive information against unauthorized use or breaches.
Perform Regular Analysis
Ongoing analysis through daily/weekly/monthly reporting provides dynamic visibility rather than static annual reviews. Measurement of different key performance indicators and drill-downs into details permits continuous learning. Regular assessment allows companies to respond quickly if metrics drift from goals.
Leverage Data Science Techniques
Sophisticated modeling approaches can help in the discovery of data correlations that may be impossible to spot through manual evaluation.
Data mining can uncover hidden trends across massive datasets. And machine learning can be used to come up with predictions and risk probability calculations. These techniques can thus easily spotlight focus areas for safety plans.
Foster Data-Driven Culture
Data insights are only useful if shared, understood, and acted upon across the companies. And so training at all levels will help employees interpret analytics instead of relying on intuition. And executive sponsorship can reinforce evidence-based decisions using metrics. Keeping data transparent and engagement ongoing creates culture shifts.
With sound data practices in place, fleet managers can gain an extensive capacity to observe patterns and respond appropriately as issues arise.
Analytics to Improve Safety Performance
Numerous reporting techniques can allow trucking companies to leverage data analytics towards optimizing driver and fleet safety in the following ways:
Risk Analysis
Statistical modeling helps determine risk factors most highly correlated with crash occurrence. As a result, variables like driver tenure, vehicle type, weather patterns, load contents, routes taken and timing deficits can illuminate focus areas for risk mitigation tactics.
Predictive Modeling
Algorithms can power projections of future outcomes based on historical trends. This method allows for estimating if the current trajectory aligns with safety goals or whether course corrections are necessary. And so models may indicate when certain drivers show high-risk patterns and hence meriting intervention.
Alert Creation
Configured trigger thresholds on key metrics can send email notifications when set boundaries are exceeded. These alerts can inform assigned personnel immediately if dynamic targets (like a 20% weekly increase in speeding occurrences) require intervention vs waiting for retrospective monthly reporting.
Driver Scorecards
Custom scorecards tracking individual metrics like hard braking, acceleration, following distance, seat belt usage and idling rates incentivize and reinforce safe behaviors. Gamification through leaderboards and point systems motivates engagement.
Heat Maps
Plotting incident data by location on digital maps quickly visually conveys high-density zones of crashes by severity, type, or root cause. This geospatial perspective allows operations leaders to pinpoint risks like dangerous intersections, bridges, sharp turns, and target improvements.
Crash Rate Analysis
Viewing crashes normalized by exposure metrics like road segments traveled, miles driven or hours worked facilitates apples-to-apples safety benchmarks across fleet, corporate, and industry levels. Rate-based metrics prevent fleet size disparities from distorting results.
Root Cause Evaluation
Diving into details behind each crash captured through structured categorization uncovers common themes requiring intervention like distracted driving, fatigue, inadequate lighting, slippery roads or medical emergencies. Mitigating policy or training gaps can alleviate these frequent triggers by type and severity of collision.
Reporting Summary Views
Executive-friendly views provide quick overviews of the safety status through monthly/quarterly dashboarding of crash rates, injuries, costs, citations per 100 drivers and preventability rates. Keeping leadership engaged in trends ensures safety prioritization aligns with corporate objectives around risk.
Start Leveraging Data to Drive Safety Today
With fierce competition and thin margins across the trucking sector, transport businesses cannot afford to leave safety to chance. The stakes are too high in terms of insurance, healthcare, repair and legal costs – you can learn more about these costs by consulting with a legal or financial expert.
Data analysis provides assurance programs that operate based on verifiable evidence not just speculative risk assessments. And while it is true that advanced analytics requires some investment in skills, staff and technology, the payoff in enhanced visibility, targeted risk mitigation and continuous improvement is well justified. This is mainly so because data holds the key to dramatically increasing safety through each incremental gain compounding over time.